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Spectrograms of the recorded data averaged over all subjects at the four stimulation frequencies for non-interpolated (top-row) and interpolated (bottom-row) stimulation. Additional signal power was elicited in specific frequency bands when stimulus presentation was interpolated at a refresh rate of 85 Hz (10 and 15 Hz), but not at a refresh rate of 120 Hz (10.63 and 14.17 Hz). 0 dB corresponds to 1 µV
Source publication
Background
Steady-state visual evoked potentials have been utilized widely in basic and applied research in recent years. These oscillatory responses of the visual cortex are elicited by flickering stimuli. They have the same fundamental frequency as the driving stimulus and are highly sensitive to manipulations of attention and stimulus properties...
Citations
... This allows driving the luminance of each pixel with high temporal precision, resulting in smooth sinusoidal modulations without unwanted harmonics. This is different from interpolation techniques in which "impossible frequencies" (frequencies that are not multiples of the refresh rate) are approximated by presenting the stimulus at lower intensities on the frames around the on-off reversal to elicit SSVEPs (Andersen and Müller 2015). With a projector that is capable of presenting stimuli at a refresh rate of 1440 Hz, one could theoretically modulate the luminance of a stimulus at frequencies up to 720 Hz (the Nyquist frequency of the projector). ...
Frequency tagging has been successfully used to investigate selective stimulus processing in electroencephalography (EEG) or magnetoencephalography (MEG) studies. Recently, new projectors have been developed that allow for frequency tagging at higher frequencies (>60 Hz). This technique, rapid invisible frequency tagging (RIFT), provides two crucial advantages over low-frequency tagging as (i) it leaves low-frequency oscillations unperturbed, and thus open for investigation, and ii) it can render the tagging invisible, resulting in more naturalistic paradigms and a lack of participant awareness. The development of this technique has far-reaching implications as oscillations involved in cognitive processes can be investigated, and potentially manipulated, in a more naturalistic manner.
... Stimulus presentation is indeed necessarily synchronized with the device's refresh rate, which can severely restrict the range of possible stimulation frequencies. For example with a visual flickering stimulation, if all stimuli have to have a 50/50 on/off ratio for the purpose of the experiment, then frequencies are limited to even integer divisors of the monitor's refresh rate (but see (Andersen and Müller, 2015) for interpolation methods). Finally, if the paradigm involves multiple stimulation frequencies, one should ensure that the successive frequencies and their harmonics can be separated by a minimum of 4 to 8 frequency bins during the spectral analysis. ...
Electroencephalography (EEG) is a non-invasive and painless recording of cerebral activity, particularly well-suited for studying young infants, allowing the inspection of cerebral responses in a constellation of different ways. Of particular interest for developmental cognitive neuroscientists is the use of rhythmic stimulation, and the analysis of steady-state evoked potentials (SS-EPs) – an approach also known as frequency tagging. In this paper we rely on the existing SS-EP early developmental literature to illustrate the important advantages of SS-EPs for studying the developing brain. We argue that (1) the technique is both objective and predictive: the response is expected at the stimulation frequency (and/or higher harmonics), (2) its high spectral specificity makes the computed responses particularly robust to artifacts, and (3) the technique allows for short and efficient recordings, compatible with infants’ limited attentional spans. We additionally provide an overview of some recent inspiring use of the SS-EP technique in adult research, in order to argue that (4) the SS-EP approach can be implemented creatively to target a wide range of cognitive and neural processes. For all these reasons, we expect SS-EPs to play an increasing role in the understanding of early cognitive processes. Finally, we provide practical guidelines for implementing and analyzing SS-EP studies.
... Here, the available tagging frequencies can be computed by dividing 1000 by the product of the refresh interval and the integers between 2 and 20, resulting in the same potential tagging frequencies for on-off flicker. Note that periodic presentations with other stimulation (e. g., sinusoidal, rather than on-off, modulation of luminance) may result in additional frequencies becoming available, as discussed for example by Andersen and Müller (2015). ...
Steady-state visual evoked potential (ssVEP) frequency tagging is an increasingly used method in electrophysiological studies of visual attention and perception. Frequency tagging is suitable for studies examining a wide range of populations, including infants and children. Frequency tagging involves the presentation of different elements of a visual array at different temporal rates, thus using stimulus timing to “tag” the brain response to a given element by means of a unique time signature. Leveraging the strength of the ssVEP frequency tagging method to isolate brain responses to concurrently presented and spatially overlapping visual objects requires specific signal processing methods. Here, we introduce the FreqTag suite of functions, an open source MATLAB toolbox. The purpose of the FreqTag toolbox is three-fold. First, it will equip users with a set of transparent and reproducible analytical tools for the analysis of ssVEP data. Second, the toolbox is designed to illustrate fundamental features of frequency domain and time-frequency domain approaches. Finally, decision criteria for the application of different functions and analyses are described. To promote reproducibility, raw algorithms are provided in a modular fashion, without additional hidden functions or transformations. This approach is intended to facilitate a fundamental understanding of the transformations and algorithmic steps in FreqTag, and to allow users to visualize and test each step in the toolbox.
