Reliability of EEG microstate analysis at different electrode densities during propofol-induced transitions of brain states

A Novel Event-Related Neural Activity Extraction Technique by Fusing Neural Oscillation and Neural Synchronization

Previous research has proposed a number of techniques for the extraction of single-trial event-related neural activity (ERNA). However, these single-trial extraction techniques did not simultaneously consider about neural oscillation and neural synchronization, thereby creating an opportunity to optimize the single-trial extraction technique. In this study, based on the neural basis of electroencephalography (EEG), a novel single-trial extraction technique, which fuses neural oscillation and neural synchronization, was proposed. The neural activity extracted by this technique was identified as a novel type of ERNA and defined as connectivity-related neural activity (CRNA). The CRNA from the single-trial EEG was extracted through the utilisation of independent component analysis (ICA), non-negative matrix factorization (NNMF), and the alternating direction method of multipliers (ADMM). Furthermore, the performance of CRNA was evaluated using two key measures : signal-to-noise ratio (SNR) and Pearson's correlation coefficient (RHO). To investigate the effect of electrode density on CRNA performance, we compared five standard electrode configurations (including 9, 16, 32, 64, and 128 channel configurations). It was observed that there was a notable enhancement in the SNR of CRNA with an increase in electrode density. Finally, the comparative analysis demonstrated that the SNR and RHO of CRNA exhibited superior performance compared to several existing single-trial extraction techniques, including the conventional wavelet-based approach, the translation-invariant (TI) wavelet-based approach, the NZT, and the Generalized Subspace Approach (GSA). The proposed method facilitated the optimization of the single-trial extraction technique.

Pediatric sleep electrophysiology: Using polysomnography in developmental cognitive neuroscience

Research suggests a bidirectional relationship between brain and cognitive development and sleep in early childhood. Polysomnography is essential for the investigation of the mechanisms underlying sleep's role in brain and cognitive development. This paper outlines methods for integrating measures of sleep and sleep physiology into cognitive developmental neuroscience research. There are various options when choosing a polysomnography system depending on the research question. We offer considerations such as application time, recording time, montage density and analysis options, and cost. We also review suggestions for modifying procedures with developmental populations to support high quality polysomnography data collection. We hope that this overview will facilitate more developmental cognitive neuroscience studies of sleep to advance our understanding of early brain and cognitive development.

Frequency- and state-dependent dynamics of EEG microstates during propofol anesthesia

Electroencephalography microstate analysis has emerged as a powerful tool for investigating brain dynamics during anesthesia-induced unconsciousness. However, existing studies typically analyze EEG signals across broad frequency bands, leaving the frequency-specific temporal characteristics of microstates poorly understood. In this study, we investigated frequency-specific EEG microstate features in the delta (0.5-4 Hz) and EEG-without-delta (4-30 Hz) frequency bands during propofol anesthesia. Sixty-channel EEG recordings were collected from 18 healthy male participants during wakefulness and propofol-induced unconsciousness. Microstate analysis was conducted separately for delta and EEG-without-delta frequency bands and microstate features were compared across frequency bands and conscious states. Our results revealed eight consistent microstate classes (MS1-MS8) with high topographic similarity across frequency bands, while global explained variance (GEV), mean duration (MeanDur), occurrence (Occ), and coverage (Cov) exhibited significant frequency- and state-dependent variations during propofol anesthesia. In the delta band, propofol-induced unconsciousness was associated with significantly longer MeanDur for microstate classes of MS4, MS5, and MS6 (p < 0.05). In the EEG-without-delta band, GEV, Cov, and Occ significantly increased for MS1 and MS3 (p < 0.01) and decreased for MS2 and MS4 (p < 0.05) during unconsciousness. Notably, microstate features in the EEG-without-delta band showed better sensitivity for discriminating conscious states, achieving a classification accuracy of 0.944. These findings emphasize the importance of frequency-specific microstate analysis in unraveling the neural dynamics of anesthesia-induced unconsciousness and highlight its potential clinical applications for improving anesthesia depth monitoring.

Current status of EEG microstate in psychiatric disorders: a systematic review and meta-analysis

BACKGROUND

EEG microstates are promising biomarkers for psychiatric conditions, though prior meta-analyses mainly focused on schizophrenia and mood disorders. This study expands the analysis to a wider range of mental disorders, examining microstate variations across the psychosis and mood spectra and assessing medication effects on schizophrenia.

METHODS

Following PRISMA guidelines, we conducted a comprehensive literature search, identifying 24 studies meeting inclusion criteria. Analyses were performed across two psychiatric subgroups: psychotic disorders and mood disorders. We further conducted a subgroup analysis within the schizophrenia spectrum to examine differences in microstate properties between medicated and unmedicated patients.

RESULTS

Microstate C demonstrated significant increase in coverage, and occurrence in patients with schizophrenia, first episode psychosis and high risk for psychosis, and increased duration in schizophrenia. The absence of increased occurrence in medicated schizophrenia patients suggests that this feature may be state-dependent or modulated by treatment. In contrast, microstate D exhibited significant decreases in duration and coverage in unmedicated schizophrenia patients, indicating potential links with acute psychotic states.

CONCLUSIONS

Our findings suggest that microstates C and D could serve as potential biomarkers in schizophrenia, with microstate D alterations linked to acute psychotic symptoms and microstate C potentially reflecting a chronic course or treatment effects. These results emphasize the clinical potential of microstate analysis in psychotic disorder diagnosis and treatment monitoring. The literature on microstate variations in neurodevelopmental and mood disorders is limited, highlighting the need for further research to determine their biomarker potential in these conditions.

Spectral and Microstate EEG Analysis in Narcolepsy Type 1 and Type 2 Across Sleep Stages

BACKGROUND

The primary distinction between narcolepsy type 1 (NT1) and narcolepsy type 2 (NT2) is the presence or absence of cataplexy, which is commonly determined through clinical interviews, though it can be prone to error due to vague patients descriptions.

OBJECTIVE

This study aimed to investigate EEG microstate differences between NT1 and NT2 and their correlation with clinical assessments.

