EEG Source Space Research Articles

Rapid reconfiguration of cortical networks after repeated exposure to visual-vestibular conflicts

Visual-vestibular conflicts can induce motion sickness and further postural instability. Visual-vestibular habituation is recommended to reduce the symptoms of motion sickness and improve postural stability with an altered multisensory reweighting progress. However, it is unclear how the human brain reweights multisensory information after repeated exposure to visual-vestibular conflicts. Therefore, we synchronized a rotating platform and a virtual scene to present visual-vestibular congruent (natural visual stimulation) and incongruent (conflicted visual stimulation) conditions and collected EEG and center of pressure (COP) data. We constructed the effective brain connectivity of region of interest (ROI) derived from source-space EEG in theta-band activity, and quantified the postural stability and the inflow and outflow of each ROI. We found repeated exposure to congruent and incongruent conditions both decreased COP path length and increased COP complexity. Besides, we found that repeated exposure to the incongruent environment decreased the inflow into visual cortex, suggesting the brain down-weighted the less reliable visual information for postural stability. In contrast, repeated exposure to the congruent environment increased the inflow into posterior parietal cortex and the outflow from visual cortex and S1, suggesting an increase in efficiency of multisensory integration. We concluded that repeated exposure to congruent and incongruent conditions both improved postural stability with different multisensory reweighting patterns as revealed by different dynamic changes of brain networks.

Uncovering Brain Network Insights for Prognosis in Disorders of Consciousness: EEG Source Space Analysis and Brain Dynamics.

Accurate prognostic prediction in patients with disorders of consciousness (DOC) is a core clinical concern and a formidable challenge in neuroscience. Resting-state EEG has shown promise in identifying electrophysiological prognostic markers and may be easily deployed at the bedside. However, the lack of brain dynamic modeling and the spatial mixture of signals in scalp EEG have constrained our exploration of biomarkers and comprehension of the mechanisms underlying consciousness recovery. Here, we introduce EEG source space analysis and brain dynamics to investigate the brain networks of patients with DOC (n = 178) with different outcomes (six-month follow-up), followed by graph theory and high-order topological analysis to explore the relationship between network structure and prognosis, and finally assess the importance of features. We show that a positive prognosis is associated with large-scale lower levels of low-frequency hypersynchrony. Moreover, we provide evidence that this pattern is driven not by all brain states but only by specific states. Analyses reveal that the positive prognosis is attributed to the network retaining lower segregation, higher integration, and stronger stability compared to the negative prognosis. Furthermore, our results highlight the importance of brain networks derived from brain dynamics in prognosis. The prognosis models based on clinical and neural features can achieve acceptable and even excellent performance under different outcome definitions (AUC = 0.714-0.893). Overall, our study offers new perspectives for the identification of prognostic biomarkers and provides avenues for profound insights into the mechanisms underlying consciousness improvement or recovery.

Network-level mechanisms underlying effects of transcranial direct current stimulation (tDCS) on visuomotor learning in schizophrenia

Motor learning is a fundamental skill to our daily lives. Dysfunction in motor performance in schizophrenia (Sz) has been associated with poor social and functional outcomes. Transcranial direct current stimulation (tDCS), a non-invasive electrical brain stimulation approach, can influence underlying brain function with potential for improving motor learning in Sz. We used a well-established Serial Reaction Time Task (SRTT) to study motor learning, in combination with simultaneous tDCS and EEG recording, to investigate mechanisms of motor and procedural learning deficits in Sz, and to develop refined non-invasive brain stimulation approaches to improve neurocognitive dysfunction. We recruited 27 individuals with Sz and 21 healthy controls (HC). Individuals performed the SRTT task as they received sham and active tDCS with simultaneous EEG recording. Reaction time (RT), neuropsychological, and measures of global functioning were assessed. SRTT performance was significantly impaired in Sz and showed significant correlations with motor-related and working memory measures as well as global function. Source-space time-frequency decomposition of EEG showed beta-band coherence across supplementary-motor, primary-motor and visual cortex forming a network involved in SRTT performance. Motor-cathodal and visual-cathodal stimulations resulted in significant modulation in coherence particularly across the motor-visual nodes of the network accompanied by significant improvement in motor learning in both controls and patients. Here, we confirm earlier reports of SRTT impairment in Sz and demonstrate significant reversal of the deficits with tDCS. The findings support continued development of tDCS for enhancement of plasticity-based interventions in Sz, as well as source-space EEG analytic approaches for evaluating underlying neural mechanisms.