... High-frequency stimuli can decrease visual fatigue caused by flickering, thus making the SSVEP-based BCI a more comfortable system (Wang et al., 2005;Diez et al., 2011;Volosyak et al., 2011). Other visual stimulation techniques have been proposed to enhance SSVEP BCIs performance, like amplitude modulation (Chang et al., 2014), variation of the duty cycle (Shyu et al., 2013) or interpolation techniques (Andersen and Müller, 2015). ...
Brain-Computer Interface (BCI) is a technology that uses electroencephalographic (EEG) signals to control external devices, such as Functional Electrical Stimulation (FES). Visual BCI paradigms based on P300 and Steady State Visually Evoked potentials (SSVEP) have shown high potential for clinical purposes. Numerous studies have been published on P300- and SSVEP-based non-invasive BCIs, but many of them present two shortcomings: (1) they are not aimed for motor rehabilitation applications, and (2) they do not report in detail the artificial intelligence (AI) methods used for classification, or their performance metrics. To address this gap, in this paper the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology was applied to prepare a systematic literature review (SLR). Papers older than 10 years, repeated or not related to a motor rehabilitation application, were excluded. Of all the studies, 51.02% referred to theoretical analysis of classification algorithms. Of the remaining, 28.48% were for spelling, 12.73% for diverse applications (control of wheelchair or home appliances), and only 7.77% were focused on motor rehabilitation. After the inclusion and exclusion criteria were applied and quality screening was performed, 34 articles were selected. Of them, 26.47% used the P300 and 55.8% the SSVEP signal. Five applications categories were established: Rehabilitation Systems (17.64%), Virtual Reality environments (23.52%), FES (17.64%), Orthosis (29.41%), and Prosthesis (11.76%). Of all the works, only four performed tests with patients. The most reported machine learning (ML) algorithms used for classification were linear discriminant analysis (LDA) (48.64%) and support vector machine (16.21%), while only one study used a deep learning algorithm: a Convolutional Neural Network (CNN). The reported accuracy ranged from 38.02 to 100%, and the Information Transfer Rate from 1.55 to 49.25 bits per minute. While LDA is still the most used AI algorithm, CNN has shown promising results, but due to their high technical implementation requirements, many researchers do not justify its implementation as worthwile. To achieve quick and accurate online BCIs for motor rehabilitation applications, future works on SSVEP-, P300-based and hybrid BCIs should focus on optimizing the visual stimulation module and the training stage of ML and DL algorithms.
... Finally, the SSVEP responses from the Oz channel were used as the main source of the evoked response for further data analysis (Di Russo et al., 2007;Bianciardi et al., 2009;Vialatte et al., 2010). Specifically, we used the SSVEP temporal window from 500 to 2500 ms after the onset of each stimulus to exclude any VEP and to increase the signal to noise ratio of the SSVEP (Andersen et al., 2013;Andersen and Müller, 2015). SPM8 (Wellcome Trust Centre for Neuroimaging 1 ), and customized Matlab codes (The MathWorks Inc., Natick, MA, United States) were utilized for further data analysis. ...
Patterns in external sensory stimuli can rapidly entrain neuronally generated oscillations observed in electrophysiological data. Here, we manipulated the temporal dynamics of visual stimuli with cross-frequency coupling (CFC) characteristics to generate steady-state visual evoked potentials (SSVEPs). Although CFC plays a pivotal role in neural communication, some cases reporting CFC may be false positives due to non-sinusoidal oscillations that can generate artificially inflated coupling values. Additionally, temporal characteristics of dynamic and non-linear neural oscillations cannot be fully derived with conventional Fourier-based analyses mainly due to trade off of temporal resolution for frequency precision. In an attempt to resolve these limitations of linear analytical methods, Holo-Hilbert Spectral Analysis (HHSA) was investigated as a potential approach for examination of non-linear and non-stationary CFC dynamics in this study. Results from both simulation and SSVEPs demonstrated that temporal dynamic and non-linear CFC features can be revealed with HHSA. Specifically, the results of simulation showed that the HHSA is less affected by the non-sinusoidal oscillation and showed possible cross frequency interactions embedded in the simulation without any a priori assumptions. In the SSVEPs, we found that the time-varying cross-frequency interaction and the bidirectional coupling between delta and alpha/beta bands can be observed using HHSA, confirming dynamic physiological signatures of neural entrainment related to cross-frequency coupling. These findings not only validate the efficacy of the HHSA in revealing the natural characteristics of signals, but also shed new light on further applications in analysis of brain electrophysiological data with the aim of understanding the functional roles of neuronal oscillation in various cognitive functions.
