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Across-trial averaging of event-related EEG responses and beyond
Abstract
Internally and externally triggered sensory, motor and cognitive events elicit a number of transient changes in the ongoing electroencephalogram (EEG): event-related brain potentials (ERPs), event-related synchronization and desynchronization (ERS/ERD), and event-related phase resetting (ERPR). To increase the signal-to-noise ratio of event-related brain responses, most studies rely on across-trial averaging in the time domain, a procedure that is, however, blind to a significant fraction of the elicited cortical activity. Here, we outline the key concepts underlying the limitations of time-domain averaging and consider three alternative methodological approaches that have received increasing interest: time-frequency decomposition of the EEG (using the continuous wavelet transform), blind source separation of the EEG (using Independent Component Analysis) and the analysis of event-related brain responses at the level of single trials. In addition, we provide practical guidelines on the implementation of these methods and on the interpretation of the results they produce.
... This kind of approaches, and considerations highlights the fact that the assumption of stationarity of ERPs is at the core of the averaging procedure [14]. But latency jitter [15], or delay jitter [16] and nonlinear timing distortions of signals (jitter) [17] affect the averaging https://doi. ...
... In [14], the authors explicitly state that ERPs can suffer significant across-trial variability in terms of latency and latency jitter that may lead to important distortion of the averaged ERP and, thereby, to the misestimation of their amplitude and latency. They review some methods to deal with these problems, including the usage of Fourier transform, independent component analysis (ICA) or time frequency decomposition based on continuous wavelet transform (CWT), though these are not specifically oriented to deal with jitter and latency jitter issues. ...
... Within this context, the utilization of averages across responses corresponding to the same activities, and state help to reduce the influence of noise with respect to the underlying common signal. However, other issues like delay or jitter still affect the recovery of such signal [14][15][16]. Precisely, our proposal is oriented to fight against noise, delay, and jitter. ...
... These distortions arise because of the time variation in the by-trial generators of these average curves, making inferences about the timing of the generators complex (Luck, 2005). These distortions are exacerbated when comparing condition-averaged signals: if the process or component time jitter distribution differs between conditions -as could be expected from behavioral (Noorani & Carpenter, 2016;Ratcliff, 1978;Weindel et al., 2021) and electrophysiological studies (O'connell et al., 2012;Smulders et al., 1994) -then both the peak amplitude and latency of the averaged signal will be different across conditions (Mouraux & Iannetti, 2008). ...
... In this paper, we introduced HMP, a method intended to detect sequential events in neural time-series.We believe that this approach is a significant step forward to resolving distortions related to averaging in neural time-series (see Luck, 2005;Mouraux & Iannetti, 2008, and Section 2.5). In simulation, we showed the method's ability to recover cognitive processing events under various levels of signal strength and different parameter settings of the optimization method we used. ...
Measuring the time-course of neural events that make up cognitive processing is crucial to understand the relation between brain and behavior. To this aim, we formulated a method to discover a trial-wise sequence of events in multivariate neural signals such as electro- or magneto-encephalograpic (E/MEG) recordings. This sequence of events is assumed to be represented by multivariate patterns in neural time-series, with inter-event durations following probability distributions. By estimating event-specific multivariate patterns, and between-event duration distributions, the method allows to recover the by-trial onsets of brain responses. We demonstrate the properties and robustness of this hidden multivariate pattern (HMP) method through simulations, including robustness to low signal-to-noise ratio, as typically observed in EEG recordings. The applicability of HMP is illustrated using previously published data from a speed-accuracy trade-off task. We show how HMP provides, for any experiment or condition, an estimate of the number of events, the sensors contributing to each event (e.g. EEG scalp topography), and the durations between each event. Traditional exploration of tasks' cognitive architectures can thus be enhanced by HMP estimates.
... All EEG data were preprocessed and analyzed with MATLAB (version R2016a, The MathWorks, Inc.) toolboxes: EEGLAB (Delorme and Makeig, 2004) and Letswave7 (Mouraux and Iannetti, 2008). The EEG signals were digitally filtered (1-40 Hz bandpass), and the sampling rate was reduced to 250 Hz. ...
... The parameters of central frequency (ω) and restriction (σ) in the CWT were 1 and 1.5, respectively, and TFRs were explored between 1 Hz and 30 Hz in steps of 0.29 Hz. The average power values (i.e., squared amplitude) thus obtained were expressed as an increase or decrease in oscillation to the prestimulus interval according to equation (1), where A t,f was the signal power at a given time (t) and frequency (f), and R f was the signal power averaged within the prestimulus interval (Mouraux and Iannetti, 2008). To avoid edge effects when performing CWT, the prestimulus time interval (− 200 ms to − 50 ms) was used as a baseline interval. ...
The neural mechanism underlying the acquisition of scripts of a second language (L2) is an open issue. The aim of the present study is to investigate the neural specialization for L2 scripts by focusing on the influence of overall visual similarity between first language (L1) and L2. EEG signals were recorded in native Chinese Han readers at the first and ninth months of learning Korean as L2 when they passively viewed Chinese characters (CC), high Chinese-like Korean characters (HKC) and low Chinese-like Korean characters (LKC). Time-frequency analysis revealed that event-related synchronization in the theta band (θ-ERS) is sensitive to CC and Korean character (KC), with a stronger and more left-lateralized θ-ERS for CC and a clear initial response trend of left-/right-lateralization for HKC/LKC. After nine months of learning, increased θ-ERS was shown for both HKC and LKC, whereas robust left lateralization was observed only for HKC. These results suggest that high visual similarity to native language scripts may facilitate the progress of neural specialization for L2. These findings were discussed in light of the “neural recycling” theory.
... The EEG and EOG signals were amplified using a 0.01-100 Hz band pass filter and continuously sampled at 500 Hz/channel for offline analysis. After data acquisition, EEG data were transferred into the EEGLAB and Letswave toolboxes, which are open-source Matlab toolboxes for neurophysiologic data analysis (54,55). EEG were re-referenced to the average of the two mastoids and filtered with a band pass of 0.1-30 Hz. ...
... An estimate of the oscillatory power as a function of time and frequency (time-frequency representation) was obtained from single-trial EEG epochs using the continuous wavelet transform (CWT) (55). The time-frequency representations were explored between 1 Hz and 30 Hz in steps of 0.29 Hz. ...
Background
Impairment of interference control ability may reflect a more general deficit in executive functioning, and lead to an increase in internal-externalized problems such as impulsivity, which has been reported in deaf children. However, few researches have examined the neural mechanism of this impairment.
Methods
This study applied the electroencephalogram (EEG) technique to investigate the interference control ability in 31 deaf children and 28 hearing controls with emotional face-word stroop task.
Results
Results from behavioral task showed that deaf children exhibited lower accuracy compared to hearing controls. As for EEG analysis, reduced activation of ERP components in N1 and enhanced activation of ERP components in N450 have been found in deaf children. Besides, incongruent condition elicited larger N450 than congruent condition. Furthermore, for brain oscillation, alpha band (600–800 ms) revealed a reduced desynchronization in deaf children, while theta band (200–400 ms) revealed an enhanced synchronization in deaf children and incongruent condition, which were in line with ERP components.
Conclusion
The present findings seem to indicate that the deficit during emotional interference control ability among deaf children might be due to the impaired attention allocation ability and emotional cognitive monitoring function during emotional conflict detection process. Consequently, reduced N1 and enhanced N450 might be due to early attention impairment causing more effort of deaf children later in emotional cognitive monitoring.
... Hence, the evoked response, y m (t), is cleaned from the non-phase random oscillations, elided by the simple averaging process represented by the last addend of Equation (1). Although the averaging technique is the most widely used and established approach, it has some drawbacks [64][65][66]. In fact, the following aspects are not considered by the averaging technique: ...
... The key issue would be to achieve a proper signal-to-noise ratio with a reduced number of trials. The literature suggests ERP analysis techniques based on single epoch analysis [63,65,99], improving the grand average with a priori knowledge [62,66], time-frequency analysis techniques (i.e., wavelet) [100,101], and Shannon entropy [102]. ...
The human sense of smell is important for many vital functions, but with the current state of the art, there is a lack of objective and non-invasive methods for smell disorder diagnostics. In recent years, increasing attention is being paid to olfactory event-related potentials (OERPs) of the brain, as a viable tool for the objective assessment of olfactory dysfunctions. The aim of this review is to describe the main features of OERPs signals, the most widely used recording and processing techniques, and the scientific progress and relevance in the use of OERPs in many important application fields. In particular, the innovative role of OERPs is exploited in olfactory disorders that can influence emotions and personality or can be potential indicators of the onset or progression of neurological disorders. For all these reasons, this review presents and analyzes the latest scientific results and future challenges in the use of OERPs signals as an attractive solution for the objective monitoring technique of olfactory disorders.
... The core challenge of this dataset is how to effectively collect the driver's reaction decisions and behaviour during driving. Therefore, the core work is to obtain multimodal driving behaviour datasets and analyse different human driving behaviours by designing experiments based on Event-Related Desynchronization/Synchronization (ERD/ ERS) paradigm [62][63][64] and combining data from physiological signals. We did not utilise Event-Related Potentials (ERPs) as an EEG experimental paradigm in our experiments. ...
Physiological signal monitoring and driver behavior analysis have gained increasing attention in both fundamental research and applied research. This study involved the analysis of driving behavior using multimodal physiological data collected from 35 participants. The data included 59-channel EEG, single-channel ECG, 4-channel EMG, single-channel GSR, and eye movement data obtained via a six-degree-of-freedom driving simulator. We categorized driving behavior into five groups: smooth driving, acceleration, deceleration, lane changing, and turning. Through extensive experiments, we confirmed that both physiological and vehicle data met the requirements. Subsequently, we developed classification models, including linear discriminant analysis (LDA), MMPNet, and EEGNet, to demonstrate the correlation between physiological data and driving behaviors. Notably, we propose a multimodal physiological dataset for analyzing driving behavior(MPDB). The MPDB dataset’s scale, accuracy, and multimodality provide unprecedented opportunities for researchers in the autonomous driving field and beyond. With this dataset, we will contribute to the field of traffic psychology and behavior.
... Combining across-trial averaging in the time domain with the chosen oddball paradigm reduced changes in the EEG signal that were not time-locked to the stimulus onset. Consequently, the signal noise enhanced by averaging signals decreased the differences between groups 35,36 . www.nature.com/scientificreports/ ...
In taste disorders, the key to a correct diagnosis and an adequate treatment is an objective assessment. Compared to psychophysical tests, EEG-derived gustatory event-related potentials (gERP) could be used as a less biased measure. However, the responses identified using conventional time-domain averaging show a low signal-to-noise ratio. This study included 44 patients with dysgeusia and 59 healthy participants, who underwent a comprehensive clinical examination of gustatory function. gERPs were recorded in response to stimulation with two concentrations of salty solutions, which were applied with a high precision gustometer. Group differences were examined using gERP analyzed in the canonical time domain and with Time–Frequency Analyses (TFA). Dysgeusic patients showed significantly lower scores for gustatory chemical and electrical stimuli. gERPs failed to show significant differences in amplitudes or latencies between groups. However, TFA showed that gustatory activations were characterized by a stronger power in controls than in patients in the low frequencies (0.1–4 Hz), and a higher desynchronization in the alpha-band (8–12 Hz). Hence, gERPs reflect the altered taste sensation in patients with dysgeusia. TFA appears to enhance the signal-to-noise ratio commonly present when using conventional time-domain averaging, and might be of assistance for the diagnosis of dysgeusia.
... Regarding time-frequency-domain indicators, the total EEG power was analyzed by following these steps, and the specific electrodes were determined. First, single-trial data were used to estimate the oscillatory power via the Morlet continuous wavelet transform (MCWT, [39]. The parameters of central frequency (ω) and restriction(σ) in MCWT were 5 and 0.15, respectively [40]. ...
