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EEG microstates. Subjects were asked to execute pure planar reaching movements or to reach, grasp, and hold 16 different objects with four different grasp types. A screen was used to indicate the experimental timeline. (a) Subject-specific EEG microstates were extracted for each subject and dataset using CARTOOL⁷⁷. The plot reports typical subject-specific microstates for resting state (white bar – first row), pure-reaching during movement phase (yellow bar – second row), reaching-and-grasping during movement phase (blue bar – third row), pure-reaching during holding phase (light yellow bar – fourth row), and reaching-and-grasping during holding phase (light blue bar – fifth row). Red and blue colors correspond to positive and negative voltages, respectively. (b) To identify the presence of spontaneous brain activity during motor behavior, subject-specific microstates found during motor task conditions were matched to the resting-state microstates. The non-matching microstates were then compared across motor task conditions. Correlation across conditions (c) and across grasp types (d) are reported coded in red for each microstate. Correlations are reported in absolute values. Grey squares code “correlation not available” (e.g., for microstate E the comparisons with the resting-state condition are not possible because microstate E is not present during resting state). Black squares code “correlation meaningless”. Indeed, we considered meaningless to compare the holding phase of a specific motor task with the movement phase of another motor task (e.g., the holding phase of pure-reaching with the movement phase of a grasp type). For the purpose of summarizing the results across all subjects, subject-specific microstates were matched across individual using a second k-means cluster analysis. For each subject and microstate, averaged correlation values across conditions are reported coded in blue. (e) Correlation values were calculated between the maps presenting highest similarity within a cluster and the subject-specific microstates within the same cluster.
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Electroencephalography (EEG) of brain activity can be represented in terms of dynamically changing topographies (microstates). Notably, spontaneous brain activity recorded at rest can be characterized by four distinctive topographies. Despite their well-established role during resting state, their implication in the generation of motor behavior is...
Citations
... Motor imagery EEG (MI-EEG) is a spontaneous electrical potential generated when subjects imagine movements without actual movement. MI-EEG-based BCIs have garnered attention for their applications in motor control [2], neural rehabilitation [3], and specialized environmental operations [4]. ...
... OF EEG-DBNET BLOCKS FOR BCI COMPETITION IV-2A * Average of nine subjects.1 Average pooling for temporal branch and max pooling for spectral branch.2 Average pooling for both branches.3 ...
Motor imagery electroencephalogram (EEG)-based brain-computer interfaces (BCIs) aid individuals with restricted limb mobility. However, challenges like low signal-to-noise ratio and limited spatial resolution hinder accurate feature extraction from EEG signals, impacting classification. To tackle these issues, we propose an end-to-end dual-branch neural network (EEG-DBNet). This network decodes temporal and spectral sequences separately using distinct branches. Each branch has local and global convolution blocks for extracting local and global features. The temporal branch employs three convolutional layers with smaller kernels, fewer channels, and average pooling, while the spectral branch uses larger kernels, more channels, and max pooling. Global convolution blocks then extract comprehensive features. Outputs from both branches are concatenated and fed to fully connected layers for classification. Ablation experiments demonstrate that our architecture, with specialized convolutional parameters for temporal and spectral sequences, significantly improves classification accuracy compared to single-branch structures. The complementary relationship between local and global convolutional blocks compensates for traditional CNNs' limitations in global feature extraction. Our method achieves accuracies of 85.84% and 91.42% on BCI Competition 4-2a and 4-2b datasets, respectively, surpassing existing state-of-the-art models. Source code is available at https://github.com/xicheng105/EEG-DBNet.
... We selected C3, CZ, C4, CP5, CP1, CPZ, CP2, CP6, P3, PZ and P4 electrodes as prior studies have indicated that spectral power in mu (8-12 Hz) and beta (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) frequency bands over the sensorimotor area are associated with motor processing. [44][45][46][47] For each participant, we computed the grand mean ERSP by firstly taking the mean of the baseline-normalized ERSPs of 45 trials and then taking the mean over the selected electrodes. We also computed a mean ERSP over the 15 trials per incentive level to investigate whether the ERSP were modulated by the reward condition ($1, $10 or $50). ...