METHODS

Polysomnography (PSG) and the Multiple Sleep Latency Test (MSLT) were performed on 14 NT1 and 13 NT2 patients from three hospitals, with data from the ISRUC-SLEEP dataset serving as the comparison group. After EEG preprocessing, we performed the spectral analysis in NT1 and NT2, followed by microstate analysis. Grand mean maps were used for backfitting to obtain microstate parameters. Then, Spearman correlation was performed between the microstate parameters and the ESS and MSLT parameters.

RESULTS

We found that the relative delta power in N2 was lower in the NT1 group compared to the NT2 group. Four microstates were clustered in all groups, and no statistical differences were observed in the microstate parameters between NT1 and NT2 groups. In the NT1 group, microstate D during wakefulness showed a positive correlation with ESS, while in the NT2 group, microstate D during wakefulness showed a negative correlation with ESS.

CONCLUSIONS

There are spectral differences between the NT1 and NT2 groups, and the opposite correlation between microstate D and ESS during wakefulness in NT1 and NT2 suggest that the underlying mechanisms leading to excessive daytime sleepiness in the two groups may be different.

Neural Oscillatory Mechanisms Underlying Step Accuracy: Integrating Microstate Segmentation with eLORETA-Independent Component Analysis

BACKGROUND/OBJECTIVES

Precise stepping control is fundamental to human mobility, and impairments increase fall risk in older adults and individuals with neurological conditions. This study investigated the cortical networks underlying stepping accuracy using mobile brain/body imaging with electroencephalography (EEG)-based exact low-resolution electromagnetic tomography-independent component analysis (eLORETA-ICA) and microstate segmentation analysis (MSA).

METHODS

Sixteen healthy male participants performed a precision stepping task while wearing a mobile EEG system. Step performance was quantified using error distance, measuring deviation between target and heel contact points. Preprocessed EEG data were analyzed using eLORETA-ICA and MSA, with participants categorized into high- and low-performing groups.

RESULTS

Seven microstate clusters were identified, with the anterior cingulate cortex (ACC) showing the highest microstate probability (21.15%). The high-performing group exhibited amplified theta-band activity in the ACC, enhanced activity in the precuneus and postcentral gyrus, and suppressed mu- and beta-band activity in the paracentral lobules.

CONCLUSIONS

Stepping accuracy relies on a distributed neural network, with the ACC playing a central role in performance monitoring. We propose an integrated framework comprising the following systems: error monitoring (ACC), sensorimotor integration (paracentral lobules), and visuospatial processing (precuneus and occipital regions). These findings highlight the importance of neural oscillatory mechanisms in precise motor control and offer insights for rehabilitation strategies and fall prevention programs.

Stable EEG Spatiospectral Patterns Estimated in Individuals by Group Information Guided NMF

Electroencephalographic (EEG) oscillations occur across a wide range of spatial and spectral scales, and analysis of neural rhythmic variability have attracted recent attention as markers of development, intelligence, cognitive states and neural disorders. Nonnegative matrix factorization (NMF) has been successfully applied to multi-subject electroencephalography (EEG) spectral analysis. However, existing group NMF methods have not explicitly optimized the individual-level EEG components derived from group-level components. To preserve EEG characteristics at the individual level while establishing correspondence of patterns across participants, we present a novel framework for obtaining subject-specific EEG components, which we term group-information guided NMF (GIGNMF). In this framework, group information captured by standard NMF at the group level is utilized as guidance to compute individual subject-specific components through a multi-objective optimization strategy. Specifically, we propose a three-stage framework: first, group-level consensus EEG patterns are derived using standard group NMF tools; second, an optimal procedure is implemented to determine the number of components; and finally, the group-level EEG patterns serve as references in a new one-unit NMF employing a multi-objective optimization solver. We test the performance of the algorithm on both synthetic signals and real EEG recordings obtained from Alzheimer's disease data. Our results highlight the feasibility of using GIGNMF to identify EEG spatiotemporal patterns and present novel individual electrophysiological characteristics that enhance our understanding of cognitive function and contribute to clinical neuropathological diagnosis.

EEG microstate in people with different degrees of fear of heights during virtual high-altitude exposure

Previous neuroimaging studies based on electroencephalography (EEG) microstate analysis have identified abnormal neural electric activity in patients with psychiatric diseases. However, the microstate information in individuals with different degrees of fear of heights (FoH) remains unknown so far. The aim of the study was therefore to explore the changes of EEG microstate characteristics in different FoH individuals when exposed to high-altitude stimulated by virtual reality (VR). First, acrophobia questionnaire (AQ) before the experiment and 32-channel EEG signals under the virtual high-altitude exposure were collected from 69 subjects. Second, each subject was divided into one of three levels of FoH including no-FoH, mild or moderate FoH (m-FoH) and severe FoH (s-FoH) groups according to their AQ scores. Third, using microstate analysis, we transformed EEG data into sequences of characteristic topographic maps and computed EEG microstate features including microstate basic parameters, microstate sequences complexity and microstate energy. Finally, the extracted features as inputs were sent to train and test an support vector machine (SVM) for classifying different FoH groups. The results demonstrated that five types of microstates (labeled as A, B, C, D and F) were identified across all subjects, of which microstates A-D resembled the four typical microstate classes and microstate F was a non-canonical microstate. Significantly decreased occurrence, coverage and duration of microstate F and transition probabilities from other microstates to microstate F in m-FoH and s-FoH groups were observed compared to no-FoH group. It was also demonstrated that both m-FoH and s-FoH groups showed a notable reduction in sample entropy and Lempel-Ziv complexity. Moreover, energies of microstate D for m-FoH group and microstate B for s-FoH group in right parietal, parietooccipital and occipital regions exhibited prominent decreases as comparison to people without FoH. But, no significant differences were found between m-FoH and s-FoH groups. Additionally, the results indicated that AQ-anxiety scores were negatively correlated with microstate basic metrics as well as microstate energy. For classification, the performance of SVM reached a relatively high accuracy of 89 % for distinguishing no-FoH from m-FoH. In summary, the findings highlight the alterations of EEG microstates in people with fear of heights induced by virtual high-altitude, reflecting potentially underlying abnormalities in the allocation of neural assemblies. Therefore, the combination of EEG microstate analysis and VR may be a potential valuable approach for the diagnosis of fear of heights.