... Placeholders were created by phase-scrambling chequerboards (Fig. 1c). Placeholder flicker was counter-phased using an interpolation approach 69 . Placeholders at the target and distractor locations flickered at unique frequencies (17 and 19 Hz) throughout the trial following rest (5500-6000 ms total), which were counterbalanced across target positions. ...
It is often necessary for individuals to coordinate their actions with others. In the real world, joint actions rely on the direct observation of co-actors and rhythmic cues. But how are joint actions coordinated when such cues are unavailable? To address this question, we recorded brain activity while pairs of participants guided a cursor to a target either individually (solo control) or together with a partner (joint control) from whom they were physically and visibly separated. Behavioural patterns revealed that joint action involved real-time coordination between co-actors and improved accuracy for the lower performing co-actor. Concurrent neural recordings and eye tracking revealed that joint control affected cognitive processing across multiple stages. Joint control involved increases in both behavioural and neural coupling – both quantified as interpersonal correlations – peaking at action completion. Correspondingly, a neural offset response acted as a mechanism for and marker of interpersonal neural coupling, underpinning successful joint actions.
... Challenges in building such a stimulation platform on a smartphone include the limited number of visual stimuli frequencies presentable, as these are limited to ones that are integer divisors of the smartphone screen refresh rate [14]. Similarly, extracting EEG phase in real-time on a smartphone requires noise-robust algorithms with low computational cost, to satisfy the overall low loop latency requirement, which is in the order of milliseconds. ...
... On the other hand, an 11 Hz flicker is not directly possible since it would require 2.725 frames on and 2.725 frames off. To address this issue, an interpolation algorithm proposed by [14] is used, where intermediate intensity values for the screen brightness are used at specific frames for frequencies that are not integer divisors of the screen refresh rate. The intensity value (w) at each frame (i) is given as, ...
... Here R is the screen refresh rate, f is the desired stimulus frequency, and r on is the fraction of the stimulus cycle in which the stimulus is on [14]. This algorithm was used to generate flicker frequencies in the range 1-20 Hz, matching standard EEG frequency bands. ...
This paper presents a smartphone based system for presenting light and sound stimulation to a user for neuromodulation experiments. A smartphone platform was used to increase ease of use and enable out-of-the-clinic experiments. The created Android app provides both visual and auditory entrainment stimuli, along with a real-time extraction of ongoing electroencephalogram (EEG) phase using a Phase Locked Loop (PLL) for enabling closed loop simulations. Both visual and auditory stimulation is provided accurately in the 1-20 Hz range using the smartphone class hardware. Test results for both gave R squared fit values of 1 between a fitted line and the measured stimulation frequencies. Data from 13 subjects showed the PLL could track EEG phase in the slow oscillation band with an error of 14.83±38.47°, with an average of 0.04±0.20 ms processing overhead per sample.
... The stimulation frequencies ranging between 5 and 90 Hz could be used to elicit SSVEPs, but only few frequencies could be used due to the technological constraints of the current systems [38]. For instance, the conventional frequency coding method can only generate specific frequencies due to the limitation posed by the monitor refresh rate [39]. For example, a 60 Hz refresh rate monitor can only generate frequencies that are integer divisible of 60, e.g., 60/2 = 30, 60/3 = 20, and 60/4 = 15. ...
... It is noteworthy that none of the subjects felt tiredness above a medium level after using the proposed speller, whereas 75% of the subjects were highly tired after using the other spellers. According to the results, all of the subjects were significantly more comfortable using the proposed speller system as compared with the spellers used in previous BCI studies [34,39]. Thus, the proposed speller could be implemented as a more comfortable and easy-to-use mode for practical and clinical applications, e.g., patients in locked-in state [89]. ...
... It was also indicated by previous studies that the simultaneous flickering of a large number of stimuli can cause discomfort and fatigue to users, and this can also affect the performance of the system. Furthermore, another important advantage of the proposed framework is that it can overcome the restrictions and limitations that are caused by the monitor refresh rate to generate large number of frequencies to decode large number of targets [34,38,39], since the proposed speller only uses six frequencies that can be generated by any monitor. In the light of the above, the proposed BCI-speller system could be used as an efficient and better alternative to the previous speller systems. ...