Apathy is one of the most prevalent non-motor symptoms of Parkinson’s disease and is characterized by decreased goal-directed behaviour due to a lack of motivation and/or impaired emotional reactivity. Despite its high prevalence, the neurophysiological mechanisms underlying apathy in Parkinson’s disease, which may guide neuromodulation interventions, are poorly understood. Here, we investigated the neural oscillatory characteristics of apathy in Parkinson’s disease using EEG data recorded during an incentivized motor task. Thirteen Parkinson’s disease patients with apathy and 13 Parkinson’s disease patients without apathy as well as 12 healthy controls were instructed to squeeze a hand grip device to earn a monetary reward proportional to the grip force they used. Event-related spectral perturbations during the presentation of a reward cue and squeezing were analysed using multiset canonical correlation analysis to detect different orthogonal components of temporally consistent event-related spectral perturbations across trials and participants. The first component, predominantly located over parietal regions, demonstrated suppression of low-beta (12–20 Hz) power (i.e. beta desynchronization) during reward cue presentation that was significantly smaller in Parkinson’s disease patients with apathy compared with healthy controls. Unlike traditional event-related spectral perturbation analysis, the beta desynchronization in this component was significantly correlated with clinical apathy scores. Higher monetary rewards resulted in larger beta desynchronization in healthy controls but not Parkinson’s disease patients. The second component contained gamma and theta frequencies and demonstrated exaggerated theta (4–8 Hz) power in Parkinson’s disease patients with apathy during the reward cue and squeezing compared with healthy controls (HCs), and this was positively correlated with Montreal Cognitive Assessment scores. The third component, over central regions, demonstrated significantly different beta power across groups, with apathetic groups having the lowest beta power. Our results emphasize that altered low-beta and low-theta oscillations are critical for reward processing and motor planning in Parkinson’s disease patients with apathy and these may provide a target for non-invasive neuromodulation.
... It explores progress toward transitioning this technology from controlled laboratory and clinical settings to real-world applications, often referred to as "in the wild" deployments. By enabling real-world applications, BCIs can enhance accessibility for various fields, including healthcare and assistive technology, unlocking the potential for widespread societal benefits and fostering innovation beyond controlled settings (Pirondini et al., 2017). The pursuit of new and improved machine learning and signal processing for neurotechnologies attracts a lot of new researchers as it presents very challenging problems which require innovative solutions and transdisciplinary approaches. ...
... These microstates are considered the fundamental building blocks of the chain of spontaneous conscious mental processes and have been associated with the level of mentation (Michel & Koenig, 2018). Recent research has shown that the temporal evolution of microstate series varies depending on various physiological processes, such as sleep, motor tasks, mentation, hypnosis, as well as mental and psychiatric disorders (Brodbeck et al., 2012;Katayama et al., 2007;Khanna et al., 2014;Lehmann et al., 2005;Pierpaolo et al., 2022;Pirondini et al., 2017;Tait et al., 2020;Zappasodi et al., 2019). These disorders have been found to manifest EEG microstates with scale-free dynamics . ...
... The occurrence of microstates has been based on the current understanding that brain functions arise from massive parallel processing in diffused and distributed brain networks (Bressler & Menon, 2010;Li et al., 2022). Although microstates have historically been linked to the resting state (RS) of the brain, recent investigations associated microstate dynamics with other functional cognitive activities or physiological conditions (Hu et al., 2022;Katayama et al., 2007;Li et al., 2022;Michel & Koenig, 2018;Pierpaolo et al., 2022;Pirondini et al., 2017). This allowed the identification of numerous cortical and subcortical brain regions whose activity has been linked to microstate dynamics, particularly the insula, thalamus, amygdala, anterior cingulate cortex, and others Musso et al., 2010). ...