Background: This study aims to investigate the behavioral and neurophysiological changes accompanying the empathy for pain among individuals with insomnia in nonclinical samples, which has been scarcely explored in the existing literature despite the deleterious effects of sleep disturbance on social behavior, and interactions had been well-documented.
Methods: Twenty-one individuals with insomnia in nonclinical samples and 20 healthy individuals as normal controls participated in the study. Electroencephalograph (EEG) was continuously recorded, while the participants underwent an empathy for pain task.
Results: Subjective ratings of pain for painful and non-painful images revealed no statistically significant differences between the insomnia and control groups. The painful images induced a smaller P2 compared to non-painful images in the insomnia group, whereas no such difference was revealed for the controls. Moreover, a higher power density of the alpha and theta2 bands in the posterior brain regions was found in the insomnia group compared to the control group.
Conclusion: These findings suggest that individuals with insomnia exhibit altered neurophysiological responses to pain stimuli and a lower capacity to share empathy for pain. These alterations may be associated with changes in attentional mechanisms.
... The offline EEG data were preprocessed and analyzed by Letswave 7 (Mouraux and Iannetti, 2008), an open-source toolbox running on MATLAB (version R2021a). The EEG data were filtered using Butterworth filters between 0.05 and 35 Hz. ...
This paper investigates the neural mechanism that underlies the effect of group identity on hold-up problems. The behavioral results indicated that the investment rate among members of the ingroup was significantly higher than that of the outgroup. In comparison to the NoChat treatment, the Chat treatment resulted in significantly lower offers for both ingroup and outgroup members. The event-related potentials (ERP) results demonstrated the presence of a distinct N2 component in the frontal midline of the brain when investment decisions were made for both ingroup and outgroup members. During the offer decision-making stage, the P3 peak amplitude was significantly larger when interacting with ingroup members compared to the outgroup members. The event-related potentials oscillations (ERO) results indicated that when investment decisions were made for ingroup members in the NoChat treatment, the beta band (18-28 Hz, 250-350 ms) power was more pronounced than when decisions were made for outgroup members. In the NoChat treatment, offer decisions for ingroup members yielded a more pronounced difference in beta band (15-20 Hz, 200-300 ms) power when compared to outgroup members. Evidence from this study suggests that group identity can reduce the hold-up problem and corroborates the neural basis of group identity.
... The P2 component evoked by noxious stimuli reflecting neural activation in bilateral operculo-insular cortex and anterior cingulate cortex is believed to be related to processing of the cognitive and affective dimensions of pain. 32 Therefore, the smaller P2 amplitude in the VR condition compared with the other two conditions could reflect the top-down modulation of pain perception via distraction and emotion modulation at the electrophysiological level. ...
... We suggest that the "total oscillatory responses" procedure, which captures both the non-stationary and event-locked reactivity, has a higher tolerance and can thus describe the possible transient modulations of oscillatory activity caused by TMS. 36 The power of theta reactivity was found to be higher in MCS patients when compared to healthy controls, with a distinctly lower peak in the former. However, VS/UWS patients showed no such peak, suggesting a correlation between consciousness levels and theta reactivity. ...
Introduction
Combining transcranial magnetic stimulation with electroencephalography (TMS‐EEG), oscillatory reactivity can be measured, allowing us to investigate the interaction between local and distant cortical oscillations. However, the extent to which human consciousness is related to these oscillatory effective networks has yet to be explored.
Aims
We tend to investigate the link between oscillatory effective networks and brain consciousness, by monitoring the global transmission of TMS‐induced oscillations in disorders of consciousness (DOC).
Results
A cohort of DOC patients was included in this study, which included 28 patients with a minimally conscious state (MCS) and 20 patients with vegetative state/unresponsive wakefulness syndrome (VS/UWS). Additionally, 25 healthy controls were enrolled. The oscillatory reactivity to single‐pulse TMS of the frontal, sensorimotor and parietal cortex was measured using event‐related spectral perturbation of TMS‐EEG. The temporal–spatial properties of the oscillatory reactivity were illustrated through life time, decay gradients and accumulative power. In DOC patients, an oscillatory reactivity was observed to be temporally and spatially suppressed. TMS‐EEG of DOC patients showed that the oscillations did not travel as far in healthy controls, in terms of both temporal and spatial dimensions. Moreover, cortical theta reactivity was found to be a reliable indicator in distinguishing DOC versus healthy controls when TMS of the parietal region and in distinguishing MCS versus VS/UWS when TMS of the frontal region. Additionally, a positive correlation was observed between the Coma Recovery Scale‐Revised scores of the DOC patients and the cortical theta reactivity.
Conclusions
The findings revealed a breakdown of oscillatory effective networks in DOC patients, which has implications for the use of TMS‐EEG in DOC evaluation and offers a neural oscillation viewpoint on the neurological basis of human consciousness.
... The aforementioned algorithms do not reuse the EEG information that has undergone feature recognition and therefore, the classifier parameters are unchanged. Since EEG signals are non-stationary and time-varying (Mouraux and Iannetti, 2008), adaptive classification technology was developed to track the possible changes in EEG feature distribution and obtain improved classification results. There are three types of adaptive classification method, which are supervised (Shenoy et al., 2006;Schlögl et al., 2010), semi-supervised , and unsupervised (Blumberg et al., 2007;Vidaurre et al., 2011;Kindermans et al., 2014;Zanini et al., 2017). ...
Introduction
As an important human-computer interaction technology, steady-state visual evoked potential (SSVEP) plays a key role in the application of brain computer interface (BCI) systems by accurately decoding SSVEP signals. Currently, the majority SSVEP feature recognition methods use a static classifier. However, electroencephalogram (EEG) signals are non-stationary and time-varying. Hence, an adaptive classification method would be an alternative option to a static classifier for tracking the changes in EEG feature distribution, as its parameters can be re-estimated and updated with the input of new EEG data.
Methods
In this study, an unsupervised adaptive classification algorithm is designed based on the self-similarity of same-frequency signals. The proposed classification algorithm saves the EEG data that has undergone feature recognition as a template signal in accordance with its estimated label, and the new testing signal is superimposed with the template signals at each stimulus frequency as the new test signals to be analyzed. With the continuous input of EEG data, the template signals are continuously updated.
Results
By comparing the classification accuracy of the original testing signal and the testing signal superimposed with the template signals, this study demonstrates the effectiveness of using the self-similarity of same-frequency signals in the adaptive classification algorithm. The experimental results also show that the longer the SSVEP-BCI system is used, the better the responses of users on SSVEP are, and the more significantly the adaptive classification algorithm performs in terms of feature recognition. The testing results of two public datasets show that the adaptive classification algorithm outperforms the static classification method in terms of feature recognition.
Discussion
The proposed adaptive classification algorithm can update the parameters with the input of new EEG data, which is of favorable impact for the accurate analysis of EEG data with time-varying characteristics.
... After EEG data acquisition, pre-processing was conducted using Letswave7 (Mouraux and Iannetti, 2008) 1 and MATLAB (MathWorks, Natick, MA, USA) scripts. EEG data went through the following steps of pre-processing. ...
A contest usually involves expenditures, termed “overbidding,” exceeding the theoretical Nash equilibrium. A considerable number of studies have shown that group identity can affect decision-making and competitive behavior, thus providing a new perspective on alleviating the overbidding problem. How group identity influences brain activity when competitors bid in different groups is not yet clear, however. In this study, we implemented group identity manipulation into the lottery contest game and we recorded behavioral and electroencephalography (EEG) data at the same time. Two experimental treatments were conducted to study the effect of group identity on bidding behavior. The event-related potentials (ERP) and event-related oscillations (ERO) techniques were utilized to explore brain activity differences caused by participants’ different bidding behaviors under in-group and out-group conditions. Behavioral results showed that individual expenditure was significantly lower when bidding with in-group opponents than with out-group opponents. Analyses of EEG results revealed that compared to in-group conditions, greater N2 amplitudes and theta power were found under out-group conditions. To extend previous studies, we performed supplementary analysis to explore whether enhancement of group identity had effects on conflict alleviation. Behavioral results indicated that individual expenditure was significantly lower after enhancing group identity when bidding with in-group, and EEG results showed more negative N2 amplitudes, smaller P3 amplitudes and larger theta power after enhancing group identity. Collectively, these findings indicate that group identity modulated bidding behavior, and they provide insight into a mechanism to de-escalate group conflict by enhancing group identity.
... The STFT provided a complex TF estimate F (t, f), ranging from À1,000 to 2,000 ms (in 2-ms steps) in the time domain and from 2 to 20 Hz (in 1-Hz steps) in the frequency domain, for each trial at each TF point (t, f). The resulting spectrogram, P (t, f) ¼ j F (t, f) j 2 , which represents the signal power as a joint function of time and frequency at each TF point, included both phase-locked and nonphaselocked neural responses (Mouraux & Iannetti, 2008). The obtained power values were baseline-corrected in each frequency band by subtracting the averaged power values during the baseline À700 to À300 ms before target onset from each data point. ...
The present study adopted a primed target grasping-categorization task and selected pictures of animals as target stimuli to investigate whether motor inhibition influences the motor interference effect of dangerous animals. The results identified more positive P2 and P3 amplitudes accompanied by larger delta event-related synchronization in the dangerous condition than in the neutral condition, suggesting that compared to neutral animal targets, dangerous animal targets attracted increased attentional resources in early processing and that subjects recruited more cognitive resources to process dangerous animal targets than neutral animal targets. Moreover, the results identified larger theta event-related synchronization (reflecting motor inhibition) in the dangerous condition than in the neutral condition. Thus, the results suggested that prepared motor responses were inhibited to avoid touching dangerous animal targets in the current task, supporting that motor inhibition influences the motor interference effect of dangerous animals based on a primed target grasping-categorization task.
... However, classical time-locked ERP/ERF analysis is blind to information not phase-locked to the stimuli, resulting in less sensitivity when tapping into ongoing neurocognitive dynamics associated with bilingual language processing 27,28 . Relative to ERP/ERF, time-frequency analysis can better characterize the temporal dynamics of oscillations contained in the brain signal. ...
Bilinguals with a high proficiency in their first (L1) and second language (L2) often show comparable reaction times when switching from their L1 to L2 and vice-versa (“symmetrical switch costs”). However, the neurophysiological signatures supporting this effect are not well understood. Here, we ran two separate experiments and assessed behavioral and MEG responses in highly proficient Spanish-Basque bilinguals while they overtly name pictures in a mixed-language context. In the behavioral experiment, bilinguals were slower when naming items in switch relative to non-switch trials, and this switch cost was comparable for both languages (symmetrical). The MEG experiment mimicked the behavioral one, with switch trials showing more desynchronization than non-switch trials across languages (symmetric neural cost) in the alpha band (8–13 Hz). Source-localization revealed the engagement of right parietal and premotor areas, which have been linked to language selection and inhibitory control; and of the left anterior temporal lobe (ATL), a cross-linguistic region housing conceptual knowledge that generalizes across languages. Our results suggest that highly proficient bilinguals implement a language-independent mechanism, supported by alpha oscillations, which is involved in cue-based language selection and facilitates conceptually-driven lexical access in the ATL, possibly by inhibiting non-target lexical items or disinhibiting target ones.
... The period from −300 ms to 0 ms served as the baseline for signal correction. To improve the signal-to-noise ratio of each participant's data, cross-trial averaging was used, with five trials per participant being averaged into a single epoch [44]. After preprocessing, 315 P stimulus trials were available for analysis in both the innocent and lying groups. ...