Microstate Analysis of Continuous Infant EEG: Tutorial and Reliability

Microstate analysis of resting-state EEG is a unique data-driven method for identifying patterns of scalp potential topographies, or microstates, that reflect stable but transient periods of synchronized neural activity evolving dynamically over time. During infancy - a critical period of rapid brain development and plasticity - microstate analysis offers a unique opportunity for characterizing the spatial and temporal dynamics of brain activity. However, whether measurements derived from this approach (e.g., temporal properties, transition probabilities, neural sources) show strong psychometric properties (i.e., reliability) during infancy is unknown and key information for advancing our understanding of how microstates are shaped by early life experiences and whether they relate to individual differences in infant abilities. A lack of methodological resources for performing microstate analysis of infant EEG has further hindered adoption of this cutting-edge approach by infant researchers. As a result, in the current study, we systematically addressed these knowledge gaps and report that most microstate-based measurements of brain organization and functioning except for transition probabilities were stable with four minutes of video-watching resting-state data and highly internally consistent with just one minute. In addition to these results, we provide a step-by-step tutorial, accompanying website, and open-access data for performing microstate analysis using a free, user-friendly software called Cartool. Taken together, the current study supports the reliability and feasibility of using EEG microstate analysis to study infant brain development and increases the accessibility of this approach for the field of developmental neuroscience.

MICROSTATELAB: The EEGLAB Toolbox for Resting-State Microstate Analysis

Microstate analysis is a multivariate method that enables investigations of the temporal dynamics of large-scale neural networks in EEG recordings of human brain activity. To meet the enormously increasing interest in this approach, we provide a thoroughly updated version of the first open source EEGLAB toolbox for the standardized identification, visualization, and quantification of microstates in resting-state EEG data. The toolbox allows scientists to (i) identify individual, mean, and grand mean microstate maps using topographical clustering approaches, (ii) check data quality and detect outlier maps, (iii) visualize, sort, and label individual, mean, and grand mean microstate maps according to published maps, (iv) compare topographical similarities of group and grand mean microstate maps and quantify shared variances, (v) obtain the temporal dynamics of the microstate classes in individual EEGs, (vi) export quantifications of these temporal dynamics of the microstates for statistical tests, and finally, (vii) test for topographical differences between groups and conditions using topographic analysis of variance (TANOVA). Here, we introduce the toolbox in a step-by-step tutorial, using a sample dataset of 34 resting-state EEG recordings that are publicly available to follow along with this tutorial. The goals of this manuscript are (a) to provide a standardized, freely available toolbox for resting-state microstate analysis to the scientific community, (b) to allow researchers to use best practices for microstate analysis by following a step-by-step tutorial, and (c) to improve the methodological standards of microstate research by providing previously unavailable functions and recommendations on critical decisions required in microstate analyses.

Analysis of microstate features for Parkinson's disease based on reliability validation

BACKGROUND

Parkinson's disease (PD) is a disorder with abnormal changes in brain activity. The lack of objective indicators makes the assessment of PD progression difficult. Assessment of brain activity changes in PD may offer a potential solution.

NEW METHOD

Electroencephalogram (EEG) microstates reflect global dynamic changes in the brain. Therefore, we utilized microstates to assess changes in PD brain activity. However, the effect of epoch duration on the reliability of microstate analyses in PD is unclear. Thus, we first assessed the effect of data duration on the reliability of microstate topography and temporal features in PD and older healthy individuals. According to the reliability assessment, EEG epochs with high reliability were selected for microstate analysis in PD. Finally, we investigated the correlation between microstate features and clinical scales to determine whether these features could serve as objective indicators to evaluate PD progression.

RESULTS

Microstate analysis features that show high reliability for 3 min and above epoch durations. The topology of microstate D was significantly changed in PD compared to healthy controls, as well as the temporal features of microstates C and D. Additionally, the occurrence of C was negatively correlated with MoCA, and the duration of D was positively correlated with UPDRS.

COMPARISON WITH EXISTING METHOD(S)

High reliability of PD microstate features obtained by our approach.

CONCLUSION

EEG for PD microstate analysis should be at least 3 min. Microstate analysis is expected to provide new ideas and objective indicators for assessing Parkinson's disease progression in the clinical setting.

State-transition dynamics of resting-state functional magnetic resonance imaging data: model comparison and test-to-retest analysis

Electroencephalogram (EEG) microstate analysis entails finding dynamics of quasi-stable and generally recurrent discrete states in multichannel EEG time series data and relating properties of the estimated state-transition dynamics to observables such as cognition and behavior. While microstate analysis has been widely employed to analyze EEG data, its use remains less prevalent in functional magnetic resonance imaging (fMRI) data, largely due to the slower timescale of such data. In the present study, we extend various data clustering methods used in EEG microstate analysis to resting-state fMRI data from healthy humans to extract their state-transition dynamics. We show that the quality of clustering is on par with that for various microstate analyses of EEG data. We then develop a method for examining test-retest reliability of the discrete-state transition dynamics between fMRI sessions and show that the within-participant test-retest reliability is higher than between-participant test-retest reliability for different indices of state-transition dynamics, different networks, and different data sets. This result suggests that state-transition dynamics analysis of fMRI data could discriminate between different individuals and is a promising tool for performing fingerprinting analysis of individuals.