Electroencephalographic (EEG) microstates are brain states with quasi-stable scalp topography. Whether such states extend to the body level, that is, the peripheral autonomic nerves, remains unknown. We hypothesized that microstates extend at the brain-heart axis level as a functional state of the central autonomic network. Thus, we combined the EEG and heartbeat dynamics series to estimate the directional information transfer originating in the cortex targeting the sympathovagal and parasympathetic activity oscillations and vice versa for the afferent functional direction. Data were from two groups of participants: 36 healthy volunteers who were subjected to cognitive workload induced by mental arithmetic, and 26 participants who underwent physical stress induced by a cold pressure test. All participants were healthy at the time of the study. Based on statistical testing and goodness-of-fit evaluations, we demonstrated the existence of microstates of the functional brain-heart axis, with emphasis on the cerebral cortex, since the microstates are derived from EEG. Such nervous-system microstates are spatio-temporal quasi-stable states that exclusively refer to the efferent brain-to-heart direction. We demonstrated brain-heart microstates that could be associated with specific experimental conditions as well as brain-heart microstates that are non-specific to tasks.
... Given the RSN was found to effectively portray the individual MVC behaviors, we then quantified the RSN configurations, as well as their relationships with individual MVC [47,48]. And as expected, the RSN properties were indeed closely related to MVC within these two bands, clarifying the consistency of the RSN topology and property. ...
Current research in the field of neuroscience primarily focuses on the analysis of electroencephalogram (EEG) activities associated with movement within the central nervous system. However, there is a dearth of studies investigating the impact of prolonged individual strength training on the resting state of the brain. Therefore, it is crucial to examine the correlation between upper body grip strength and resting-state EEG networks. In this study, coherence analysis was utilized to construct resting-state EEG networks using the available datasets. A multiple linear regression model was established to examine the correlation between the brain network properties of individuals and their maximum voluntary contraction (MVC) during gripping tasks. The model was used to predict individual MVC. The beta and gamma frequency bands showed significant correlation between RSN connectivity and MVC (p < 0.05), particularly in left hemisphere frontoparietal and fronto-occipital connectivity. RSN properties were consistently correlated with MVC in both bands, with correlation coefficients greater than 0.60 (p < 0.01). Additionally, predicted MVC positively correlated with actual MVC, with a coefficient of 0.70 and root mean square error of 5.67 (p < 0.01). The results show that the resting-state EEG network is closely related to upper body grip strength, which can indirectly reflect an individual's muscle strength through the resting brain network.
... The proposed computational circuit mechanisms [37] have presented selective attention [15] as cortical excitability alterations by the thalamus [43] acting as a "spotlight, " which is postulated for the error-related brain state changes [44]. Here, the microstate approach for a brain state correlates of the response [73] to error has a crucial a priori assumption that only one spatial topography map entirely defines the relevant global state of the brain at each moment in time and the residuals are considered noise. ...
... For example, novices may lack error perception (e.g., lack of medial frontal cortex activation on errors [32]) that can disrupt their skill learning, which can be improved with non-invasive brain stimulation of the medial frontal cortex in conjunction with explicit error feedback in the medical simulator. Then, EEG topographies provide subject-specific correlates of motor control [73], where portable neuroimaging guided noninvasive brain stimulation may be feasible [99] to enforce beneficial scalp topographies to facilitate perception and action that together form a functional system. The two crucial attributes of the perception-action cycle are perceptual, and executive memory [28], and error sensitivity is postulated to depend on the memory of errors, i.e., the history of past consistent perceptual errors, e.g., error in depth prediction from a 2D view [75] or executive errors, e.g., "incorrect needle insertion" [2]. ...
Abstract Error-based learning is one of the basic skill acquisition mechanisms that can be modeled as a perception–action system and investigated based on brain–behavior analysis during skill training. Here, the error-related chain of mental processes is postulated to depend on the skill level leading to a difference in the contextual switching of the brain states on error commission. Therefore, the objective of this paper was to compare error-related brain states, measured with multi-modal portable brain imaging, between experts and novices during the Fundamentals of Laparoscopic Surgery (FLS) “suturing and intracorporeal knot-tying” task (FLS complex task)—the most difficult among the five psychomotor FLS tasks. The multi-modal portable brain imaging combined functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) for brain–behavior analysis in thirteen right-handed novice medical students and nine expert surgeons. The brain state changes were defined by quasi-stable EEG scalp topography (called microstates) changes using 32-channel EEG data acquired at 250 Hz. Six microstate prototypes were identified from the combined EEG data from experts and novices during the FLS complex task that explained 77.14% of the global variance. Analysis of variance (ANOVA) found that the proportion of the total time spent in different microstates during the 10-s error epoch was significantly affected by the skill level (p
... It is now a consensus that the brain at rest is not truly "at rest", and the spontaneous brain activity exhibits complex dynamic spatiotemporal configurations (Raichle et al., 2001;Fox and Greicius, 2010;Pirondini et al., 2017). Of note, spontaneous brain activity can predict behavioral performance, and intrinsic activity plays a basic and functional role in brain function (Spisak et al., 2020). ...