Harmonized multi-metric and multi-centric assessment of EEG source space connectivity for dementia characterization.

Harmonization protocols that address batch effects and cross-site methodological differences in multi-center studies are critical for strengthening electroencephalography (EEG) signatures of functional connectivity (FC) as potential dementia biomarkers. We implemented an automatic processing pipeline incorporating electrode layout integrations, patient-control normalizations, and multi-metric EEG source space connectomics analyses. Spline interpolations of EEG signals onto a head mesh model with 6067 virtual electrodes resulted in an effective method for integrating electrode layouts. Z-score transformations of EEG time series resulted in source space connectivity matrices with high bilateral symmetry, reinforced long-range connections, and diminished short-range functional interactions. A composite FC metric allowed for accurate multicentric classifications of Alzheimer's disease and behavioral variant frontotemporal dementia. Harmonized multi-metric analysis of EEG source space connectivity can address data heterogeneities in multi-centric studies, representing a powerful tool for accurately characterizing dementia.

Brain network dynamics in the alpha band during a complex postural control task

Objective. To decipher brain network dynamic remodeling from electroencephalography (EEG) during a complex postural control (PC) task combining virtual reality and a moving platform. Approach. EEG (64 electrodes) data from 158 healthy subjects were acquired. The experiment is divided into several phases, and visual and motor stimulation is applied progressively. We combined advanced source-space EEG networks with clustering algorithms to decipher the brain networks states (BNSs) that occurred during the task. Main results. The results show that BNS distribution describes the different phases of the experiment with specific transitions between visual, motor, salience, and default mode networks coherently. We also showed that age is a key factor that affects the dynamic transition of BNSs in a healthy cohort. Significance. This study validates an innovative approach, based on a robust methodology and a consequent cohort, to quantify the brain networks dynamics in the BioVRSea paradigm. This work is an important step toward a quantitative evaluation of brain activities during PC and could lay the foundation for developing brain-based biomarkers of PC-related disorders.

Source space connectomics of neurodegeneration: One-metric approach does not fit all

Brain functional connectivity in dementia has been assessed with dissimilar EEG connectivity metrics and estimation procedures, thereby increasing results' heterogeneity. In this scenario, joint analyses integrating information from different metrics may allow for a more comprehensive characterization of brain functional interactions in different dementia subtypes. To test this hypothesis, resting-state electroencephalogram (rsEEG) was recorded in individuals with Alzheimer's Disease (AD), behavioral variant frontotemporal dementia (bvFTD), and healthy controls (HCs). Whole-brain functional connectivity was estimated in the EEG source space using 101 different types of functional connectivity, capturing linear and nonlinear interactions in both time and frequency-domains. Multivariate machine learning and progressive feature elimination was run to discriminate AD from HCs, and bvFTD from HCs, based on joint analyses of i) EEG frequency bands, ii) complementary frequency-domain metrics (e.g., instantaneous, lagged, and total connectivity), and iii) time-domain metrics with different linearity assumption (e.g., Pearson correlation coefficient and mutual information). <10% of all possible connections were responsible for the differences between patients and controls, and atypical connectivity was never captured by >1/4 of all possible connectivity measures. Joint analyses revealed patterns of hypoconnectivity (patients<HCs) involving convergent temporo-parieto-occipital regions in AD, and fronto-temporo-parietal areas in bvFTD. Hyperconnectivity (patients>HCs) in both groups was mainly identified in frontotemporal regions. These atypicalities were differently captured by frequency- and time-domain connectivity metrics, in a bandwidth-specific fashion. The multi-metric representation of source space whole-brain functional connectivity evidenced the inadequacy of single-metric approaches, and resulted in a valid alternative for the selection problem in EEG connectivity. These joint analyses reveal patterns of brain functional interdependence that are overlooked with single metrics approaches, contributing to a more reliable and interpretable description of atypical functional connectivity in neurodegeneration.