The Functional Aspects of Resting EEG Microstates: A Systematic Review

A growing body of clinical and cognitive neuroscience studies have adapted a broadband EEG microstate approach to evaluate the electrical activity of large-scale cortical networks. However, the functional aspects of these microstates have not yet been systematically reviewed. Here, we present an overview of the existing literature and systematize the results to provide hints on the functional role of electrical brain microstates. Studies that evaluated and manipulated the temporal properties of resting-state microstates and utilized questionnaires, task-initiated thoughts, specific tasks before or between EEG session(s), pharmacological interventions, neuromodulation approaches, or localized sources of the extracted microstates were selected. Fifty studies that met the inclusion criteria were included. A new microstate labeling system has been proposed for a comprehensible comparison between the studies, where four classical microstates are referred to as A-D, and the others are labeled by the frequency of their appearance. Microstate A was associated with both auditory and visual processing and links to subjects' arousal/arousability. Microstate B showed associations with visual processing related to self, self-visualization, and autobiographical memory. Microstate C was related to processing personally significant information, self-reflection, and self-referential internal mentation rather than autonomic information processing. In contrast, microstate E was related to processing interoceptive and emotional information and to the salience network. Microstate D was associated with executive functioning. Microstate F is suggested to be a part of the Default Mode Network and plays a role in personally significant information processing, mental simulations, and theory of mind. Microstate G is potentially linked to the somatosensory network.

Propofol Reversibly Attenuates Short-Range Microstate Ordering and 20 Hz Microstate Oscillations

Microstate sequences summarize the changing voltage patterns measured by electroencephalography, using a clustering approach to reduce the high dimensionality of the underlying data. A common approach is to restrict the pattern matching step to local maxima of the global field power (GFP) and to interpolate the microstate fit in between. In this study, we investigate how the anesthetic propofol affects microstate sequence periodicity and predictability, and how these metrics are changed by interpolation. We performed two frequency analyses on microstate sequences, one based on time-lagged mutual information, the other based on Fourier transform methodology, and quantified the effects of interpolation. Resting-state microstate sequences had a 20 Hz frequency peak related to dominant 10 Hz (alpha) rhythms, and the Fourier approach demonstrated that all five microstate classes followed this frequency. The 20 Hz periodicity was reversibly attenuated under moderate propofol sedation, as shown by mutual information and Fourier analysis. Characteristic microstate frequencies could only be observed in non-interpolated microstate sequences and were masked by smoothing effects of interpolation. Information-theoretic analysis revealed faster microstate dynamics and larger entropy rates under propofol, whereas Shannon entropy did not change significantly. In moderate sedation, active information storage decreased for non-interpolated sequences. Signatures of non-equilibrium dynamics were observed in non-interpolated sequences, but no changes were observed between sedation levels. All changes occurred while subjects were able to perform an auditory perception task. In summary, we show that low dose propofol reversibly increases the randomness of microstate sequences and attenuates microstate oscillations without correlation to cognitive task performance. Microstate dynamics between GFP peaks reflect physiological processes that are not accessible in interpolated sequences.

A Potential Source of Bias in Group-Level EEG Microstate Analysis

Microstate analysis is a promising technique for analyzing high-density electroencephalographic data, but there are multiple questions about methodological best practices. Between and within individuals, microstates can differ both in terms of characteristic topographies and temporal dynamics, which leads to analytic challenges as the measurement of microstate dynamics is dependent on assumptions about their topographies. Here we focus on the analysis of group differences, using simulations seeded on real data from healthy control subjects to compare approaches that derive separate sets of maps within subgroups versus a single set of maps applied uniformly to the entire dataset. In the absence of true group differences in either microstate maps or temporal metrics, we found that using separate subgroup maps resulted in substantially inflated type I error rates. On the other hand, when groups truly differed in their microstate maps, analyses based on a single set of maps confounded topographic effects with differences in other derived metrics. We propose an approach to alleviate both classes of bias, based on a paired analysis of all subgroup maps. We illustrate the qualitative and quantitative impact of these issues in real data by comparing waking versus non-rapid eye movement sleep microstates. Overall, our results suggest that even subtle chance differences in microstate topography can have profound effects on derived microstate metrics and that future studies using microstate analysis should take steps to mitigate this large source of error.

Changes in electroencephalography microstates are associated with reduced levels of vigilance after sleep deprivation

Total sleep deprivation (TSD) negatively affects cognitive functions, especially vigilance attention, but studies on vigilance changes in terms of electroencephalography (EEG) microstates after TSD are limited. This study investigates the impact of TSD on vigilance attention, EEG microstates and its relationship. Thirty healthy adult males completed a psychomotor vigilance task (PVT) before, 24 h after, and 36 h after TSD while their EEG was recorded during rest. Microstate analysis revealed significant changes in the occurrence and contribution of microstate class B after TSD. Moreover, changes in the probability of transitioning between microstate classes A and D were observed, correlating with decreased vigilance. Specifically, a positive correlation was found between transitioning from class B to class C and vigilance, while a trend of negative correlation was observed between transitioning between classes A and D and vigilance. These findings indicate abnormal activity in the salience network and dorsal attention network following sleep deprivation. TSD impairs vigilance attention, as demonstrated by the effects on EEG microstate class B and the transitions between classes A and D. The study suggests its potential as an early warning indicator for predicting vigilance attention after sleep deprivation.

Changes in oscillatory patterns of microstate sequence in patients with first-episode psychosis

We aimed to utilize chaos game representation (CGR) for the investigation of microstate sequences and explore its potential as neurobiomarkers for psychiatric disorders. We applied our proposed method to a public dataset including 82 patients with first-episode psychosis (FEP) and 61 control subjects. Two time series were constructed: one using the microstate spacing distance in CGR and the other using complex numbers representing the microstate coordinates in CGR. Power spectral features of both time series and frequency matrix CGR (FCGR) were compared between groups and employed in a machine learning application. The four canonical microstates (A, B, C, and D) were identified using both shared and separate templates. Our results showed the microstate oscillatory pattern exhibited alterations in the FEP group. Using oscillatory features improved machine learning performance compared with classical features and FCGR. This study opens up new avenues for exploring the use of CGR in analyzing EEG microstate sequences. Features derived from microstate sequence CGR offer fine-grained neurobiomarkers for psychiatric disorders.