... Furthermore, motor scores are related to EEG features (e.g., functional connectivity) of spontaneous brain activity (Riahi et al., 2020;Hoshino et al., 2021). Given the tight link between microstates and brain networks of spontaneous brain activity, we speculated that microstate dynamics could reflect the motor capacity (e.g., lower/upper limb function) to some extent (Pirondini et al., 2017;Spisak et al., 2020;Zhang et al., 2021). However, the current microstate studies have mainly focused on the cognitive functions of the brain in health (Brechet et al., 2019;Pirondini et al., 2020;Zanesco et al., 2020) and diseases like schizophrenia (da Cruz et al., 2020) and Alzheimer (Musaeus et al., 2019b;Tait et al., 2020). ...
... An increase in the coverage and occurrence of microstate B was observed in certain cognitive tasks with direct visual input (Zappasodi et al., 2019), and an increased presence in microstate C and decreased presence in microstate D was observed in schizophrenia (da Cruz et al., 2020). Moreover, One microstate study demonstrated that spontaneous brain activity could encode detailed information about motor control (Pirondini et al., 2017). Given the complex relationships of microstate, cognition, and behavior, it is arbitrary to reduce microstates to specific functions with current knowledge. ...
The brain, as a complex dynamically distributed information processing system, involves the coordination of large-scale brain networks such as neural synchronization and fast brain state transitions, even at rest. However, the neural mechanisms underlying brain states and the impact of dysfunction following brain injury on brain dynamics remain poorly understood. To this end, we proposed a microstate-based method to explore the functional connectivity pattern associated with each microstate class. We capitalized on microstate features from eyes-closed resting-state EEG data to investigate whether microstate dynamics differ between subacute stroke patients (N = 31) and healthy populations (N = 23) and further examined the correlations between microstate features and behaviors. An important finding in this study was that each microstate class was associated with a distinct functional connectivity pattern, and it was highly consistent across different groups (including an independent dataset). Although the connectivity patterns were diminished in stroke patients, the skeleton of the patterns was retained to some extent. Nevertheless, stroke patients showed significant differences in most parameters of microstates A, B, and C compared to healthy controls. Notably, microstate C exhibited an opposite pattern of differences to microstates A and B. On the other hand, there were no significant differences in all microstate parameters for patients with left-sided vs. right-sided stroke, as well as patients before vs. after lower limb training. Moreover, support vector machine (SVM) models were developed using only microstate features and achieved moderate discrimination between patients and controls. Furthermore, significant negative correlations were observed between the microstate-wise functional connectivity and lower limb motor scores. Overall, these results suggest that the changes in microstate dynamics for stroke patients appear to be state-selective, compensatory, and related to brain dysfunction after stroke and subsequent functional reconfiguration. These findings offer new insights into understanding the neural mechanisms of microstates, uncovering stroke-related alterations in brain dynamics, and exploring new treatments for stroke patients.
... Here, the proposed computational circuit mechanisms (Gu et al., 2021) have presented selective attention (Crick, 1984) or excitability alterations by the thalamus (Hughes et al., 2004) acting as a "spotlight" that can be postulated for error-related cognitive control (Ide and Li, 2011). The microstate approach for a correlate of motor control (Pirondini et al., 2017) has a crucial a priori assumption that only one spatial map entirely defines the relevant global state of the brain at each moment in time, and the residuals are considered noise. ...