The temporal dynamics of Large-Scale brain network changes in disorders of consciousness: A Microstate-Based study.

The resting-state brain is composed of several discrete networks, which remain stable for 10-100 ms. These functional microstates are considered the building blocks of spontaneous consciousness. Electroencephalography (EEG) microstate analysis may provide insight into the altered brain dynamics underlying consciousness recovery in patients with disorders of consciousness (DOC). We aimed to analyze microstates in the resting-state EEG source space in patients with DOC, the relationship between state-specific features and consciousness levels, and the corresponding patterns of microstates and functional networks. We obtained resting-state EEG data from 84 patients with DOC (27 in a minimally conscious state [MCS] and 57 in a vegetative state [VS] or with unresponsive wakefulness syndrome). We conducted a microstate analysis of the resting-state (EEG) source space and developed a state-transition analysis protocol for patients with DOC. We identified seven microstates with distinct spatial distributions of cortical activation. Multivariate pattern analyses revealed that different functional connectivity patterns were associated with source-level microstates. There were significant differences in the microstate properties, including spatial activation patterns, temporal dynamics, state shifts, and connectivity construction, between the MCS and VS groups. Our findings suggest that consciousness depends on complex dynamics within the brain and may originate from the anterior cortex.

Altered functional and directed connectivity in propofol-induced loss of consciousness: A source-space resting-state EEG study

ObjectiveGeneral anesthesia might disrupt neuronal network communications measured by functional connectivity (FC; undirected connectivity) and directional information flow (directed connectivity). We sought to characterize the state-dependent effects of propofol on cortico-cortical undirected and directed FC. MethodsWe collected 256-channel high-density EEGs from 14 patients undergoing surgery while awake (AWA) or in propofol-induced moderate sedation (SED) or loss of consciousness (LOC) states. Using source-space EEG, we estimated neuronal oscillatory activity for 68 cortical regions of interest. FC was analyzed using the weighted phase lag index. Directed connectivity was computed using directed phase transfer entropy (dPTE) as a measure of information flow in the bilateral prefrontal, frontal, parietal, and occipital areas. ResultsFC strength evidently reduced during LOC compared with those during the AWA and SED states. The dPTE analysis showed significant propofol-induced changes in directed connectivity. In the alpha band, the prefrontal-to-frontal information flow was significantly stronger in the AWA than in the SED (p = 0.033) and LOC states (p = 0.033). The parietal-to-frontal dPTE was significantly weaker during LOC than during the AWA (p = 0.033) and SED states (p = 0.007). Finally, a loss of occipital-to-frontal connectivity occurred during LOC but not the AWA state (p = 0.001). In the beta band, the dominant occipital-to-frontal direction of information flow in the AWA state was gradually converted to a frontal-to-occipital direction during LOC. ConclusionsPropofol-induced unconsciousness is marked by a decrease in FC and posterior-to-anterior (feedforward) directed connectivity, which may be useful as a measure to discriminate different conscious states caused by propofol administration. SignificanceThe study demonstrates that propofol produces state-dependent effects on cortico-cortical undirected and directed FC, supporting the idea that propofol induces loss of consciousness may through disrupting network interactions and cortical coordination.

A Novel Approach to Estimating the Cortical Sources of Sleep Spindles Using Simultaneous EEG/MEG.