Atypical instantaneous spatio-temporal patterns of neural dynamics in Alzheimer's disease

Cognitive functions produced by large-scale neural integrations are the most representative 'emergence phenomena' in complex systems. A novel approach focusing on the instantaneous phase difference of brain oscillations across brain regions has succeeded in detecting moment-to-moment dynamic functional connectivity. However, it is restricted to pairwise observations of two brain regions, contrary to large-scale spatial neural integration in the whole-brain. In this study, we introduce a microstate analysis to capture whole-brain instantaneous phase distributions instead of pairwise differences. Upon applying this method to electroencephalography signals of Alzheimer's disease (AD), which is characterised by progressive cognitive decline, the AD-specific state transition among the four states defined as the leading phase location due to the loss of brain regional interactions could be promptly characterised. In conclusion, our synthetic analysis approach, focusing on the microstate and instantaneous phase, enables the capture of the instantaneous spatiotemporal neural dynamics of brain activity and characterises its pathological conditions.

Distinguishing Laparoscopic Surgery Experts from Novices Using EEG Topographic Features

The study aimed to differentiate experts from novices in laparoscopic surgery tasks using electroencephalogram (EEG) topographic features. A microstate-based common spatial pattern (CSP) analysis with linear discriminant analysis (LDA) was compared to a topography-preserving convolutional neural network (CNN) approach. Expert surgeons (N = 10) and novice medical residents (N = 13) performed laparoscopic suturing tasks, and EEG data from 8 experts and 13 novices were analysed. Microstate-based CSP with LDA revealed distinct spatial patterns in the frontal and parietal cortices for experts, while novices showed frontal cortex involvement. The 3D CNN model (ESNet) demonstrated a superior classification performance (accuracy > 98%, sensitivity 99.30%, specificity 99.70%, F1 score 98.51%, MCC 97.56%) compared to the microstate based CSP analysis with LDA (accuracy ~90%). Combining spatial and temporal information in the 3D CNN model enhanced classifier accuracy and highlighted the importance of the parietal-temporal-occipital association region in differentiating experts and novices.

EEG microstate quantifiers and state space descriptors during anaesthesia in patients with postoperative delirium: a descriptive analysis

Postoperative delirium is a serious sequela of surgery and surgery-related anaesthesia. One recommended method to prevent postoperative delirium is using bi-frontal EEG recording. The single, processed index of depth of anaesthesia allows the anaesthetist to avoid episodes of suppression EEG and excessively deep anaesthesia. The study data presented here were based on multichannel (19 channels) EEG recordings during anaesthesia. This enabled the analysis of various parameters of global electrical brain activity. These parameters were used to compare microstate topographies under anaesthesia with those in healthy volunteers and to analyse changes in microstate quantifiers and EEG global state space descriptors with increasing exposure to anaesthesia. Seventy-three patients from the Surgery Depth of Anaesthesia and Cognitive Outcome study (SRCTN 36437985) received intraoperative multichannel EEG recordings. Altogether, 720 min of artefact-free EEG data, including 210 min (29.2%) of suppression EEG, were analysed. EEG microstate topographies, microstate quantifiers (duration, frequency of occurrence and global field power) and the state space descriptors sigma (overall EEG power), phi (generalized frequency) and omega (number of uncorrelated brain processes) were evaluated as a function of duration of exposure to anaesthesia, suppression EEG and subsequent development of postoperative delirium. The major analyses involved covariate-adjusted linear mixed-effects models. The older (71 ± 7 years), predominantly male (60%) patients received a median exposure of 210 (range: 75-675) min of anaesthesia. During seven postoperative days, 21 patients (29%) developed postoperative delirium. Microstate topographies under anaesthesia resembled topographies from healthy and much younger awake persons. With increasing duration of exposure to anaesthesia, single microstate quantifiers progressed differently in suppression or non-suppression EEG and in patients with or without subsequent postoperative delirium. The most pronounced changes occurred during enduring suppression EEG in patients with subsequent postoperative delirium: duration and frequency of occurrence of microstates C and D progressed in opposite directions, and the state space descriptors showed a pattern of declining uncorrelated brain processes (omega) combined with increasing EEG variance (sigma). With increasing exposure to general anaesthesia, multiple changes in the dynamics of microstates and global EEG parameters occurred. These changes varied partly between suppression and non-suppression EEG and between patients with or without subsequent postoperative delirium. Ongoing suppression EEG in patients with subsequent postoperative delirium was associated with reduced network complexity in combination with increased overall EEG power. Additionally, marked changes in quantifiers in microstate C and in microstate D occurred. These putatively adverse intraoperative trajectories in global electrical brain activity may be seen as preceding and ultimately predicting postoperative delirium.

Neurophysiological biomarkers for depression classification: Utilizing microstate k-mers and a bag-of-words model

Microstates are analogous to characters in a language, and short fragments consisting of several microstates (k-mers) are analogous to words. We aimed to investigate whether microstate k-mers could be used as neurophysiological biomarkers to differentiate between depressed patients and normal controls. We utilized a bag-of-words model to process microstate sequences, using k-mers with a k range of 1-10 as terms, and the term frequency (TF) with or without inverse-document-frequency (IDF) as features. We performed nested cross-validation on Dataset 1 (27 patients and 26 controls) and Dataset 2 (34 patients and 30 controls) separately and then trained on one dataset and tested on the other. The best area under the curve (AUC) of 81.5% was achieved for the model with L1 regularization using the TF of 4-mers as features in Dataset 1, and the best AUC of 88.9% was achieved for the model with L1 regularization using the TF of 9-mers as features in Dataset 2. When Dataset 1 was used as the training set, the best AUC of predicting Dataset 2 was 74.1% for the model with L2 regularization using the TF-IDF of 9-mers as features, while the best AUC of predicting Dataset 1 was 70.2% for the model with L1 regularization using the TF of 8-mers as features. Our study provided novel insights into the potential of microstate k-mers as neurophysiological biomarkers for individual-level classification of depression. These may facilitate further exploration of microstate sequences using natural language processing techniques.