In this study, partial mutual information at the source level was used to construct brain functional networks in order to examine differences in brain functions between lying and honest responses. The study used independent component analysis and clustering methods to computationally generate source signals from EEG signals recorded from subjects who were lying and those who were being honest. Partial mutual information was calculated between regions of interest (ROIs), and used to construct a functional brain network with ROIs as nodes and partial mutual information values as connections between them. The partial mutual information connections that showed significant differences between the two groups of people were selected as the feature set and classified using a functional connectivity network (FCN) classifier, resulting in an accuracy of 88.5%. Analysis of the brain networks of the lying and honest groups showed that, in the lying state, there was increased informational exchange between the frontal lobe and temporal lobe, and the language motor center of the frontal lobe exchanged more information with other brain regions, suggesting increased working and episodic memory load and the mobilization of more cognitive resources.
... Task-related EEG data were converted into time-frequency domain data using continuous wavelet transform (CWT) in Letswave software (https://www.letswave.org/) (Mouraux & Iannetti, 2008). The values of central frequency (ω) and limit (σ) were set to 5 and 0.15, respectively, in the continuous wavelet transform. ...
Analogical reasoning is important for human. We have found that a short executive attention intervention improved analogical reasoning performance in healthy young adults. Nevertheless, previous electrophysiological evidence was limited for comprehensively characterizing the neural mechanisms underlying the improvement. And although we hypothesized that the intervention improved active inhibitory control and attention shift first and then relation integration, it is still unclear whether there are two sequential cognitive neural activities were indeed changed during analogical reasoning. In the present study, we combined hypothesis with multivariate pattern analysis (MVPA) to explore the effects of the intervention on electrophysiology. Results showed that in the resting state after the intervention, alpha and high gamma power and the functional connectivity between the anterior and middle in the alpha band could discriminate the experimental group from the active control group, respectively. These indicated that the intervention influenced the activity of multiple bands and the interaction of frontal and parietal regions. In the analogical reasoning, alpha, theta, and gamma activities could also fulfill such discrimination, and furthermore, they were sequential (alpha first, theta, and gamma later). These results directly supported our previous hypothesis. The present study deepens our understanding about how executive attention contributes to higher-order cognition.
... webno de. com) software 59 . EMG signals from each participant were high-pass filtered (55 Hz) and full-wave rectified. ...
Selecting appropriate defensive behaviours for threats approaching the space surrounding the body (peripersonal space, PPS) is crucial for survival. The extent of defensive PPS is measured by recording the hand-blink reflex (HBR), a subcortical defensive response. Higher-order cortical areas involved in PPS representation exert top-down modulation on brainstem circuits subserving HBR. However, it is not yet known whether pre-existing models of social relationships (internal working models, IWM) originating from early attachment experiences influence defensive responses. We hypothesized that organized IWM ensure adequate top-down regulation of brainstem activity mediating HBR, whereas disorganized IWM are associated with altered response patterns. To investigate attachment-dependent modulation on defensive responses, we used the Adult Attachment Interview to determine IWM and recorded HBR in two sessions (with or without the neurobehavioral attachment system activated). As expected, the HBR magnitude in individuals with organized IWM was modulated by the threat proximity to the face, regardless of the session. In contrast, for individuals with disorganized IWM, attachment system activation enhances HBR regardless of the threat position, suggesting that triggering emotional attachment experiences magnifies the threatening valence of external stimuli. Our results indicate that the attachment system exerts a strong modulation on defensive responses and the magnitude of PPS.
... To this end, power was calculated for each participant and trial in the alpha and beta frequency bands using a time-frequency wavelet analysis (Tallon-Baudry et al., 1997). Complex Morlet wavelets (CMW) were chosen as the wavelet method due to its reliability for spectral estimations and common use in the analysis of EEG data (Mouraux and Iannetti, 2008;Pavlov et al., 2012;Ullah and Halim, 2021). Here, the EEG signal was convoluted with CMW w t, f 0 with a Gaussian shape in time (σ t ) and frequency domains (σ f = 1/2πσ t ), as described in Eq. 1 (Tallon-Baudry et al., 1997): ...
Introduction
Brain-Computer Interfaces (BCI) can allow control of external devices using motor imagery (MI) decoded from electroencephalography (EEG). Although BCI have a wide range of applications including neurorehabilitation, the low spatial resolution of EEG, coupled to the variability of cortical activations during MI, make control of BCI based on EEG a challenging task.
Methods
An assessment of BCI control with different feedback timing strategies was performed. Two different feedback timing strategies were compared, comprised by passive hand movement provided by a robotic hand orthosis. One of the timing strategies, the continuous, involved the partial movement of the robot immediately after the recognition of each time segment in which hand MI was performed. The other feedback, the discrete, was comprised by the entire movement of the robot after the processing of the complete MI period. Eighteen healthy participants performed two sessions of BCI training and testing, one with each feedback.
Results
Significantly higher BCI performance (65.4 ± 17.9% with the continuous and 62.1 ± 18.6% with the discrete feedback) and pronounced bilateral alpha and ipsilateral beta cortical activations were observed with the continuous feedback.
Discussion
It was hypothesized that these effects, although heterogenous across participants, were caused by the enhancement of attentional and closed-loop somatosensory processes. This is important, since a continuous feedback timing could increase the number of BCI users that can control a MI-based system or enhance cortical activations associated with neuroplasticity, important for neurorehabilitation applications.
... EEG analyses were conducted in MATLAB (The Mathworks, Inc.) with the Fieldtrip toolbox (Oostenveld et al., 2011). Topographies were made using Letswave6 (Mouraux & Iannetti, 2008) (www.letswave.org), with the posterior view of the 3D head shape presented in the figures to facilitate visualization of EEG activity over visual regions. ...
Periodicity is a fundamental property of biological systems, including human movement systems. Periodic movements support displacements of the body in the environment as well as interactions and communication between individuals. Here we use electroencephalography (EEG) to investigate the neural tracking of visual periodic motion, and more specifically, the relevance of spatiotemporal information contained at and between their turning points. We compared EEG responses to visual sinusoidal oscillations versus nonlinear Rayleigh oscillations, which are both typical of human movements. These oscillations contain the same spatiotemporal information at their turning points but differ between turning points, with Rayleigh oscillations having an earlier peak velocity, shown to increase an individual’s capacity to produce accurately synchronized movements. EEG analyses highlighted the relevance of spatiotemporal information between the turning points by showing that the brain precisely tracks subtle differences in velocity profiles, as indicated by earlier EEG responses for Rayleigh oscillations. The results suggest that the brain is particularly responsive to velocity peaks in visual periodic motion, supporting their role in conveying behaviorally relevant timing information at a neurophysiological level. The results also suggest key functions of neural oscillations in the Alpha and Beta frequency bands, particularly in the right hemisphere. Together, these findings provide insights into the neural mechanisms underpinning the processing of visual periodic motion and the critical role of velocity peaks in enabling proficient visuomotor synchronization.
... For each participant and condition, EEG epochs were averaged across trials in the time domain. Across-trial averaging in the time domain is expected to cancel out or at least markedly reduce the contribution of EEG signals that are not phase locked to the stimulation train, and therefore increase signal-to-noise ratio (Mouraux and Iannetti 2008). Subsequently, for each participant and condition, the obtained waveforms were then examined in the frequency domain by using a discrete Fourier transform (Frigo and Johnson 1998), using a Hanning window, yielding a power spectrum ranging from 0 to 100 Hz, since the spectrum was trimmed up to 100 Hz, with a frequency resolution of 0.017 Hz (i.e. ...
Human movement synchronisation with moving objects strongly relies on visual input. However, auditory information also plays an important role, since real environments are intrinsically multimodal. We used electroencephalography (EEG) frequency tagging to investigate the selective neural processing and integration of visual and auditory information during motor tracking and tested the effects of spatial and temporal congruency between audiovisual modalities. EEG was recorded while participants tracked with their index finger a red flickering (rate fV = 15 Hz) dot oscillating horizontally on a screen. The simultaneous auditory stimulus was modulated in pitch (rate fA = 32 Hz) and lateralised between left and right audio channels to induce perception of a periodic displacement of the sound source. Audiovisual congruency was manipulated in terms of space in Experiment 1 (no motion, same direction or opposite direction), and timing in Experiment 2 (no delay, medium delay or large delay). For both experiments, significant EEG responses were elicited at fV and fA tagging frequencies. It was also hypothesised that intermodulation products corresponding to the nonlinear integration of visual and auditory stimuli at frequencies fV ± fA would be elicited, due to audiovisual integration, especially in Congruent conditions. However, these components were not observed. Moreover, synchronisation and EEG results were not influenced by congruency manipulations, which invites further exploration of the conditions which may modulate audiovisual processing and the motor tracking of moving objects.
... The Letswave toolbox MATLAB 41 was used for preprocessing during offline analysis. Invalid trials were excluded. ...
Warning sign plays an important role in risk avoidance. Many studies have found that images are better warnings than text, while others have revealed flaws of image-only warning signs. To better understand the factors underlying the effectiveness of different types of warning signs (image only, text only, or image and text), this study adopted event-related potential technology to explore the differences at the neurocognitive level using the oddball paradigm and the Go/No-go paradigm. Together, the behavioral and electroencephalogram results showed that text-only warnings had the lowest effectiveness, but there was little difference between the image-only and image-and-text warnings. The differences in the effects of the three warning signs were mainly in the areas of attention and cognitive control, implying differences in the underlying cognitive processes. Therefore, in the design of warning signs, the effects of different design attributes on cognitive processing should be taken into account based on actual needs in order to improve the effectiveness of the signs.
... Preprocessing was carried out in Letswave 5 (Mouraux & Iannetti, 2008), an open-source toolbox running in MATLAB 2016b. ...
Humans show individual differences in neural facial identity discrimination (FID) responses across viewing positions. Critically, these variations have been shown to be reliable over time and to directly relate to observers' idiosyncratic preferences in facial information sampling. This functional signature in facial identity processing might relate to observer-specific diagnostic information processing. Although these individual differences are a valuable source of information for interpreting data, they can also be difficult to isolate when it is not possible to test many conditions. To address this potential issue, we explored whether reducing stimulus size would help decrease these interindividual variations in neural FID. We manipulated the size of face stimuli (covering 3°, 5°, 6.7°, 8.5°, and 12° of visual angle), as well as the fixation location (left eye, right eye, below the nasion, nose, and mouth) while recording electrophysiological responses. Same identity faces were presented with a base frequency of 6 Hz. Different identity faces were periodically inserted within this sequence to trigger an objective index of neural FID. Our data show robust and consistent individual differences in neural face identity discrimination across viewing positions for all face sizes. Nevertheless, FID was optimal for a larger number of observers when faces subtended 6.7° of visual angle and fixation was below the nasion. This condition is the most suited to reduce natural interindividual variations in neural FID patterns, defining an important benchmark to measure neural FID when it is not possible to assess and control for observers' idiosyncrasies.
... The correlation coefficient was highest for the signal processed by the time-frequency denoising method and the reference signal, indicating that the single trial signal after time-frequency denoising was most Introduction Electroencephalogram (EEG) is used to measure the synchronous changes of postsynaptic potential produced by pyramidal neurons with similar orientation in the brain. Almost all sensory, motion, or mental events can cause transient changes in spontaneous EEG activity, for time locked and phase locked event-related potential (ERP) (Mouraux and Iannetti, 2008). Currently, the cross-trial averaging method is the most widely used method to detect event-related brain response. ...
Introduction
Electroencephalogram (EEG) acquisition is easily affected by various noises, including those from electrocardiogram (ECG), electrooculogram (EOG), and electromyogram (EMG). Because noise interference can significantly limit the study and analysis of brain signals, there is a significant need for the development of improved methods to remove this interference for more accurate measurement of EEG signals.