Sleep spindles, defining oscillations of stage II non-rapid eye movement sleep (N2), mediate sleep-dependent memory consolidation. Spindles are disrupted in several neurodevelopmental, neuropsychiatric, and neurodegenerative disorders characterized by cognitive impairment. Increasing spindles can improve memory suggesting spindles as a promising physiological target for the development of cognitive enhancing therapies. This effort would benefit from more comprehensive and spatially precise methods to characterize spindles. Spindles, as detected with electroencephalography (EEG), are often widespread across electrodes. Available evidence, however, suggests that they act locally to enhance cortical plasticity in the service of memory consolidation. Here, we present a novel method to enhance the spatial specificity of cortical source estimates of spindles using combined EEG and magnetoencephalography (MEG) data constrained to the cortex based on structural MRI. To illustrate this method, we used simultaneous EEG and MEG recordings from 25 healthy adults during a daytime nap. We first validated source space spindle detection using only EEG data by demonstrating strong temporal correspondence with sensor space EEG spindle detection (gold standard). We then demonstrated that spindle source estimates using EEG alone, MEG alone and combined EEG/MEG are stable across nap sessions. EEG detected more source space spindles than MEG and each modality detected non-overlapping spindles that had distinct cortical source distributions. Source space EEG was more sensitive to spindles in medial frontal and lateral prefrontal cortex, while MEG was more sensitive to spindles in somatosensory and motor cortices. By combining EEG and MEG data this method leverages the differential spatial sensitivities of the two modalities to obtain a more comprehensive and spatially specific source estimation of spindles than possible with either modality alone.

Functional Connectivity Analysis in EEG Source Space During Deception

Purpose: Deception is described as a deliberate endeavor to deceive others. In this research, the main purpose is to survey the brain network between deception and telling the truth.&#x0D; Materials and Methods: Electroencephalography (EEG) data were collected from 17 subjects during a deception task in which the subjects had to classify the target stimuli deceptively while responding truthfully to other stimuli (non-targets). Functional Connectivity (FC) analysis was carried out in source space in order to attenuate the volume conduction effect. The coherence criterion was applied for calculating FC.&#x0D; Results: The results revealed that deception is associated with significantly greater connectivity between distant regions, including frontal-occipital and frontal-parietal connectivity. In addition, Anterior Cingulate Cortex (ACC) demonstrated significantly greater connectivity with regions of the frontal and occipital lobes. Besides, deception was accompanied by high number of strong connectivity between the left parietal and frontal lobes.&#x0D; Conclusions: The findings demonstrated that the FC studies in source space can strikingly assist in the investigation of deception.

Frontal-occipital network alterations while viewing 2D &amp; 3D movies: a source-level EEG and graph theory approach

Abstract Many researchers have measured the differences in electroencephalography (EEG) while viewing 2D and 3D movies to uncover the neuromechanism underlying distinct viewing experiences. Using whole-brain network analyses of scalp EEG, our previous study reported that beta and gamma bands presented higher global efficiencies while viewing 3D movies. However, scalp EEG is influenced by volume conduction, not allowing inference from a neuroanatomy perspective; thus, source reconstruction techniques are recommended. This paper is the first to measure the differences in the frontal-occipital networks in EEG source space during 2D and 3D movie viewing. EEG recordings from 40 subjects were performed during 2D and 3D movie viewing. We constructed frontal-occipital networks of alpha, beta, and gamma bands in EEG source space and analyzed network efficiencies. We found that the beta band exhibited higher global efficiency in 3D movie viewing than in 2D movie viewing; however, the alpha global efficiency was not statistically significant. In addition, a support vector machine (SVM) classifier, taking functional connectivities as classification features, was built to identify whether the frontal-occipital networks contain patterns that could distinguish 2D and 3D movie viewing. Using the 6 most important functional connectivity features of the beta band, we obtained the best accuracy of 0.933. Our findings shed light on uncovering the neuromechanism underlying distinct experiences while viewing 2D and 3D movies.