... Here, microstate 3 can be related to the attention reorientation (Britz et al., 2010) and medial frontal cortex activation related to error (Gehring and Fencsik, 2001) in the novice, while the microstate 4 can be associated with the activation of the left inferior parietal lobe (Numssen et al., 2021) since experts have the action semantics knowledge (van Elk, 2014). Therefore, EEG topographies provide subject-specific correlates of motor control (Pirondini et al., 2017), and portable neuroimaging guided non-invasive brain stimulation may be feasible to facilitate skill training (Walia et al., 2021a). Here, perception and action together form a functional system that adapts novice behavior during motor learning. ...
Fundamentals of Laparoscopic Surgery (FLS) is a training module designed to provide basic surgical skills. During skill training of the FLS "suturing and intracorporeal knot-tying" task – the most difficult among the five psychomotor FLS tasks, learning from errors is one of the basic principles of motor skill acquisition where appropriate contextual switching of the brain state on error is postulated. This study investigated changes in the brain state following an error event based on the fusion of simultaneously acquired functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) signals. Here, human error processing is postulated to differentiate experts from novices based on the differences in the error-related chain of mental processes. Thirteen right-handed novice medical students and nine expert surgeons participated in this study. Error-related microstate analysis was performed using 32-channel EEG data at a high temporal resolution. Six microstate prototypes were identified from combined EEG data from experts and novices during the FLS task. Analysis of variance (ANOVA) found that the proportion of the total time spent in different microstates during the 10 sec error epoch was significantly affected by the skill level (p<0.01), microstate type (p<0.01), and the interaction between the skill level and the microstate type (p<0.01). Then, the EEG band power (1-40Hz) related to slower oxyhemoglobin (HbO) changes were found using regularized temporally embedded Canonical Correlation Analysis of the fNIRS-EEG signals. The HbO signal from the fNIRS channel overlying ‘Frontal_Inf_Oper_L’, ‘Frontal_Mid_Orb_L’, ‘Postcentral_L’, ‘Temporal_Sup_L’, ‘Frontal_Mid_Orb_R’ cortical areas from Automatic Anatomical Labelling showed significant (p<0.05) difference between experts and novices in the 10-sec error epoch. Here, the frontal/prefrontal cortical areas are postulated to be related to the perception and the activation of the primary somatosensory cortex at the postcentral cortical area is hypothesized to be related to the action underpinning perception-action coupling model for the error-related chain of mental processes. Therefore, our study highlighted the importance of error-related brain states from portable brain imaging when comparing complex surgical skill levels.
... The proposed computational circuit mechanisms (Gu et al., 2021) have presented selective attention (Crick, 1984) or cortical excitability alterations by the thalamus (Hughes et al., 2004) acting as a "spotlight," which is postulated for the errorrelated cognitive control (Ide and Li, 2011). Here, the microstate approach for a brain state correlates of motor control (Pirondini et al., 2017) has a crucial a priori assumption that only one spatial topography map entirely de nes the relevant global state of the brain at each moment in time, and the residuals are considered noise. ...
... For example, novices may lack error perception (e.g., lack of medial frontal cortex activation on errors (Gehring and Fencsik, 2001)) that can disrupt their skill learning, which can be improved with non-invasive brain stimulation of the medial frontal cortex in conjunction with explicit error feedback in the medical simulator. Then, EEG topographies provide subject-speci c correlates of motor control (Pirondini et al., 2017), where portable neuroimaging guided non-invasive brain stimulation may be feasible (Walia et al., 2021) to enforce bene cial scalp topographies to facilitate perception and action that together form a functional system. The two crucial attributes of the perception-action cycle are perceptual and executive memory (Fuster, 2004), and error sensitivity is postulated to depend on the memory of errors, i.e., the history of past consistent perceptual errors, e.g., error in depth prediction from a 2D view (Popa and Ebner, 2019) or executive errors, e.g., "incorrect needle insertion" (Albert et al., 2021). ...