Methods
Based on the non-linear and non-stationary characteristics of brain signals, a strategy was developed to denoise brain signals using a time-frequency denoising algorithm framework of short-time Fourier transform (STFT), bidimensional empirical mode decomposition (BEMD), and non-local means (NLM). Time-frequency analysis can reveal the signal frequency component and its evolution process, allowing the elimination of noise according to the signal and noise distribution. BEMD can be used to decompose the time-frequency signals into sub-time-frequency signals for noise removal at different scales. NLM relies on structural self-similarity to locally smooth an image to remove noise and restore its main geometric structure, making this method appropriate for time-frequency signal denoising.
Results
The experimental results show that the proposed method can effectively suppress the high-frequency components of brain signals, resulting in a smoother brain signal waveform after denoising. The correlation coefficient of the reference signal, a superposition average of multiple trial signals, and the original single trial signal was determined, and then correlation coefficients were calculated between the reference signal and single trial signals processed by time-frequency denoising, ensemble empirical mode decomposition (EEMD)-independent component analysis (ICA), EEMD-canonical correlation analysis (CCA), and wavelet threshold denoising methods. The correlation coefficient was highest for the signal processed by the time-frequency denoising method and the reference signal, indicating that the single trial signal after time-frequency denoising was most similar to the waveform of the reference signal and suggesting this is a feasible strategy to effectively reduce noise and more accurately determine signals.
Discussion
The proposed time-frequency denoising method exhibits excellent performance with promising potential for practical application.
... Finally, data were re-referenced to the average of all the electrodes. Participants needed to have at least seven artifact-free epochs per stimulus to be included for analysis ( Artifact-free trials were averaged and FFT was applied using Letswave6 (Mouraux and Iannetti, 2008). In order to remove the unrelated residual background noise, the magnitude of SS-EPs was calculated in relation to the amplitude of the frequency spectrum at surrounding bins. ...
Neural entrainment is defined as the process whereby brain activity, and more specifically neuronal oscillations measured by EEG, synchronize with exogenous stimulus rhythms. Despite the importance that neural oscillations have assumed in recent years in the field of auditory neuroscience and speech perception, in human infants the oscillatory brain rhythms and their synchronization with complex auditory exogenous rhythms are still relatively unexplored. In the present study, we investigate infant neural entrainment to complex non-speech (musical) and speech rhythmic stimuli; we provide a developmental analysis to explore potential similarities and differences between infants’ and adults’ ability to entrain to the stimuli; and we analyze the associations between infants’ neural entrainment measures and the concurrent level of development. 25 8-month-old infants were included in the study. Their EEG signals were recorded while they passively listened to non-speech and speech rhythmic stimuli modulated at different rates. In addition, Bayley Scales were administered to all infants to assess their cognitive, language, and social-emotional development. Neural entrainment to the incoming rhythms was measured in the form of peaks emerging from the EEG spectrum at frequencies corresponding to the rhythm envelope. Analyses of the EEG spectrum revealed clear responses above the noise floor at frequencies corresponding to the rhythm envelope, suggesting that – similarly to adults – infants at 8 months of age were capable of entraining to the incoming complex auditory rhythms. Infants’ measures of neural entrainment were associated with concurrent measures of cognitive and social-emotional development.
... Fourth, by analyzing the post-stimulus ITPC of theta and alpha bands, we found that the post-stimulus alpha-ITPC was significantly larger in the congruent condition than in the incongruent condition, especially over the left parietal scalp region. Distinct from event-related desynchronization/ synchronization (ERD/ERS), which measures post-stimulus non-phase-locked (i.e., induced) spectral power changes, the ITPC (also named inter-trial coherence [ITC]) can be used to detect time-locked and phase-locked (i.e., evoked) phase-resetting across EEG trials at each time-frequency point (Mouraux and Iannetti 2008). Commonly, ITPC activities were largest over the lower EEG band (e.g., delta/theta/ alpha band) in many previous studies (Luck and Kappenman 2012;Zhang et al. 2012). ...
Metaphors commonly represent mental representations of abstract concepts. One example is the valence-space metaphor (i.e., positive word-up, negative word-down), which suggests that the vertical position of positive/negative words can modulate the evaluation of word valence. Here, the spatial Stroop task and electroencephalography (EEG) techniques were used to explore the neural mechanism of the valence-space congruency effect in valence-space metaphors. This study showed that the reaction time of the congruent condition (i.e., positive words at the top and negative words at the bottom of the screen) was significantly shorter than that of the incongruent condition (i.e., positive words at the bottom and negative words at the top of the screen), while the accuracy rate of the congruent condition was significantly larger than that of the incongruent condition. The analysis of the amplitudes of event-related potential components revealed that congruency between the vertical position and valence of Chinese words could significantly modulate the amplitude of attention allocation-related P2 component and semantic violations related N400 component. Moreover, statistical tests conducted on the post-stimulus inter-trial phase coherence (ITPC) found that the ITPC value of an alpha band region of interest (8–12 Hz, 100–300 ms post-stimulus) in the time-frequency plane of the congruent condition was significantly larger than that of the incongruent condition. Above all, the current study proved the existence of the space-valence congruency effect in Chinese words and provided some interesting neurophysiological mechanisms regarding the valence-space metaphor.
Past research has demonstrated that it is possible to detect implicit responses to face trustworthiness using fast periodic visual stimulation (FPVS). Because people readily retrieve affective associations with faces, the current study investigated whether learned trustworthiness would yield similar responses to face trustworthiness as measured via FPVS. After learning to associate faces with untrustworthy or trustworthy behaviors, participants completed three separate tasks while electroencephalography (EEG) was recorded. In each of these tasks, participants viewed oddball sequences of faces where a single base face was presented repeatedly at a rate of 6 Hz and oddball faces with different identities were presented every fifth face (6 Hz/5 = 1.2 Hz). Providing evidence of learning, the oddball response at 1.2 Hz and its harmonics was stronger for the learned faces compared to novel faces over bilateral occipitotemporal cortex and beyond. In addition, reproducing previous findings with face trustworthiness, we observed a stronger response at 1.2 Hz and its harmonics for sequences with less trustworthy-looking versus trustworthy-looking oddball faces over bilateral occipitotemporal cortex and other sites. However, contrary to our predictions, we did not observe a significant influence of learned trustworthiness on the oddball response. These data indicate that impressions based on learning are treated differently than impressions based on appearance, and they raise questions about the types of design and stimuli that yield responses that are measurable via FPVS.
Numerous studies have identified traveling waves in the cortex and suggested they play important roles in brain processing. These waves are most often measured using macroscopic methods that are unable to assess the local spiking activity underlying wave dynamics. Here, we investigated the possibility that waves may not be traveling at the single neuron scale. We first show that sequentially activating two discrete brain areas can appear as traveling waves in EEG simulations. We next reproduce these results using an analytical model of two sequentially activated regions. Using this model, we were able to generate wave-like activity with variable directions, velocities, and spatial patterns, and to map the discriminability limits between traveling waves and modular sequential activations. Finally, we investigated the link between field potentials and single neuron excitability using large-scale measurements from turtle cortex ex vivo. We found that while field potentials exhibit wave-like dynamics, the underlying spiking activity was better described by consecutively activated spatially adjacent groups of neurons. Taken together, this study suggests caution when interpreting phase delay measurements as continuously propagating wavefronts in two different spatial scales. A careful distinction between modular and wave excitability profiles across scales will be critical for understanding the nature of cortical computations.
Background: Numerous studies have highlighted the pivotal role of alterations in the monetary reward system in the development and maintenance of substance use disorder (SUD). Although these alterations have been well documented in various forms of SUD, the electrophysiological mechanisms specific to opioid use disorder (OUD) remain underexplored. Understanding these mechanisms is critical for developing targeted interventions and advancing theories of addiction specific to opioid use.Objectives: To explore abnormalities in monetary reward outcome processing in males with OUD. We hypothesized that control individuals would show higher feedback-related negativity (FRN) to losses, unlike those in the OUD group, where FRN to losses and gains would not differ significantly.Methods: Fifty-seven participants (29 male individuals with OUD [heroin] and 28 male controls) were evaluated. A combination of the monetary incentive delay task (MIDT) and event-related potential (ERP) technology was used to investigate electrophysiological differences in monetary reward feedback processing between the OUD and healthy control groups.Results: We observed a significant interaction between group (control vs. OUD) and monetary outcome (loss vs. gain), indicated by p < .05 and η2p = 0.116. Specifically, control participants showed stronger negative FRN to losses than gains (p < .05), unlike the OUD group (p > .05).Conclusion: This study's FRN data indicate that males with OUD show altered processing of monetary rewards, marked by reduced sensitivity to loss. These findings offer electrophysiological insights into why males with OUD may pursue drugs despite potential economic downsides.
Past research showed that emotional contexts can impair recognition memory for the target item. Given that item‐context congruity may enhance recognition memory, the present study aims to examine the effect of the congruent emotional encoding contexts on recognition memory. Participants studied congruent word‐picture pairs (e.g., the word “cow” – a picture describing a cow) and incongruent word‐picture pairs (e.g., the word “cow” – a picture describing a goat) and, subsequently, were asked to report the nature of the picture (emotional or neutral). Behavioral results revealed that emotional contexts impaired source but not item recognition, with congruent word‐context mitigating this impairment and enhancing item recognition. Neural results from ERPs and theta oscillations found the recollection process, as shown by the LPC old/new effect and theta oscillations, for both item and source recognition across emotional contexts, irrespective of congruity. Meanwhile, the familiarity process as indexed by the FN400 old/new effect was found only for item recognition in congruent emotional contexts. These findings suggest that the congruent relationship of item‐context could mitigate the emotion‐induced source memory impairment and enhance item memory, with neural results elucidating the memory processes involved in retrieval of emotional information. Specifically, while emotion‐related information generally elicits the recollection‐based memory process, only congruent emotional information elicits the familiarity‐based process.
Extensive work has investigated the neural processing of single faces, including the role of shape and surface properties. However, much less is known about the neural basis of face ensemble perception (e.g., simultaneously viewing several faces in a crowd). Importantly, the contribution of shape and surface properties have not been elucidated in face ensemble processing. Furthermore, how single central faces are processed within the context of an ensemble remains unclear. Here, we probe the neural dynamics of ensemble representation using pattern analyses as applied to electrophysiology data in healthy adults (seven males, nine females). Our investigation relies on a unique set of stimuli, depicting different facial identities, which vary parametrically and independently along their shape and surface properties. These stimuli were organized into ensemble displays consisting of six surround faces arranged in a circle around one central face. Overall, our results indicate that both shape and surface properties play a significant role in face ensemble encoding, with the latter demonstrating a more pronounced contribution. Importantly, we find that the neural processing of the center face precedes that of the surround faces in an ensemble. Further, the temporal profile of center face decoding is similar to that of single faces, while those of single faces and face ensembles diverge extensively from each other. Thus, our work capitalizes on a new center-surround paradigm to elucidate the neural dynamics of ensemble processing and the information that underpins it. Critically, our results serve to bridge the study of single and ensemble face perception.
Numerous studies have identified travelling waves in the cortex and suggested they play important roles in brain processing. These waves are most often measured using macroscopic methods that are unable to assess the local spiking activity underlying wave dynamics. Here we investigated the possibility that waves may not be travelling at the single neuron scale. We first show that sequentially activating two discrete brain areas can appear as travelling waves in EEG simulations. We next reproduce these results using an analytical model of two sequentially activated regions. Using this model, we were able to generate wave-like activity with variable directions, velocities, and spatial patterns, and to map the discriminability limits between travelling waves and modular sequential activations. Finally, we investigated the link between field potentials and single neuron excitability using large-scale measurements from turtle cortex ex-vivo. We found that while field potentials exhibit wave-like dynamics, the underlying spiking activity was better described by consecutively activated spatially adjacent groups of neurons. Taken together, this study suggests caution when interpreting phase delay measurements as continuously propagating wavefronts in two different spatial scales. A careful distinction between modular and wave excitability profiles across scales will be critical for understanding the nature of cortical computations.