Modeling the Hemodynamic Response Function Using EEG-fMRI Data During Eyes-Open Resting-State Conditions and Motor Task Execution

Being able to accurately quantify the hemodynamic response function (HRF) that links the blood oxygen level dependent functional magnetic resonance imaging (BOLD-fMRI) signal to the underlying neural activity is important both for elucidating neurovascular coupling mechanisms and improving the accuracy of fMRI-based functional connectivity analyses. In particular, HRF estimation using BOLD-fMRI is challenging particularly in the case of resting-state data, due to the absence of information about the underlying neuronal dynamics. To this end, using simultaneously recorded electroencephalography (EEG) and fMRI data is a promising approach, as EEG provides a more direct measure of neural activations. In the present work, we employ simultaneous EEG-fMRI to investigate the regional characteristics of the HRF using measurements acquired during resting conditions. We propose a novel methodological approach based on combining distributed EEG source space reconstruction, which improves the spatial resolution of HRF estimation and using block-structured linear and nonlinear models, which enables us to simultaneously obtain HRF estimates and the contribution of different EEG frequency bands. Our results suggest that the dynamics of the resting-state BOLD signal can be sufficiently described using linear models and that the contribution of each band is region specific. Specifically, it was found that sensory-motor cortices exhibit positive HRF shapes, whereas the lateral occipital cortex and areas in the parietal cortex, such as the inferior and superior parietal lobule exhibit negative HRF shapes. To validate the proposed method, we repeated the analysis using simultaneous EEG-fMRI measurements acquired during execution of a unimanual hand-grip task. Our results reveal significant associations between BOLD signal variations and electrophysiological power fluctuations in the ipsilateral primary motor cortex, particularly for the EEG beta band, in agreement with previous studies in the literature.

Spontaneous transient brain states in EEG source space in disorders of consciousness

Spontaneous transient states were recently identified by functional magnetic resonance imaging and magnetoencephalography in healthy subjects. They organize and coordinate neural activity in brain networks. How spontaneous transient states are altered in abnormal brain conditions is unknown. Here, we conducted a transient state analysis on resting-state electroencephalography (EEG) source space and developed a state transfer analysis to patients with disorders of consciousness (DOC). They uncovered different neural coordination patterns, including spatial power patterns, temporal dynamics, spectral shifts, and connectivity construction varies at potentially very fast (millisecond) time scales, in groups with different consciousness levels: healthy subjects, patients in minimally conscious state (MCS), and patients with vegetative state/unresponsive wakefulness syndrome (VS/UWS). Machine learning based on transient state features reveal high classification accuracy between MCS and VS/UWS. This study developed methodology of transient states analysis on EEG source space and abnormal brain conditions. Findings correlate spontaneous transient states with human consciousness and suggest potential roles of transient states in brain disease assessment.

Functional connectivity of EEG is subject-specific, associated with phenotype, and different from fMRI

A variety of psychiatric, behavioral and cognitive phenotypes have been linked to brain ‘’functional connectivity’’ -- the pattern of correlation observed between different brain regions. Most commonly assessed using functional magnetic resonance imaging (fMRI), here, we investigate the connectivity-phenotype associations with functional connectivity measured with electroencephalography (EEG), using phase-coupling. We analyzed data from the publicly available Healthy Brain Network Biobank. This database compiles a growing sample of children and adolescents, currently encompassing 1657 individuals. Among a variety of assessment instruments we focus on ten phenotypic and additional demographic measures that capture most of the variance in this sample. The largest effect sizes are found for age and sex for both fMRI and EEG. We replicate previous findings of an association of Intelligence Quotient (IQ) and Attention Deficit Hyperactivity Disorder (ADHD) with the pattern of fMRI functional connectivity. We also find an association with socioeconomic status, anxiety and the Child Behavior Checklist Score. For EEG we find a significant connectivity-phenotype relationship with IQ. The actual spatial patterns of functional connectivity are quite different between fMRI and source-space EEG. However, within EEG we observe clusters of functional connectivity that are consistent across frequency bands. Additionally we analyzed reproducibility of functional connectivity. We compare connectivity obtained with different tasks, including resting state, a video and a visual flicker task. For both EEG and fMRI the variation between tasks was smaller than the variability observed between subjects. We also found an increase of reliability with increasing frequency of the EEG, and increased sampling duration. We conclude that, while the patterns of functional connectivity are distinct between fMRI and phase-coupling of EEG, they are nonetheless similar in their robustness to the task, and similar in that idiosyncratic patterns of connectivity predict individual phenotypes.