Fundamentals of Laparoscopic Surgery (FLS) is a standard education and training module with a set of basic surgical skills. During surgical skill acquisition, novices need to learn from errors due to perturbations in their performance which is one of the basic principles of motor skill acquisition. This study on thirteen healthy novice medical students and nine expert surgeons aimed to capture the brain state during error epochs using multimodal brain imaging by combining functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG). We performed error-related microstate analysis in the latent space that was found using regularized temporally embedded Canonical Correlation Analysis from fNIRS-EEG recordings during the performance of FLS "suturing and intracorporeal knot-tying" task – the most difficult among the five psychomotor FLS tasks. We found from two-way analysis of variance (ANOVA) with factors, skill level (expert, novice), and microstate type (1-6) that the proportion of the total time spent in microstates in the error epochs was significantly affected by the skill level (p<0.01), microstate type (p<0.01), and the interaction between the skill level and the microstate type (p<0.01). Therefore, our study highlighted the relevance of portable brain imaging to capture error behavior when comparing the skill level during a complex surgical task.
... The proposed computational circuit mechanisms (Gu et al., 2021) have presented selective attention (Crick, 1984) or cortical excitability alterations by the thalamus (Hughes et al., 2004) acting as a "spotlight," which is postulated for the errorrelated cognitive control (Ide and Li, 2011). Here, the microstate approach for a brain state correlates of motor control (Pirondini et al., 2017) has a crucial a priori assumption that only one spatial topography map entirely de nes the relevant global state of the brain at each moment in time, and the residuals are considered noise. ...
... For example, novices may lack error perception (e.g., lack of medial frontal cortex activation on errors (Gehring and Fencsik, 2001)) that can disrupt their skill learning, which can be improved with non-invasive brain stimulation of the medial frontal cortex in conjunction with explicit error feedback in the medical simulator. Then, EEG topographies provide subject-speci c correlates of motor control (Pirondini et al., 2017), where portable neuroimaging guided non-invasive brain stimulation may be feasible (Walia et al., 2021) to enforce bene cial scalp topographies to facilitate perception and action that together form a functional system. The two crucial attributes of the perception-action cycle are perceptual and executive memory (Fuster, 2004), and error sensitivity is postulated to depend on the memory of errors, i.e., the history of past consistent perceptual errors, e.g., error in depth prediction from a 2D view (Popa and Ebner, 2019) or executive errors, e.g., "incorrect needle insertion" (Albert et al., 2021). ...
The Fundamentals of Laparoscopic Surgery (FLS) training module is designed to provide essential surgical skills. During skill training of the FLS "suturing and intracorporeal knot-tying" task (FLS complex task) – the most difficult among the five psychomotor FLS tasks, the error-related chain of mental processes is postulated to depend on the skill level leading to a difference in the contextual switching of the brain states on error commission between experts and novices. So, this study investigated changes in the brain states using simultaneously acquired functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) signals during FLS complex task and following motor errors in thirteen right-handed novice medical students and nine expert surgeons. A brain state analysis of the quasi-stable EEG scalp topography (called microstates) changes was performed using 32-channel EEG data acquired at 250Hz (high temporal resolution). Six microstate prototypes were identified from the combined EEG data from experts and novices during the FLS complex task that explained 77.14% of the global variance. Analysis of variance (ANOVA) found that the proportion of the total time spent in different microstates during the 10-sec error epoch was significantly affected by the skill level (p < 0.01), the microstate type (p < 0.01), and the interaction between the skill level and the microstate type (p < 0.01). Brain activation based on the slower oxyhemoglobin (HbO) changes corresponding to the EEG band power (1-40Hz) changes were found using the regularized temporally embedded Canonical Correlation Analysis of the simultaneously acquired fNIRS-EEG signals. We found that the HbO signal from the fNIRS channels overlying left inferior frontal gyrus – opercular part, left superior frontal gyrus – medial orbital, left postcentral gyrus, left superior temporal gyrus, right superior frontal gyrus – medial orbital cortical areas showed significant (p < 0.05) difference between experts and novices in the 10-sec error epoch. Here, the left superior and inferior frontal gyrus areas are postulated to be related to the error perception, while the activation of the primary somatosensory cortex at the postcentral cortical area can be associated with the error-related corrective action underpinning the perception-action coupling model.