Numerous studies have identified travelling waves in the cortex and suggested they play important roles in brain processing. These waves are most often measured using macroscopic methods that are unable to assess the local spiking activity underlying wave dynamics. Here we investigated the possibility that waves may not be travelling at the single neuron scale. We first show that sequentially activating two discrete brain areas can appear as travelling waves in EEG simulations. We next reproduce these results using an analytical model of two sequentially activated regions. Using this model, we were able to generate wave-like activity with variable directions, velocities, and spatial patterns, and to map the discriminability limits between travelling waves and modular sequential activations. Finally, we investigated the link between field potentials and single neuron excitability using large-scale measurements from turtle cortex ex-vivo. We found that while field potentials exhibit wave-like dynamics, the underlying spiking activity was better described by consecutively activated spatially adjacent groups of neurons. Taken together, this study suggests caution when interpreting phase delay measurements as continuously propagating wavefronts in two different spatial scales. A careful distinction between modular and wave excitability profiles across scales will be critical for understanding the nature of cortical computations.
Numerous studies have identified travelling waves in the cortex and suggested they play important roles in brain processing. These waves are most often measured using macroscopic methods that are unable to assess the local spiking activity underlying wave dynamics. Here we investigated the possibility that waves may not be travelling at the single neuron scale. We first show that sequentially activating two discrete brain areas can appear as travelling waves in EEG simulations. We next reproduce these results using an analytical model of two sequentially activated regions. Using this model, we were able to generate wave-like activity with variable directions, velocities, and spatial patterns, and to map the discriminability limits between travelling waves and modular sequential activations. Finally, we investigated the link between field potentials and single neuron excitability using large-scale measurements from turtle cortex ex-vivo. We found that while field potentials exhibit wave-like dynamics, the underlying spiking activity was better described by consecutively activated spatially adjacent groups of neurons. Taken together, this study suggests caution when interpreting phase delay measurements as continuously propagating wavefronts in two different spatial scales. A careful distinction between modular and wave excitability profiles across scales will be critical for understanding the nature of cortical computations.
Previous studies have shown that people implicitly associate the emotional valence of abstract words with vertical position (i.e., positive words up, negative words down), resulting in the so-called valence-space congruency effect. Research has demonstrated that there is a valence-space congruency effect when it comes to emotional words. It's interesting to see that whether the emotional pictures with different levels of valence are mapped to distinct vertical space positions. Here, the event-related potential (ERP) and time-frequency techniques were employed to investigate the neural basis of the valence-space congruency effect of emotional pictures in a spatial Stroop task. Firstly, this study showed that the reaction time of the congruent condition (i.e., positive pictures in the top and negative pictures in the bottom of the screen) was significantly shorter than that of the incongruent condition (i.e., positive pictures in the bottom and negative pictures in the top of the screen), suggesting that exposure to stimuli with positive or negative valence, regardless of whether these stimuli were comprised of words or pictures, would be enough to invoke the vertical metaphor. Moreover, we found that the congruency between the vertical position and the valence of emotional pictures could significantly modulate the amplitude of the P2 component and the Late Positive Component (LPC) in ERP waveforms, as well as the post-stimulus alpha-ERD in the time-frequency plane. This study has conclusively demonstrated the presence of a space-valence congruency effect in emotional pictures and has elucidated the underlying neurophysiological mechanisms associated with the valence-space metaphor.
Empirical evidence on error processing comes from the comparison between errors and correct responses in general, but essential differences may exist between different error types. Typically, cognitive control tasks elicit errors without conflicts (congruent errors) and with conflicts (incongruent errors), which may employ different monitoring and adjustment mechanisms. However, the neural indicators that distinguish between both error types remain unclear. To solve this issue, behavioral and electrophysiological data were measured while subjects performed the flanker task. Results showed that a significant post-error improvement in accuracy on incongruent errors, but not on congruent errors. Theta and beta power were comparable between both error types. Importantly, the basic error-related alpha suppression (ERAS) effect was observed on both errors, whereas ERAS evoked by incongruent errors was greater than congruent errors, indicating that post-error attentional adjustments are both source-general and source-specific. And the brain activity in alpha band, but not theta or beta band, successfully decoded congruent and incongruent errors. Furthermore, improved post-incongruent error accuracy was predicted by a measure of post-error attentional adjustments, the alpha power. Together, these findings demonstrate that ERAS is a reliable neural indicator for identifying error types, and directly conduces to the improvement of post-error behavior.
Neurons communicate by electrical and chemical signals. Neurophysiological studies measuring and manipulating these signals are therefore of utmost importance to understand neural function. In this chapter, we review an extensive set of tools used in our laboratory to study tactile processing in rats. After a very brief summary of the anatomy and physiology of the sense of touch, instrumentation for generating mechanical stimuli is covered in detail. Next, techniques for studying mechanoreceptive afferents are presented. Remaining sections include electroencephalography (EEG), electrocorticography (EcoG), local field potentials (LFP), and extracellular spike recordings for the brain. Acute and chronic preparations for recording from the somatosensory cortex are reviewed in line with the current state-of-the-art technology for electrodes and equipment. Dedicated sections are devoted to electrical microstimulation of neural tissue and microinjection of drugs, which allow manipulation of the somatosensory system for basic and applied research. The material presented in this chapter is also useful for guiding neural engineering applications such as neuroprostheses and brain-machine interfaces (BMI), at their initial developmental stages.Key wordsRodentTouchGlabrous skinSciatic nerveSomatosensory cortexVibrotactile stimulationExtracellular recordingSpikeLocal field potentialPsychophysicsYes/no detection taskMicrostimulationMicroinjection
Successful encoding, maintenance, and retrieval of information stored in working memory requires persistent coordination of activity among multiple brain regions. It is generally assumed that the pattern of such coordinated activity remains consistent for a given task. Thus, to separate this task-relevant signal from noise, multiple trials of the same task are completed, and the neural response is averaged across trials to generate an event-related potential (ERP). However, from trial to trial, the neuronal activity recorded with electroencephalogram (EEG) is actually spatially and temporally diverse, conflicting with the assumption of a single pattern of activity for a given task. Here, we show that variability in neuronal activity among single time-locked trials arises from the presence of multiple forms of stimulus dependent synchronized activity (i.e., distinct ERPs). We develop a data-driven classification method based on community detection to identify three discrete spatio-temporal clusters, or subtypes, of trials with different patterns of activation that are further associated with differences in decision-making processes. These results demonstrate that differences in the patterns of neural activity during working memory tasks represent fluctuations in the engagement of distinct brain networks and cognitive processes, suggesting that the brain can choose from multiple mechanisms to perform a given task.
Pain typically evolves over time, and the brain needs to learn this temporal evolution to predict how pain is likely to change in the future and orient behavior. This process is termed temporal statistical learning (TSL). Recently, it has been shown that TSL for pain sequences can be achieved using optimal Bayesian inference, which is encoded in somatosensory processing regions. Here, we investigate whether the confidence of these probabilistic predictions modulates the EEG response to noxious stimuli, using a TSL task. Confidence measures the uncertainty about the probabilistic prediction, irrespective of its actual outcome. Bayesian models dictate that the confidence about probabilistic predictions should be integrated with incoming inputs and weight learning, such that it modulates the early components of the EEG responses to noxious stimuli, and this should be captured by a negative correlation: when confidence is higher, the early neural responses are smaller as the brain relies more on expectations/predictions and less on sensory inputs (and vice versa). We show that participants were able to predict the sequence transition probabilities using Bayesian inference, with some forgetting. Then, we find that the confidence of these probabilistic predictions was negatively associated with the amplitude of the N2 and P2 components of the vertex potential: the more confident were participants about their predictions, the smaller the vertex potential. These results confirm key predictions of a Bayesian learning model and clarify the functional significance of the early EEG responses to nociceptive stimuli, as being implicated in confidence-weighted statistical learning.
Objective:
Functional connectivity networks explain the different brain states during diverse motor, cognitive, and sensory functions. Extracting spatial network configurations and their temporal evolution is crucial for understanding the brain function during diverse behavioral tasks.
Approach:
In this study, we introduce the use of dynamic mode decomposition (DMD) to extract the dynamics of brain networks. We compared DMD with principal component analysis (PCA) using real magnetoencephalography (MEG) data during motor and memory tasks.
Main results:
The framework generates dominant spatial brain networks and their time dynamics during simple tasks, such as button press and left-hand movement, as well as more complex tasks, such as picture naming and memory tasks. Our findings show that the DMD-based approach provides a better temporal resolution than the PCA-based approach.
Significance:
We believe that DMD has a very high potential for deciphering the spatiotemporal dynamics of electrophysiological brain network states during tasks.
Objective
To compare nociceptive event-related brain potentials elicited by a high-speed contact-thermode vs. an infrared CO2 laser stimulator.
Methods
Contact heat-evoked potentials (CHEPs) and CO2 laser-evoked potentials (LEPs) were recorded in healthy volunteers using a high-speed contact-thermode (>200°C/s) and a temperature-controlled CO2 laser. In separate experiments, stimuli were matched in terms of target surface temperature (55°C) and intensity of perception. A finite-element model of skin heat transfer was used to explain observed differences.
Results
For 55°C stimuli, CHEPs were reduced in amplitude and delayed in latency as compared to LEPs. For perceptually matched stimuli (CHEPs: 62°C; LEPs: 55°C), amplitudes were similar, but CHEPs latencies remained delayed. These differences could be explained by skin thermal inertia producing differences in the heating profile of contact vs radiant heat at the dermo-epidermal junction.
Conclusions
Provided that steep heating ramps are used, and that target temperature is matched at the dermo–epidermal junction, contact and radiant laser heat stimulation elicit responses of similar magnitude. CHEPs are delayed compared to LEPs.
Significance
CHEPs could be used as an alternative to LEPs for the diagnosis of neuropathic pain. Dedicated normative values must be used to account for differences in skin thermal transfer.
Studies have revealed that memory performance can be affected by perceived gaze direction. However, it remains unclear whether direct gaze promotes or hinders word memory, and the effect of gaze direction on memory of words with different concreteness requires investigation. In the study phase, concrete and abstract words were presented on direct- or averted-gaze faces, and participants were instructed to judge gaze direction and memorize words. In the test phase, participants were asked to discriminate whether a word was old or new. Electroencephalogram recordings were taken in both phases. Behavioral and time-frequency results verified the direct-gaze memory advantage, showing that memory performance was better in the direct-gaze condition than the averted-gaze condition for both concrete and abstract words. Event-related potential results showed that in both direct- and averted-gaze conditions, the early old/new effects (FN400) associated with familiarity were only elicited for concrete words but not abstract words. The late old/new effects (LPC) associated with recollection were elicited in all conditions. More importantly, concrete words elicited greater LPC than abstract words in the direct-gaze condition, whereas there was no such significant LPC difference in the averted-gaze condition. Topographic map analysis found that neural generators between concrete and abstract words differed in the direct-gaze condition but not in the averted-gaze condition. The study supports the hypothesis that direct-gaze promotes memory performance. Furthermore, it is mainly in memory recollection that gaze direction affects words with different concreteness.