Sub-Scalp Implantable Telemetric EEG (SITE) for the Management of Neurological and Behavioral Disorders beyond Epilepsy

Sub-scalp Implantable Telemetric EEG (SITE) devices are under development for the treatment of epilepsy. However, beyond epilepsy, continuous EEG analysis could revolutionize the management of patients suffering from all types of brain disorders. This article reviews decades of foundational EEG research, collected from short-term routine EEG studies of common neurological and behavioral disorders, that may guide future SITE management and research. Established quantitative EEG methods, like spectral EEG power density calculation combined with state-of-the-art machine learning techniques applied to SITE data, can identify new EEG biomarkers of neurological disease. From distinguishing syncopal events from seizures to predicting the risk of dementia, SITE-derived EEG biomarkers can provide clinicians with real-time information about diagnosis, treatment response, and disease progression.

Clustering-Fusion Feature Selection Method in Identifying Major Depressive Disorder Based on Resting State EEG Signals

Depression is a heterogeneous syndrome with certain individual differences among subjects. Exploring a feature selection method that can effectively mine the commonness intra-groups and the differences inter-groups in depression recognition is therefore of great significance. This study proposed a new clustering-fusion feature selection method. Hierarchical clustering (HC) algorithm was used to capture the heterogeneity distribution of subjects. Average and similarity network fusion (SNF) algorithms were adopted to characterize the brain network atlas of different populations. Differences analysis was also utilized to obtain the features with discriminant performance. Experiments showed that compared with traditional feature selection methods, HCSNF method yielded the optimal classification results of depression recognition in both sensor and source layers of electroencephalography (EEG) data. Especially in the beta band of EEG data at sensor layer, the classification performance was improved by more than 6%. Moreover, the long-distance connections between parietal-occipital lobe and other brain regions not only have high discriminative power, but also significantly correlate with depressive symptoms, indicating the important role of these features in depression recognition. Therefore, this study may provide methodological guidance for the discovery of reproducible electrophysiological biomarkers and new insights into common neuropathological mechanisms of heterogeneous depression diseases.

Electroencephalography microstates imbalance across the spectrum of early psychosis, autism, and mood disorders

BACKGROUND

Electroencephalography (EEG) microstates translate resting-state temporal dynamics of neuronal networks throughout the brain and could constitute possible markers of psychiatric disorders. We tested the hypothesis of an increased imbalance between a predominant self-referential mode (microstate C) and a decreased attentional mode (microstate D) in psychosis, mood, and autism spectrum disorders.

METHODS

We retrospectively included 135 subjects from an early psychosis outpatient unit, with available eyes-closed resting-state 19 electrodes EEG. Individual-level then group-level modified K-means clustering in controls provided four microstate maps that were then backfitted to all groups. Differences between microstate parameters (occurrence, coverage, and mean duration) were computed between controls and each group, and between disease groups.

RESULTS

Microstate class D parameters were systematically decreased in disease groups compared with controls, with an effect size increasing along the psychosis spectrum, but also in autism. There was no difference in class C. C/D ratios of mean duration were increased only in SCZ compared with controls.

CONCLUSIONS

The decrease in microstate class D may be a marker of stage of psychosis, but it is not specific to it and may rather reflect a shared dimension along the schizophrenia-autism spectrum. C/D microstate imbalance may be more specific to schizophrenia.

Microstate analyses as an indicator of anesthesia-induced unconsciousness

OBJECTIVE

The objective of this study was to identify differences in electroencephalographic microstate topographies across three perioperative phases: anesthetic pre-induction, surgical anesthesia, and post-anesthesia care unit (PACU) admission.

METHODS

Whole-scalp 16-channel electroencephalographic recordings were taken throughout the perioperative period on n = 22 adult, non-cardiac surgical patients.

RESULTS

Several differences between perioperative periods were identified. Most notably, during surgical anesthesia, patients demonstrated increased mean duration and, consequently, a reduction in the occurrence of microstates when compared to both preoperative baseline and PACU admission. We also observed the presence of microstate F with propofol anesthesia during surgery, which had been previously identified with propofol infusion in laboratory settings using human volunteers. Finally, we observed inverse age effects with mean occurrence and duration of microstates, particularly during PACU recovery.

CONCLUSIONS

Microstate duration is significantly increased during surgery compared to both pre-induction and PACU recovery. These data suggest that microstate topographies may be useful in monitoring anesthetic depth.

SIGNIFICANCE

This work highlights the potential for microstate analysis in the perioperative setting. We identified distinct topographical signatures across perioperative periods and with increasing age, which is predictive of post-operative delirium.

Event-related microstate dynamics represents working memory performance

In recent years, EEG microstate analysis has attracted much attention as a tool for characterizing the spatial and temporal dynamics of large-scale electrophysiological activities in the human brain. Canonical 4 states (classes A, B, C, and D) have been widely reported, and they have been pointed out for their relationships with cognitive functions and several psychiatric disorders such as schizophrenia, in particular, through their static parameters such as average duration, occurrence, coverage, and transition probability. However, the relationships between event-related microstate changes and their related cognitive functions, as is often analyzed in event-related potentials under time-locked frameworks, is still not well understood. Furthermore, not enough attention has been paid to the relationship between microstate dynamics and static characteristics. To clarify the relationships between the static microstate parameters and dynamic microstate changes, and between the dynamics and working memory (WM) function, we first examined the temporal profiles of the microstates during the N-back task. We found significant event-related microstate dynamics that differed predominantly with WM loads, which were not clearly observed in the static parameters. Furthermore, in the 2-back condition, patterns of state transitions from class A to C in the high- and low-performance groups showed prominent differences at 50-300 ms after stimulus onset. We also confirmed that the transition patterns of the specific time periods were able to predict the performance level (low or high) in the 2-back condition at a significant level, where a specific transition between microstates, namely from class A to C with specific polarity, contributed to the prediction robustly. Taken together, our findings indicate that event-related microstate dynamics at 50-300 ms after onset may be essential for WM function. This suggests that event-related microstate dynamics can reflect more highly-refined brain functions.