Resting-state networks in adolescents with poor behavior regulation. An analysis of effective cortical connectivity in eeg source space

Resting-state networks in adolescents with poor behavior regulation. An analysis of effective cortical connectivity in eeg source space

Age-related Trends in Functional Organization of Cortical Parts of Regulatory Brain Systems in Adolescents: an Analysis of Resting-State Networks in the EEG Source Space

Age-related trends in the strength of functional and effective connectivity between key cortical structures that belong to the three most important resting-state neural networks: the default mode network (DMN), the key event detection network (salience network, SN) and the central executive network (CEN) were investigated in adolescents of both sexes. The study involved 34 adolescents of the younger age group: 11–13 years old (12.61 ± 0.75 years, 13 girls and 21 boys) and 54 adolescents of the older group: 14–16 years old (15.00 ± 0.75 years, 20 girls and 34 boys). Frequency-specific estimates of the strength of functional and effective connectivity between the nodes of DMN, SN and CEN were computed in six frequency bands θ (4–7 Hz), α1 (7.5–9.5 Hz), α2 (10–13 Hz), β1 (14–18 Hz), β2 (20–27 Hz) and γ (30–40 Hz) using the vector autoregressive modeling of cortical sources of EEG registered in the resting condition. The analysis of functional connectivity revealed age differences in the θ frequency range, in which the strength of connections between the nodes of all networks in the younger age group was higher than in the older group. Besides, sex-related differences were found in the beta-2 and gamma ranges: the connections between the nodes of DMN were stronger for girls than for boys. The analysis of effective connectivity (directed influences) revealed a significant effect of age in all frequency ranges for the CEN and DMN resting-state networks; for SN, this effect was significant in all but the beta-2 and gamma ranges. The most pronounced age-related changes were found for bottom-up connections directed from more caudal to more frontal areas in CEN and DMN, and in the younger age group, the strength of effective connections was greater than in the older one. The effect of sex on the strength of effective connections was limited mainly to the younger group and manifested itself in stronger DMN and CEN connectivity in girls (as compared with boys) .

Decoding Motor Imagery through Common Spatial Pattern Filters at the EEG Source Space.

Brain-Computer Interface (BCI) is a rapidly developing technology that aims to support individuals suffering from various disabilities and, ultimately, improve everyday quality of life. Sensorimotor rhythm-based BCIs have demonstrated remarkable results in controlling virtual or physical external devices but they still face a number of challenges and limitations. Main challenges include multiple degrees-of-freedom control, accuracy, and robustness. In this work, we develop a multiclass BCI decoding algorithm that uses electroencephalography (EEG) source imaging, a technique that maps scalp potentials to cortical activations, to compensate for low spatial resolution of EEG. Spatial features were extracted using Common Spatial Pattern (CSP) filters in the cortical source space from a number of selected Regions of Interest (ROIs). Classification was performed through an ensemble model, based on individual ROI classification models. The evaluation was performed on the BCI Competition IV dataset 2a, which features 4 motor imagery classes from 9 participants. Our results revealed a mean accuracy increase of 5.6% with respect to the conventional application method of CSP on sensors. Neuroanatomical constraints and prior neurophysiological knowledge play an important role in developing source space-based BCI algorithms. Feature selection and classifier characteristics of our implementation will be explored to raise performance to current state-of-the-art.

Identifying auditory cortex encoding abnormalities in schizophrenia: The utility of low-frequency versus 40 Hz steady-state measures.