Flexible switching between different tasks is an important cognitive ability for humans and it is often studied using the task-switching paradigm. Although the neural mechanisms of task switching have been extensively explored in previous studies using event-related potentials techniques, the activity and process mechanisms of non-phase-locked electroencephalography (EEG) have rarely been revealed. For this reason, this paper discusses the processing of non-phase-locked EEG oscillations in task switching based on frequency-band delineation. First, the roles of each frequency band in local brain regions were summarized. In particular, during the proactive control process (the cue-stimulus interval), delta, theta, and alpha oscillations played more roles in the switch condition while beta played more roles in repeat task. In the reactive control process (post-target), delta, alpha, and beta are all related to sensorimotor function. Then, utilizing the functional connectivity (FC) method, delta connections in the frontotemporal regions and theta connections located in the parietal-to-occipital sites are involved in the preparatory period before task switching, while alpha connections located in the sensorimotor areas and beta connections located in the frontal-parietal cortex are involved in response inhibition. Finally, cross-frequency coupling (CFC) play an important role in working memory among different band oscillation. The present study shows that in addition to the processing mechanisms specific to each frequency band, there are some shared and interactive neural mechanism in task switching by using different analysis techniques.
When listening to musical rhythm, humans can perceive and move to beat-like metrical pulses. Recently, it has been hypothesized that meter perception is related to brain activity responding to the acoustic fluctuation of the rhythmic input, with selective enhancement of the brain response elicited at meter-related frequencies. In the current study, electroencephalography (EEG) was recorded while younger (<35) and older (>60) adults listened to rhythmic patterns presented at two different tempi while intermittently performing a tapping task. Despite significant hearing loss compared to younger adults, older adults showed preserved brain activity to the rhythms. However, age effects were observed in the distribution of amplitude across frequencies. Specifically, in contrast with younger adults, older adults showed relatively larger amplitude at the frequency corresponding to the rate of individual events making up the rhythms as compared to lower meter-related frequencies. This difference is compatible with larger N1-P2 potentials as generally observed in older adults in response to acoustic onsets, irrespective of meter perception. These larger low-level responses to sounds have been linked to processes by which age-related hearing loss would be compensated by cortical sensory mechanisms. Importantly, this low-level effect would be associated here with relatively reduced neural activity at lower frequencies corresponding to higher-level metrical grouping of the acoustic events, as compared to younger adults.
Autistic traits—subclinical forms of characteristics associated with autism spectrum disorders—are associated with poor social interactions and high risks for mental health disorders. We hypothesized that altered sensitivity to social rejection is an important contributor to psychological distress observed among individuals with high autistic traits. Experiment 1 adopted a social‐judgment task and compared behavioral and neural activity in response to social rejection between participants exhibiting either high or low autistic traits (HAT and LAT, respectively). Rejection‐induced hurt feelings, P3 amplitudes, and θ‐oscillation magnitudes were greater in the HAT group than in the LAT group. Mediation analysis indicated that autistic traits heighten rejection‐induced social pain through increasing frontal‐midline θ‐oscillations. Responses to nonsocial feedback in the age‐judgment task were comparable, confirming that the between‐group differences were specific to social negative feedback. Experiment 2 assessed the association between autistic traits, rejection sensitivity, and psychological distress among randomly recruited participants. Results showed that autistic traits affected depressive/anxious symptomatology partially through heightened rejection sensitivity. Therefore, autistic traits heighten sensitivity to rejection‐induced social pain that leads to psychological distress. This finding will help facilitate the development of strategies for coping with social pain and improving mental health for individuals with high autistic traits.
Steady-state visual evoked potential (SSVEP) training feature recognition algorithms utilize user training data to reduce the interference of spontaneous electroencephalogram (EEG) activities on SSVEP response for improved recognition accuracy. The data collection process can be tedious, increasing the mental fatigue of users and also seriously affecting the practicality of SSVEP-based brain-computer interface (BCI) systems. As an alternative, a cross-subject spatial filter transfer (CSSFT) method to transfer an existing user data model with good SSVEP response to new user test data has been proposed. The CSSFT method uses superposition averages of data for multiple blocks of data as transfer data. However, the amplitude and pattern of brain signals are often significantly different across trials. The goal of this study was to improve superposition averaging for the CSSFT method and propose an Ensemble scheme based on ensemble learning, and an Expansion scheme based on matrix expansion. The feature recognition performance was compared for CSSFT and the proposed improved CSSFT method using two public datasets. The results demonstrated that the improved CSSFT method can significantly improve the recognition accuracy and information transmission rate of existing methods. This strategy avoids a tedious data collection process, and promotes the potential practical application of BCI systems.
Decomposition of temporally overlapping sub- epochs from 3-s electroe~icepl~alographic (EEG) epochs time locked to the presentation of visual target stimuli in a selective attention task produced many more components with common scalp maps before stimulus delivery than after it. In particular, this was the case for components accounting for posterior alpha and central mu rhythms. Moving-window ICA decomposition thus appears to be a useful technique for evaluating changes in the independence of activity in different brain regions, i.e. event-related changes in brain dynamic n~odularity. However, common component clusters found by moving- window ICA decomposition strongly resembled those found by decomposition of the whole EEG epochs, suggesting that such whole epoch decomposition reveals stable independent components of EEG signals.
We derive a new self-organizing learning algorithm that maximizes the information transferred in a network of nonlinear units. The algorithm does not assume any knowledge of the input distributions, and is defined here for the zero-noise limit. Under these conditions, information maximization has extra properties not found in the linear case (Linsker 1989). The nonlinearities in the transfer function are able to pick up higher-order moments of the input distributions and perform something akin to true redundancy reduction between units in the output representation. This enables the network to separate statistically independent components in the inputs: a higher-order generalization of principal components analysis. We apply the network to the source separation (or cocktail party) problem, successfully separating unknown mixtures of up to 10 speakers. We also show that a variant on the network architecture is able to perform blind deconvolution (cancellation of unknown echoes and reverberation in a speech signal). Finally, we derive dependencies of information transfer on time delays. We suggest that information maximization provides a unifying framework for problems in "blind" signal processing.
The coherent representation of an object in the visual system has been suggested to be achieved by the synchronization in the gamma-band (30-70 Hz) of a distributed neuronal assembly. Here we measure variations of high-frequency activity on the human scalp. The experiment is designed to allow the comparison of two different perceptions of the same picture. In the first condition, an apparently meaningless picture that contained a hidden Dalmatian, a neutral stimulus, and a target stimulus (twirled blobs) are presented. After the subject has been trained to perceive the hidden dog and its mirror image, the second part of the recordings is performed (condition 2). The same neutral stimulus is presented, intermixed with the picture of the dog and its mirror image (target stimulus). Early (95 msec) phase-locked (or stimulus-locked) gamma-band oscillations do not vary with stimulus type but can be subdivided into an anterior component (38 Hz) and a posterior component (35 Hz). Nonphase-locked gamma-band oscillations appear with a latency jitter around 280 msec after stimulus onset and disappear in averaged data. They increase in amplitude in response to both target stimuli. They also globally increase in the second condition compared with the first one. It is suggested that this gamma-band energy increase reflects both bottom-up (binding of elementary features) and top-down (search for the hidden dog) activation of the same neural assembly coding for the Dalmatian. The relationships between high- and low-frequency components of the response are discussed, and a possible functional role of each component is suggested.
Averaged event-related potential (ERP) data recorded from the human scalp reveal electroencephalographic (EEG) activity that is reliably time-locked and phase-locked to experimental events. We report here the application of a method based on information theory that decomposes one or more ERPs recorded at multiple scalp sensors into a sum of components with fixed scalp distributions and sparsely activated, maximally independent time courses. Independent component analysis (ICA) decomposes ERP data into a number of components equal to the number of sensors. The derived components have distinct but not necessarily orthogonal scalp projections. Unlike dipole-fitting methods, the algorithm does not model the locations of their generators in the head. Unlike methods that remove second-order correlations, such as principal component analysis (PCA), ICA also minimizes higher-order dependencies. Applied to detected-and undetected-target ERPs from an auditory vigilance experiment, the algorithm derived ten components that decomposed each of the major response peaks into one or more ICA components with relatively simple scalp distributions. Three of these components were active only when the subject detected the targets, three other components only when the target went undetected, and one in both cases. Three additional components accounted for the steady-state brain response to a 39-Hz background click train. Major features of the decomposition proved robust across sessions and changes in sensor number and placement. This method of ERP analysis can be used to compare responses from multiple stimuli, task conditions, and subject states.
Transient periods of synchronization of oscillating neuronal discharges in the frequency range 30-80 Hz (gamma oscillations) have been proposed to act as an integrative mechanism that may bring a widely distributed set of neurons together into a coherent ensemble that underlies a cognitive act. Results of several experiments in animals provide support for this idea. In humans, gamma oscillations have been described both on the scalp (measured by electroencephalography and magnetoencephalography) and in intracortical recordings, but no direct participation of synchrony in a cognitive task has been demonstrated so far. Here we record electrical brain activity from subjects who are viewing ambiguous visual stimuli (perceived either as faces or as meaningless shapes). We show for the first time, to our knowledge, that only face perception induces a long-distance pattern of synchronization, corresponding to the moment of perception itself and to the ensuing motor response. A period of strong desynchronization marks the transition between the moment of perception and the motor response. We suggest that this desynchronization reflects a process of active uncoupling of the underlying neural ensembles that is necessary to proceed from one cognitive state to another.
This article provides a new, more comprehensive view of event-related brain dynamics founded on an information-based approach to modeling electroencephalographic (EEG) dynamics. Most EEG research focuses either on peaks 'evoked' in average event-related potentials (ERPs) or on changes 'induced' in the EEG power spectrum by experimental events. Although these measures are nearly complementary, they do not fully model the event-related dynamics in the data, and cannot isolate the signals of the contributing cortical areas. We propose that many ERPs and other EEG features are better viewed as time/frequency perturbations of underlying field potential processes. The new approach combines independent component analysis (ICA), time/frequency analysis, and trial-by-trial visualization that measures EEG source dynamics without requiring an explicit head model.
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This text is the second edition of this book. It expands the widely acclaimed 1981 book, filling more gaps between EEG and the physical sciences. EEG opens a "window on the mind" by finding new connections between psychology and physiology. Topics include synaptic sources, electrode placement, choice of reference, volume conduction, power and coherence, projection of scalp potentials to dura surface, dynamic signatures of conscious experience, and neural networks immersed in global fields of synaptic action.
This article presents, for the first time, a practical method for the direct quantification of frequency-specific synchronization (i.e., transient phase-locking) between two neuroelectric signals. The motivation for its development is to be able to examine the role of neural synchronies as a putative mechanism for long-range neural integration during cognitive tasks. The method, called phase-locking statistics (PLS), measures the significance of the phase covariance between two signals with a reasonable time-resolution (<100 ms). Unlike the more traditional method of spectral coherence, PLS separates the phase and amplitude components and can be directly interpreted in the framework of neural integration. To validate synchrony values against background fluctuations, PLS uses surrogate data and thus makes no a priori assumptions on the nature of the experimental data. We also apply PLS to investigate intracortical recordings from an epileptic patient performing a visual discrimination task. We find large-scale synchronies in the gamma band (45 Hz), e.g., between hippocampus and frontal gyrus, and local synchronies, within a limbic region, a few cm apart. We argue that whereas long-scale effects do reflect cognitive processing, short-scale synchronies are likely to be due to volume conduction. We discuss ways to separate such conduction effects from true signal synchrony.