Social reward anticipation in infants as revealed by event-related potentials

Infants engage in gaze interaction from the early stage of life. Emerging studies suggest that infants may expect social reward of shared attention before looking to the same object with another person. However, it was unknown about the neural responses during the anticipation of social rewards before shared attention in infants. We tested infants' reward anticipations in the gaze cueing situation measured by event-related potentials in the social association learning task. Six- to ten-month-old infants (N = 20) repeatedly observed that a female predictively looked toward the animation position (valid condition) or another female looking away from the animation (invalid condition). It was posited that infants could learn associations between female faces and the event of shared attention. The results showed that the stimulus preceding negativity which reflects reward anticipation before the animation presentation was elicited in the second half of the learning phases in the valid condition. Additionally, after the presentation of the face, N290 was greater in the second half of the learning phase than in the first half in the valid condition. These results suggest that infants can anticipate social reward from gaze cues, and learning the gaze cueing validity may affect not only reward anticipation but face perception.

Effects of modafinil on electroencephalographic microstates in healthy adults

RATIONALE

Modafinil has been proposed as a potentially effective clinical treatment for neuropsychiatric disorders characterized by cognitive control deficits. However, the precise effects of modafinil, particularly on brain network functions, are not completely understood.

OBJECTIVES

To address this gap, we examined the effects of modafinil on resting-state brain activity in 30 healthy adults using microstate analysis. Electroencephalographic (EEG) microstates are discrete voltage topographies generated from resting-state network activity.

METHODS

Using a placebo-controlled, within-subjects design, we examined changes to microstate parameters following placebo (0 mg), low (100 mg), and high (200 mg) modafinil doses. We also examined the functional significance of these microstates via associations between microstate parameters and event-related potential indexes of conflict monitoring and automatic error processing (N2 and error-related negativity) and behavioral responses (accuracy and RT) from a subsequent flanker interference task.

RESULTS

Five microstates emerged following each treatment condition, including four canonical microstates (A-D). Modafinil increased microstate C proportion and occurrence regardless of dose, relative to placebo. Modafinil also decreased microstate A proportion and microstate B proportion and occurrence relative to placebo. These modafinil-related changes in microstate parameters were not associated with similar changes in flanker ERPs or behavior. Finally, modafinil made transitions between microstates A and B less likely and transitions from A and B to C more likely.

CONCLUSIONS

Previous fMRI work has correlated microstates A and B with auditory and visual networks and microstate C with a salience network. Thus, our results suggest modafinil may deactivate large-scale sensory networks in favor of a higher order functional network during resting-state in healthy adults.

Characteristics of EEG Microstate Sequences During Propofol-Induced Alterations of Brain Consciousness States

Monitoring the consciousness states of patients and ensuring the appropriate depth of anesthesia (DOA) is critical for the safe implementation of surgery. In this study, a high-density electroencephalogram (EEG) combined with blood drug concentration and behavioral response indicators was used to monitor propofol-induced sedation and evaluate the alterations in consciousness states. Microstate analysis, which can reflect the semi-stable state of the sub-second activation of the brain functional network, can be used to assess the brain's consciousness states. In this research, the EEG microstate sequences were constructed to compare the characteristics of corresponding sequences. Compared with the baseline (BS) state, the microstate sequences in the moderate sedation (MD) state exhibited higher complexity indexes of the multiscale sample entropy. With respect to the transition probability (TP) of microstates, most microstates tended to be converted into microstate C in the BS state. In contrast, they tended to be converted into microstate F in the MD state. The significant difference between the expected TP and observed TP could lead to the conclusion that hidden layers were present when there were changes in the consciousness states. According to the hidden Markov model, the accuracy of distinguishing the BS and MD states was 80.16%. The characteristics of microstate sequence revealed the variations in the brain states caused by alterations in consciousness states during anesthesia from a new perspective and presented a new idea for monitoring the DOA.

Electroencephalography Microstate Alterations in Otogenic Vertigo: A Potential Disease Marker

Objectives

A huge population, especially the elderly, suffers from otogenic vertigo. However, the multi-modal vestibular network changes, secondary to periphery vestibular dysfunction, have not been fully elucidated. We aim to identify potential microstate electroencephalography (EEG) signatures for otogenic vertigo in this study.

Materials and Methods

Patients with recurrent otogenic vertigo and age-matched healthy adults were recruited. We performed 256-channel EEG recording of all participants at resting state. Neuropsychological questionnaires and vestibular function tests were taken as a measurement of patients’ symptoms and severity. We clustered microstates into four classes (A, B, C, and D) and identified their dynamic and syntax alterations of them. These features were further fed into a support vector machine (SVM) classifier to identify microstate signatures for vertigo.

Results

We compared 40 patients to 45 healthy adults, finding an increase in the duration of Microstate A, and both the occurrence and time coverage of Microstate D. The coverage and occurrence of Microstate C decreased significantly, and the probabilities of non-random transitions between Microstate A and D, as well as Microstate B and C, also changed. To distinguish the patients, the SVM classifier, which is built based on these features, got a balanced accuracy of 0.79 with a sensitivity of 0.78 and a specificity of 0.8.

Conclusion

There are several temporal dynamic alterations of EEG microstates in patients with otogenic vertigo, especially in Microstate D, reflecting the underlying process of visual-vestibular reorganization and attention redistribution. This neurophysiological signature of microstates could be used to identify patients with vertigo in the future.

Abnormalities in resting-state EEG microstates are a vulnerability marker of migraine

Background

Resting-state EEG microstates are thought to reflect brief activations of several interacting components of resting-state brain networks. Surprisingly, we still know little about the role of these microstates in migraine. In the present study, we attempted to address this issue by examining EEG microstates in patients with migraine without aura (MwoA) during the interictal period and comparing them with those of a group of healthy controls (HC).

Methods

Resting-state EEG was recorded in 61 MwoA patients (50 females) and 66 HC (50 females). Microstate parameters were compared between the two groups. We computed four widely identified canonical microstate classes A-D.