Magnetoencephalography (MEG) and EEG have identified poststimulus low frequency and 40 Hz steady-state auditory encoding abnormalities in schizophrenia (SZ). Negative findings have also appeared. To identify factors contributing to these inconsistencies, healthy control (HC) and SZ group differences were examined in MEG and EEG source space and EEG sensor space, with better group differentiation hypothesized for source than sensor measures given greater predictive utility for source measures. Fifty-five HC and 41 chronic SZ were presented 500 Hz sinusoidal stimuli modulated at 40 Hz during simultaneous whole-head MEG and EEG. MEG and EEG source models using left and right superior temporal gyrus (STG) dipoles estimated trial-to-trial phase similarity and percent change from prestimulus baseline. Group differences in poststimulus low-frequency activity and 40 Hz steady-state response were evaluated. Several EEG sensor analysis strategies were also examined. Poststimulus low-frequency group differences were observed across all methods. Given an age-related decrease in left STG 40 Hz steady-state activity in HC (HC > SZ), 40 Hz steady-state group differences were evident only in younger participants' source measures. Findings thus indicated that optimal data collection and analysis methods depend on the auditory encoding measure of interest. In addition, whereas results indicated that HC and SZ auditory encoding low-frequency group differences are generally comparable across modality and analysis strategy (and thus not dependent on obtaining construct-valid measures of left and right auditory cortex activity), 40 Hz steady-state group-difference findings are much more dependent on analysis strategy, with 40 Hz steady-state source-space findings providing the best group differentiation.

Functional connectivity analysis in EEG source space: The choice of method.

Functional connectivity (FC) is among the most informative features derived from EEG. However, the most straightforward sensor-space analysis of FC is unreliable owing to volume conductance effects. An alternative—source-space analysis of FC—is optimal for high- and mid-density EEG (hdEEG, mdEEG); however, it is questionable for widely used low-density EEG (ldEEG) because of inadequate surface sampling. Here, using simulations, we investigate the performance of the two source FC methods, the inverse-based source FC (ISFC) and the cortical partial coherence (CPC). To examine the effects of localization errors of the inverse method on the FC estimation, we simulated an oscillatory source with varying locations and SNRs. To compare the FC estimations by the two methods, we simulated two synchronized sources with varying between-source distance and SNR. The simulations were implemented for hdEEG, mdEEG, and ldEEG. We showed that the performance of both methods deteriorates for deep sources owing to their inaccurate localization and smoothing. The accuracy of both methods improves with the increasing between-source distance. The best ISFC performance was achieved using hd/mdEEG, while the best CPC performance was observed with ldEEG. In conclusion, with hdEEG, ISFC outperforms CPC and therefore should be the preferred method. In the studies based on ldEEG, the CPC is a method of choice.

EEG source space analysis of the supervised factor analytic approach for the classification of multi-directional arm movement

Objective. In electroencephalography (EEG)-based brain–computer interface (BCI) systems for motor control tasks the conventional practice is to decode motor intentions by using scalp EEG. However, scalp EEG only reveals certain limited information about the complex tasks of movement with a higher degree of freedom. Therefore, our objective is to investigate the effectiveness of source-space EEG in extracting relevant features that discriminate arm movement in multiple directions. Approach. We have proposed a novel feature extraction algorithm based on supervised factor analysis that models the data from source-space EEG. To this end, we computed the features from the source dipoles confined to Brodmann areas of interest (BA4a, BA4p and BA6). Further, we embedded class-wise labels of multi-direction (multi-class) source-space EEG to an unsupervised factor analysis to make it into a supervised learning method. Main Results. Our approach provided an average decoding accuracy of 71% for the classification of hand movement in four orthogonal directions, that is significantly higher (>10%) than the classification accuracy obtained using state-of-the-art spatial pattern features in sensor space. Also, the group analysis on the spectral characteristics of source-space EEG indicates that the slow cortical potentials from a set of cortical source dipoles reveal discriminative information regarding the movement parameter, direction. Significance. This study presents evidence that low-frequency components in the source space play an important role in movement kinematics, and thus it may lead to new strategies for BCI-based neurorehabilitation.