In this study, a linear decomposition technique, independent component analysis (ICA), is applied to single-trial multichannel EEG data from event-related potential (ERP) experiments. Spatial filters derived by ICA blindly separate the input data into a sum of temporally independent and spatially fixed components arising from distinct or overlapping brain or extra-brain sources. Both the data and their decomposition are displayed using a new visualization tool, the “ERP image,” that can clearly characterize single-trial variations in the amplitudes and latencies of evoked responses, particularly when sorted by a relevant behavioral or physiological variable. These tools were used to analyze data from a visual selective attention experiment on 28 control subjects plus 22 neurological patients whose EEG records were heavily contaminated with blink and other eye-movement artifacts. Results show that ICA can separate artifactual, stimulus-locked, response-locked, and non-event-related background EEG activities into separate components, a taxonomy not obtained from conventional signal averaging approaches. This method allows: (1) removal of pervasive artifacts of all types from single-trial EEG records, (2) identification and segregation of stimulus- and response-locked EEG components, (3) examination of differences in single-trial responses, and (4) separation of temporally distinct but spatially overlapping EEG oscillatory activities with distinct relationships to task events. The proposed methods also allow the interaction between ERPs and the ongoing EEG to be investigated directly. We studied the between-subject component stability of ICA decomposition of single-trial EEG epochs by clustering components with similar scalp maps and activation power spectra. Components accounting for blinks, eye movements, temporal muscle activity, event-related potentials, and event-modulated alpha activities were largely replicated across subjects. Applying ICA and ERP image visualization to the analysis of sets of single trials from event-related EEG (or MEG) experiments can increase the information available from ERP (or ERF) data. Hum. Brain Mapping 14:166–185, 2001. © 2001 Wiley-Liss, Inc.
This paper studies gamma-band responses from two implanted epileptic patients during a simple visual discrimination task. Our main aim was to ascertain, in a reliable manner, whether evoked (stimulus-locked) and induced (triggered by, but not locked to, stimuli) responses are present in intracranial recordings. For this purpose, we introduce new methods adapted to detect the presence of gamma responses at this level of recording, intermediary between EEG-scalp and unicellular responses. The analysis relies on a trial-by-trial time–frequency analysis and on the use of surrogate data for statistical testing. We report that visual stimulation reliably elicits evoked and induced responses in human intracranial recordings. Induced intracranial gamma activity is significantly present in short oscillatory bursts (a few cycles) following visual stimulation. These responses are highly variable from trial to trial, beginning after 200 ms and lasting up to 500 ms. In contrast, intracranial-evoked gamma responses concentrate around 100 ms latencies corresponding to evoked responses observed on the scalp. We discuss our results in relation to scalp gamma response in a similar protocol [Tallon-Baudry et al. (1996) J. Neurosci., 16, 4240–4249] and draw some conclusions for bridging the gap between gamma oscillations observed on the scalp surface and their possible cortical sources.
Simultaneous recordings of the EEG at subdural and scalp electrodes often show very different activities. Large amplitude activity with maximum power between 15 and 30 c/sec can be often observed on the subdural electrodes together with smaller amplitude lower frequencies, whereas on the scalp only a small part of this high frequency activity is seen and the lower frequencies dominate. The impedance between cortex and scalp has been shown to be similar for low and high EEG frequencies and the high attenuation of the beta activity at scalp electrodes is believed to be due to summation of polyphasic cortical activity. The weighted summation of this polyphasic activity across a limited cortical area (spatial average) is similar to the activity of a non-recursive filter between cortex and scalp and has a low pass characteristic.
Event-related desynchronization (ERD) of alpha components was studied in 10 subjects in a visuoverbal judgment task. EEG was recorded from 29 electrodes; ERD, measured by percentage of alpha power decrease, was calculated in 125-ms intervals and displayed in a time course over 7-s intervals and in the form of topographical ERD maps. ERD, interpreted as a measure of activated cortical areas, was studied in the upper and lower alpha bands. It was found that upper alpha band ERD was short-lasting (less than 1 s), localized to the posterior region and not found before stimulation; in contrast to this, lower alpha band ERD was longer-lasting (greater than 1 s), more widespread, with a left hemisphere dominance, and was already present before stimulation. ANOVA revealed significant interactions among alpha frequency bands, scalp location, and pre-/poststimulus intervals. The self-organizing topological Kohonen network was used to analyze the stability of consecutive ERD maps. There is evidence that topographical ERD patterns can be stable for about 300-400 ms.
Examination of the single trials which are traditionally averaged to form late-component ERPs reveals a number of different sub-types of response. This study introduces an automated and robust approach to objectively classify these ERP sub-types. Auditory oddball ERP (target tones) data were examined in 25 normal subjects. Globally optimal vector quantization using simulated annealing (the "Metropolis algorithm") was employed to determine the natural groupings of the single-trial responses that constitute the average ERP. No prior assumptions about the ERP patterns were imposed. This is the first study to employ a cluster analysis technique with globally optimal properties in ERP research. We demonstrate that, due to the presence of many different undesirable local minima, a globally optimal solution is crucial if the classification of the single-trial ERPs is to reflect their real structure. The results of this study showed that only around 40% of single trials had a morphology which resembled the averaged ERP wave form. The remaining single trials had a response morphology which was different from the average, in terms of the amplitude and latency of the components. Single-trial ERP response sub-types may provide fundamental complementary functional information to the ERP average.
Cerebral potentials evoked by CO2 laser stimulation were recorded in 22 normal volunteers. Substantial variability in latency, morphology and amplitude of the responses in individual trials was noted, and in some trials no response appeared to be present. Averaging after latency correction and removal of traces without clear responses was compared with conventional time-locked averaging. We found a greater amplitude and more consistent wave form in the former case, and better preservation of amplitude with increasing age and under conditions of impaired attention. Cross-correlation techniques and Minimum Mean Square Error (MMSE) filtering did not assist identification of responses. We conclude that with laser-evoked cerebral potentials visual identification of components is a simple and reliable way of obtaining an improved estimate of the average evoked potential.
Spontaneous EEG activity was recorded at 56 electrodes in 3 healthy subjects. All subjects displayed event-related desynchronization (ERD) of mu rhythms over the cortical hand area during discrete finger movement. In contrast to this, foot movement resulted in an enhancement or event-related synchronization (ERS) of mu rhythms over the hand area. This phasic synchronization of mu waves was circumscribed and found at electrodes overlying both cortical hand areas. It is speculated, that this ERS represents a short lasting 'idling state' of hand area neurons when other body parts are moved.
An internally or externally paced event results not only in the generation of an event-related potential (ERP) but also in a change in the ongoing EEG/MEG in form of an event-related desynchronization (ERD) or event-related synchronization (ERS). The ERP on the one side and the ERD/ERS on the other side are different responses of neuronal structures in the brain. While the former is phase-locked, the latter is not phase-locked to the event. The most important difference between both phenomena is that the ERD/ERS is highly frequency band-specific, whereby either the same or different locations on the scalp can display ERD and ERS simultaneously. Quantification of ERD/ERS in time and space is demonstrated on data from a number of movement experiments.
This paper studies gamma-band responses from two implanted epileptic patients during a simple visual discrimination task. Our main aim was to ascertain, in a reliable manner, whether evoked (stimulus-locked) and induced (triggered by, but not locked to, stimuli) responses are present in intracranial recordings. For this purpose, we introduce new methods adapted to detect the presence of gamma responses at this level of recording, intermediary between EEG-scalp and unicellular responses. The analysis relies on a trial-by-trial time-frequency analysis and on the use of surrogate data for statistical testing. We report that visual stimulation reliably elicits evoked and induced responses in human intracranial recordings. Induced intracranial gamma activity is significantly present in short oscillatory bursts (a few cycles) following visual stimulation. These responses are highly variable from trial to trial, beginning after 200 ms and lasting up to 500 ms. In contrast, intracranial-evoked gamma responses concentrate around 100 ms latencies corresponding to evoked responses observed on the scalp. We discuss our results in relation to scalp gamma response in a similar protocol [Tallon-Baudry et al. (1996) J. Neurosci., 16, 4240-4249] and draw some conclusions for bridging the gap between gamma oscillations observed on the scalp surface and their possible cortical sources.
Electrical potentials produced by blinks and eye movements present serious problems for electroencephalographic (EEG) and event-related potential (ERP) data interpretation and analysis, particularly for analysis of data from some clinical populations. Often, all epochs contaminated by large eye artifacts are rejected as unusable, though this may prove unacceptable when blinks and eye movements occur frequently.
Frontal channels are often used as reference signals to regress out eye artifacts, but inevitably portions of relevant EEG signals also appearing in EOG channels are thereby eliminated or mixed into other scalp channels. A generally applicable adaptive method for removing artifacts from EEG records based on blind source separation by independent component analysis (ICA) (Neural Computation 7 (1995) 1129; Neural Computation 10(8) (1998) 2103; Neural Computation 11(2) (1999) 606) overcomes these limitations.
Results on EEG data collected from 28 normal controls and 22 clinical subjects performing a visual selective attention task show that ICA can be used to effectively detect, separate and remove ocular artifacts from even strongly contaminated EEG recordings. The results compare favorably to those obtained using rejection or regression methods.
The ICA method can preserve ERP contributions from all of the recorded trials and all the recorded data channels, even when none of the single trials are artifact-free.
The application of a recently proposed denoising implementation for obtaining event-related potentials (ERPs) at the single-trial level is shown. We study its performance in simulated data as well as in visual and auditory ERPs. For the simulated data, the method gives a significantly better reconstruction of the single-trial event-related responses in comparison with the original data and also in comparison with a reconstruction based on conventional Wiener filtering. Moreover, with wavelet denoising we obtain a significantly better estimation of the amplitudes and latencies of the simulated ERPs.
For the real data, the method clearly improves the visualization of both visual and auditory single-trial ERPs. This allows the calculation of better averages as well as the study of systematic or unsystematic variations between trials. Since the method is fast and parameter free, it could complement the conventional analysis of ERPs.
The human electroencephalogram (EEG) is generated predominantly by synchronised cortical excitatory postsynaptic potentials oscillating at frequencies <100 Hz. Unusually, EEG responses to electrical nerve stimulation contain brief bursts of high-frequency (600 Hz) wavelets. Here we show, in awake monkeys, that a subset of primary somatosensory cortex single units consistently fires both bursts and single spikes phase-locked to EEG wavelets. Spike bursts were also evoked by tactile stimuli, proving that this is a natural response mode. EEG wavelets at 600 Hz may therefore permit non-invasive assessment of population spike timing in human cortex.
The aim of this study was to identify EEG changes induced by Adelta-nociceptor activation in single trials. In a preliminary experiment, intense CO(2) laser stimuli were delivered to the hand dorsum of five volunteers. The average amplitude of EEG epochs was estimated in the time-frequency (TF) domain using the continuous Morlet wavelet transform (CMT). The result was used as a TF filter enhancing Adelta-nociceptor induced EEG responses. In a second experiment, eight other subjects were delivered laser stimuli with six intensities. The CMT of each EEG epoch was computed. After applying the TF filter, amplitudes within a predefined interval were summed. Whether this sum predicted the occurrence of Adelta-nociceptor activation was tested using the reaction-time to discriminate between Adelta- or C-fibre mediated detection. Results showed that this method accurately identified single-trial EEG responses to Adelta-nociceptor activation.
Converging evidence from different functional imaging studies indicates that the intensity of activation of different nociceptive areas (including the operculoinsular cortex, the primary somatosensory cortex, and the anterior cingulate gyrus) correlates with perceived pain intensity in the human brain. Brief radiant laser pulses excite selectively Aδ and C nociceptors in the superficial skin layers, provide a purely nociceptive input, and evoke brain potentials (laser-evoked potentials, LEPs) that are commonly used to assess nociceptive pathways in physiological and clinical studies. Aδ-related LEPs are constituted of different components. The earliest is a lateralised, small negative component (N1) which could be generated by the operculoinsular cortex. The major negative component (N2) seems to be mainly the result of activation in the bilateral operculoinsular cortices and contralateral primary somatosensory cortex, and it is followed by a positive component (P2) probably generated by the cingulate gyrus.
By co-activating A partial partial differential- and C-fibre nociceptors, intense CO2 laser heat stimuli produce a dual sensation, composed of first and second pain, but induce only a single A partial partial differential-fibre related late laser evoked potential (LEP). However, when avoiding concomitant activation of A partial partial differential-fibres, C-fibre related ultra-late LEPs are recorded. This poorly understood phenomenon was re-investigated using a method which, unlike time-domain averaging, reveals electroencephalogram (EEG) changes whether or not phase-locked to stimulus onset.