Steady-state visual evoked potentials (SSVEPs) have been extensively utilized to develop brain–computer interfaces (BCIs) due to the advantages of robustness, large number of commands, high classification accuracies, and information transfer rates (ITRs). However, the use of several simultaneous flickering stimuli often causes high levels of user discomfort, tiredness, annoyingness, and fatigue. Here we propose to design a stimuli-responsive hybrid speller by using electroencephalography (EEG) and video-based eye-tracking to increase user comfortability levels when presented with large numbers of simultaneously flickering stimuli. Interestingly, a canonical correlation analysis (CCA)-based framework was useful to identify target frequency with a 1 s duration of flickering signal. Our proposed BCI-speller uses only six frequencies to classify forty-eight targets, thus achieve greatly increased ITR, whereas basic SSVEP BCI-spellers use an equal number of frequencies to the number of targets. Using this speller, we obtained an average classification accuracy of 90.35 ± 3.597% with an average ITR of 184.06 ± 12.761 bits per minute in a cued-spelling task and an ITR of 190.73 ± 17.849 bits per minute in a free-spelling task. Consequently, our proposed speller is superior to the other spellers in terms of targets classified, classification accuracy, and ITR, while producing less fatigue, annoyingness, tiredness and discomfort. Together, our proposed hybrid eye tracking and SSVEP BCI-based system will ultimately enable a truly high-speed communication channel.
... Finally, the SSVEP responses from the Oz channel were used as the main source of the evoked response for further data analysis (Bianciardi et al., 2009;Di Russo et al., 2007;Vialatte et al., 2010). Specifically, we used the SSVEP windows from 500 to 2500 ms after the onset of each stimulus to exclude the VEP and to increase the signal to noise ratio of the SSVEP (Andersen, Hillyard, & Muller, 2013;Andersen & Müller, 2015). SPM8 (Wellcome Trust Centre for Neuroimaging; https://www.fil.ion.ucl.ac.uk/spm/), ...
The response latency of steady-state visually evoked potentials (SSVEPs) is a sensitive measurement for investigating visual functioning of the human brain, specifically in visual development and for clinical evaluation. This latency can be measured from the slope of phase versus frequency of responses by using multiple frequencies of stimuli. In an attempt to provide an alternative measurement of this latency, this study utilized an envelope response of SSVEPs elicited by amplitude-modulated visual stimulation and then compared with the envelope of the generating signal, which was recorded simultaneously with the electroencephalography recordings. The advantage of this measurement is that it successfully estimates the response latency based on the physiological envelope in the entire waveform. Results showed the response latency at the occipital lobe (Oz channel) was approximately 104.55 ms for binocular stimulation, 97.14 ms for the dominant eye, and 104.75 ms for the nondominant eye with no significant difference between these stimulations. Importantly, the response latency at frontal channels (125.84 ms) was significantly longer than that at occipital channels (104.11 ms) during binocular stimulation. Together with strong activation of the source envelope at occipital cortex, these findings support the idea of a feedforward process, with the visual stimuli propagating originally from occipital cortex to anterior cortex. In sum, these findings offer a novel method for future studies in measuring visual response latencies and also potentially shed a new light on understanding of how long collective neural activities take to travel in the human brain.
... SSVEP amplitudes were larger for the high compared with low accuracy group, especially for the first three harmonics. The grand mean SSVEP amplitude topographies of the first harmonics revealed maximal amplitudes at occipitoparietal sites, consistent with previous frequency tagging studies of attention [49][50][51] , with larger amplitudes for the high compared with low accuracy group (Fig. 4c). This effect was confirmed statistically: classification accuracy (%) was strongly positively correlated at the between-subjects level with the mean SNR of the first harmonic flicker frequencies (r 15 = 0.84, p < 0.001; Fig. 4d). ...
Free communication is one of the cornerstones of modern civilisation. While manual keyboards currently allow us to interface with computers and manifest our thoughts, a next frontier is communication without manual input. Brain-computer interface (BCI) spellers often achieve this by decoding patterns of neural activity as users attend to flickering keyboard displays. To date, the highest performing spellers report typing rates of ~10.00 words/minute. While impressive, these rates are typically calculated for experienced users repetitively typing single phrases. It is therefore not clear whether naïve users are able to achieve such high rates with the added cognitive load of genuine free communication, which involves continuously generating and spelling novel words and phrases. In two experiments, we developed an open-source, high-performance, non-invasive BCI speller and examined its feasibility for free communication. The BCI speller required users to focus their visual attention on a flickering keyboard display, thereby producing unique cortical activity patterns for each key, which were decoded using filter-bank canonical correlation analysis. In Experiment 1, we tested whether seventeen naïve users could maintain rapid typing during prompted free word association. We found that information transfer rates were indeed slower during this free communication task than during typing of a cued character sequence. In Experiment 2, we further evaluated the speller’s efficacy for free communication by developing a messaging interface, allowing users to engage in free conversation. The results showed that free communication was possible, but that information transfer was reduced by voluntary textual corrections and turn-taking during conversation. We evaluated a number of factors affecting the suitability of BCI spellers for free communication, and make specific recommendations for improving classification accuracy and usability. Overall, we found that developing a BCI speller for free communication requires a focus on usability over reduced character selection time, and as such, future performance appraisals should be based on genuine free communication scenarios.