CO2 laser stimuli were applied to the dorsum of the hand. Reaction-time was used to discriminate between A partial partial differential- and C-fibre mediated detections. Analyses were performed using a method based on the time-frequency wavelet transform of EEG epochs.
This study revealed: (1) a novel non-phase-locked component related to the activation of A partial partial differential-fibres occurring at similar latencies as the late LEP; and (2) a widespread post-stimulus event-related desynchronization (ERD) induced by both A partial partial differential- and C-fibres.
A partial partial differential- and C-fibre related LEPs could be electrophysiological correlates of similar brain processes, which, when already engaged by A partial partial differential-fibres, cannot or do not need to be reactivated by the later arriving C-fibre afferent volley. A partial partial differential-fibre related ERD could reflect a transient change of state of brain structures generating these responses.
We present an integrated approach to probabilistic independent component analysis (ICA) for functional MRI (FMRI) data that allows for nonsquare mixing in the presence of Gaussian noise. In order to avoid overfitting, we employ objective estimation of the amount of Gaussian noise through Bayesian analysis of the true dimensionality of the data, i.e., the number of activation and non-Gaussian noise sources. This enables us to carry out probabilistic modeling and achieves an asymptotically unique decomposition of the data. It reduces problems of interpretation, as each final independent component is now much more likely to be due to only one physical or physiological process. We also describe other improvements to standard ICA, such as temporal prewhitening and variance normalization of timeseries, the latter being particularly useful in the context of dimensionality reduction when weak activation is present. We discuss the use of prior information about the spatiotemporal nature of the source processes, and an alternative-hypothesis testing approach for inference, using Gaussian mixture models. The performance of our approach is illustrated and evaluated on real and artificial FMRI data, and compared to the spatio-temporal accuracy of results obtained from classical ICA and GLM analyses.
Abnormal processing of somatosensory inputs in the central nervous system (central sensitization) is the mechanism accounting for the enhanced pain sensitivity in the skin surrounding tissue injury (secondary hyperalgesia). Secondary hyperalgesia shares clinical characteristics with neurogenic hyperalgesia in patients with neuropathic pain. Abnormal brain responses to somatosensory stimuli have been found in patients with hyperalgesia as well as in normal subjects during experimental central sensitization. The aim of this study was to assess the effects of gabapentin, a drug effective in neuropathic pain patients, on brain processing of nociceptive information in normal and central sensitization states. Using functional magnetic resonance imaging (fMRI) in normal volunteers, we studied the gabapentin-induced modulation of brain activity in response to nociceptive mechanical stimulation of normal skin and capsaicin-induced secondary hyperalgesia. The dose of gabapentin was 1,800 mg per os, in a single administration. We found that (i) gabapentin reduced the activations in the bilateral operculoinsular cortex, independently of the presence of central sensitization; (ii) gabapentin reduced the activation in the brainstem, only during central sensitization; (iii) gabapentin suppressed stimulus-induced deactivations, only during central sensitization; this effect was more robust than the effect on brain activation. The observed drug-induced effects were not due to changes in the baseline fMRI signal. These findings indicate that gabapentin has a measurable antinociceptive effect and a stronger antihyperalgesic effect most evident in the brain areas undergoing deactivation, thus supporting the concept that gabapentin is more effective in modulating nociceptive transmission when central sensitization is present.
• deactivation
• fMRI
• hyperalgesia
• nociceptive system
Laser stimulation of Adelta-fibre nociceptors in the skin evokes nociceptive-specific brain responses (laser-evoked potentials, LEPs). The largest vertex complex (N2-P2) is widely used to assess nociceptive pathways in physiological and clinical studies. The aim of this study was to develop an automated method to measure amplitudes and latencies of the N2 and P2 peaks on a single-trial basis.
LEPs were recorded after Nd:YAP laser stimulation of the left hand dorsum in 7 normal volunteers. For each subject, a basis set of 4 regressors (the N2 and P2 waveforms and their respective temporal derivatives) was derived from the time-averaged data and regressed against every single-trial LEP response. This provided a separate quantitative estimate of amplitude and latency for the N2 and P2 components of each trial.
All estimates of LEP parameters correlated significantly with the corresponding measurements performed by a human expert (N2 amplitude: R2=0.70; P2 amplitude: R2=0.70; N2 latency: R2=0.81; P2 latency: R2=0.59. All P<0.0001). Furthermore, regression analysis was able to extract an LEP response from a subset of the trials that had been classified by the human expert as without response.
This method provides a simple, fast and unbiased measurement of different components of single-trial LEP responses.
This method is particularly desirable in several experimental conditions (e.g. drug studies, correlations with experimental variables, simultaneous EEG/fMRI and low signal-to-noise ratio data) and in clinical practice. The described multiple linear regression approach can be easily implemented for measuring evoked potentials in other sensory modalities.
The ability to perceive and withdraw rapidly from noxious environmental stimuli is crucial for survival. When heat stimuli are applied to primate hairy skin, first pain sensation is mediated by type-II A-fibre nociceptors (II-AMHs). In contrast, the reported absence of first pain and II-AMH microneurographical responses when heat stimuli are applied to the hand palm has led to the notion that II-AMHs are lacking in this primate glabrous skin. The aim of this study was to assess the effect of hairy and glabrous skin stimulation on neural transmission of nociceptive inputs elicited by different kinds of thermal heating. We recorded psychophysical and EEG brain responses to radiant (laser-evoked potentials, LEPs) and contact heat stimuli (contact heat-evoked potentials, CHEPs) delivered to the dorsum and the palm of the hand in normal volunteers. Brain responses were analysed at a single-trial level, using an automated approach based on multiple linear regression. Laser stimulation of hairy and glabrous skin at the same energy elicited remarkably similar psychophysical ratings and LEPs. This finding provides strong evidence that first pain to heat does exist in glabrous skin, and suggests that similar nociceptive afferents, with the physiological properties of II-AMHs, mediate first pain to heat stimulation of glabrous and hairy skin in humans. In contrast, when contact heat stimuli were employed, a significantly higher nominal temperature had to be applied to glabrous skin in order to achieve psychophysical ratings similar to those obtained following hairy skin stimulation, and CHEPs following glabrous skin stimulation had significantly longer latencies (N2 wave, +25%; P2 wave, +24%) and smaller amplitudes (N2 wave, -40%; P2 wave, -44%) than CHEPs following hairy skin stimulation. Irrespective of the stimulated territory, CHEPs always had significantly longer latencies (hairy skin N2 wave, +75%; P2 wave, +56%) and smaller amplitudes (hairy skin N2 wave, -42%; P2 wave, -19%) than LEPs. These findings are consistent with the thickness-dependent delay and attenuation of the temperature waveform at nociceptor depth when conductive heating is applied, and suggest that the previously reported lack of first pain and microneurographical II-AMH responses following glabrous skin stimulation could have been the result of a search bias consequent to the use of long-wavelength radiant heating (i.e. CO(2) laser) as stimulation procedure.
The event-related potential (ERP) is one of the most popular measures in human cognitive neuroscience. During the last few years there has been a debate about the neural fundamentals of ERPs. Two models have been proposed: The evoked model states that additive evoked responses which are completely independent of ongoing background electroencephalogram generate the ERP. On the other hand the phase reset model suggests a resetting of ongoing brain oscillations to be the neural generator of ERPs. Here, evidence for either of the two models is presented and validated, and their possible impact on cognitive neuroscience is discussed. In addition, future prospects on this field of research are presented.
Independent component analysis (ICA) has been successfully employed in the study of single-trial evoked potentials (EPs). In this paper, we present an iterative temporal ICA methodology that processes multielectrode single-trial EPs, one channel at a time, in contrast to most existing methodologies which are spatial and analyze EPs from all recording channels simultaneously. The proposed algorithm aims at enhancing individual components in an EP waveform in each single trial, and relies on a dynamic template to guide EP estimation. To quantify the performance of this method, we carried out extensive analyses with artificial EPs, using different models for EP generation, including the phase-resetting and the classical additive-signal models, and several signal-to-noise ratios and EP component latency jitters. Furthermore, to validate the technique, we employed actual recordings of the auditory N100 component obtained from normal subjects. Our results with artificial data show that the proposed procedure can provide significantly better estimates of the embedded EP signals compared to plain averaging, while with actual EP recordings, the procedure can consistently enhance individual components in single trials, in all subjects, which in turn results in enhanced average EPs. This procedure is well suited for fast analysis of very large multielectrode recordings in parallel architectures, as individual channels can be processed simultaneously on different processors. We conclude that this method can be used to study the spatiotemporal evolution of specific EP components and may have a significant impact as a clinical tool in the analysis of single-trial EPs.
Prior hypotheses in functional brain imaging are often formulated by constraining the data analysis to regions of interest (ROIs). In this context, this approach yields higher sensitivity than whole brain analyses, which could be particularly important in drug development studies and clinical decision making. Here we systematically examine the effects of different ROI definition criteria on the results inferred from a hypothesis-driven pharmacological fMRI experiment, with the aim of maximising sensitivity and providing a recommended procedure for similar studies. In order to achieve this, we compared different criteria for selecting both anatomical and functional ROIs. Anatomical ROIs were defined (i) specifically for each subject, (ii) at group level, and (iii) using a Talairach-like atlas, in order to assess the effects of inter-subject anatomical variability. Functional ROIs (fROIs) were defined, both for each subject and at group level, by (i) selecting the voxels with the highest Z-score from each study session, and (ii) selecting an inclusive union of significantly active voxels across all sessions. A single value was used to summarise the response within each ROI. For anatomical ROIs we used the mean of the parameter estimates (beta values) of either all voxels or the top 20% active voxels. For fROIs we used the mean beta value of all voxels constituting the ROI. The results were assessed in terms of the achieved sensitivity in detecting the experimental effect. The use of single-subject anatomical ROIs combined with a summary value calculated from the top 20% fraction of active voxels was the most reliable and sensitive approach for detecting the experimental effect. The use of fROIs from individual sessions introduced unacceptable biases in the results, while the use of union fROIs yielded a lower sensitivity than anatomical ROIs. For these reasons, fROIs should be employed with caution when it is not possible to make clear anatomical prior hypotheses.
Bayesian model order selection is considered in relation to the singular value decomposition (SVD) and the discrete Karhunen-Loève transform (DKLT). There are many applications of the SVD and DKLT where it is necessary to discard some of the small singular values that may represent corrupted signal information. Often this task is performed heuristically or in an ad hoc manner. The Bayesian approach to model order selection involves the determination of the evidence or the conditional posterior probability of the model structure given the data; this framework allows {the relative probabilities of all possible candidate models to be compared explicitly. Applied to the SVD, the evidence formulation enables the number of nonzero singular values (and hence the effective rank) of a singular or ill-conditioned matrix to be determined analytically. For the DKLT, the evidence allows the determination of the optimal number of basis vectors to choose for the signal reconstruction. In addition, the Bayesian method allows prior information such as physical smoothness constraints to be incorporated directly into the problem specification. Derivations of the evidence formulae are included along with results that illustrate the usefulness of the method.
This article presents, for the first time, a practical method for the direct quantification of frequency-specific synchronization (i.e., transient phase-locking) between two neuroelectric signals. The motivation for its development is to be able to examine the role of neural synchronies as a putative mechanism for long-range neural integration during cognitive tasks. The method, called phase-locking statistics (PLS), measures the significance of the phase covariance between two signals with a reasonable time-resolution (,100 ms). Unlike the more traditional method of spectral coherence, PLS separates the phase and amplitude components and can be directly interpreted in the framework of neural integration.
A quantitative study of gamma-band activity in human Analysis and visualization of single-trial event-related potentials
- Jp Lachaux
- E Rodriguez
- J Martinerie
- C Adam
- D Hasboun
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