Results

Microstate classes B and D displayed higher time coverage and occurrence in the MwoA patient group than in the HC group, while microstate class C exhibited significantly lower time coverage and occurrence in the MwoA patient group. Meanwhile, the mean duration of microstate class C was significantly shorter in the MwoA patient group than in the HC group. Moreover, among the MwoA patient group, the duration of microstate class C correlated negatively with clinical measures of headache-related disability as assessed by the six-item Headache Impact Test (HIT-6). Finally, microstate syntax analysis showed significant differences in transition probabilities between the two groups, primarily involving microstate classes B, C, and D.

Conclusions

By exploring EEG microstate characteristics at baseline we were able to explore the neurobiological mechanisms underlying altered cortical excitability and aberrant sensory, affective, and cognitive processing, thus deepening our understanding of migraine pathophysiology.

EEG microstate dynamics indicate a U-shaped path to propofol-induced loss of consciousness

Evidence suggests that the stream of consciousness is parsed into transient brain states manifesting themselves as discrete spatiotemporal patterns of global neuronal activity. Electroencephalographical (EEG) microstates are proposed as the neurophysiological correlates of these transiently stable brain states that last for fractions of seconds. To further understand the link between EEG microstate dynamics and consciousness, we continuously recorded high-density EEG in 23 surgical patients from their awake state to unconsciousness, induced by step-wise increasing concentrations of the intravenous anesthetic propofol. Besides the conventional parameters of microstate dynamics, we introduce a new implementation of a method to estimate the complexity of microstate sequences. The brain activity under the surgical anesthesia showed a decreased sequence complexity of the stereotypical microstates, which became sparser and longer-lasting. However, we observed an initial increase in microstates' temporal dynamics and complexity with increasing depth of sedation leading to a distinctive "U-shape" that may be linked to the paradoxical excitation induced by moderate levels of propofol. Our results support the idea that the brain is in a metastable state under normal conditions, balancing between order and chaos in order to flexibly switch from one state to another. The temporal dynamics of EEG microstates indicate changes of this critical balance between stability and transition that lead to altered states of consciousness.

Automatic Detection of High-Frequency Oscillations Based on an End-to-End Bi-Branch Neural Network and Clinical Cross-Validation

Accurate identification of high-frequency oscillation (HFO) is an important prerequisite for precise localization of epileptic foci and good prognosis of drug-refractory epilepsy. Exploring a high-performance automatic detection method for HFOs can effectively help clinicians reduce the error rate and reduce manpower. Due to the limited analysis perspective and simple model design, it is difficult to meet the requirements of clinical application by the existing methods. Therefore, an end-to-end bi-branch fusion model is proposed to automatically detect HFOs. With the filtered band-pass signal (signal branch) and time-frequency image (TFpic branch) as the input of the model, two backbone networks for deep feature extraction are established, respectively. Specifically, a hybrid model based on ResNet1d and long short-term memory (LSTM) is designed for signal branch, which can focus on both the features in time and space dimension, while a ResNet2d with a Convolutional Block Attention Module (CBAM) is constructed for TFpic branch, by which more attention is paid to useful information of TF images. Then the outputs of two branches are fused to realize end-to-end automatic identification of HFOs. Our method is verified on 5 patients with intractable epilepsy. In intravalidation, the proposed method obtained high sensitivity of 94.62%, specificity of 92.7%, and F1-score of 93.33%, and in cross-validation, our method achieved high sensitivity of 92.00%, specificity of 88.26%, and F1-score of 89.11% on average. The results show that the proposed method outperforms the existing detection paradigms of either single signal or single time-frequency diagram strategy. In addition, the average kappa coefficient of visual analysis and automatic detection results is 0.795. The method shows strong generalization ability and high degree of consistency with the gold standard meanwhile. Therefore, it has great potential to be a clinical assistant tool.

Effect of BCI-Controlled Pedaling Training System With Multiple Modalities of Feedback on Motor and Cognitive Function Rehabilitation of Early Subacute Stroke Patients

Brain-computer interfaces (BCIs) are currently integrated into traditional rehabilitation interventions after stroke. Although BCIs bring many benefits to the rehabilitation process, their effects are limited since many patients cannot concentrate during training. Despite this outcome post-stroke motor-attention dual-task training using BCIs has remained mostly unexplored. This study was a randomized placebo-controlled blinded-endpoint clinical trial to investigate the effects of a BCI-controlled pedaling training system (BCI-PT) on the motor and cognitive function of stroke patients during rehabilitation. A total of 30 early subacute ischemic stroke patients with hemiplegia and cognitive impairment were randomly assigned to the BCI-PT or traditional pedaling training. We used single-channel Fp1 to collect electroencephalography data and analyze the attention index. The BCI-PT system timely provided visual, auditory, and somatosensory feedback to enhance the patient's participation to pedaling based on the real-time attention index. After 24 training sessions, the attention index of the experimental group was significantly higher than that of the control group. The lower limbs motor function (FMA-L) increased by an average of 4.5 points in the BCI-PT group and 2.1 points in the control group (P = 0.022) after treatments. The difference was still significant after adjusting for the baseline indicators ( β = 2.41 , 95%CI: 0.48-4.34, P = 0.024). We found that BCI-PT significantly improved the patient's lower limb motor function by increasing the patient's participation. (clinicaltrials.gov: NCT04612426).

HFO Detection in Epilepsy: A Stacked Denoising Autoencoder and Sample Weight Adjusting Factors-Based Method

High-frequency oscillations (HFOs) recorded by the intracranial electroencephalography (iEEG) are the promising biomarkers of epileptogenic zones. Accurate detection of HFOs is the key to pre-operative assessment for epilepsy. Due to the subjective bias caused by manual features and the class imbalance between HFOs and false HFOs, it is difficult to obtain satisfactory detection performance by the existing methods. To solve these problems, we put forward a novel method to accurately detect HFOs based on the stacked denoising autoencoder (SDAE) and the ensemble classifier with sample weight adjusting factors. First, the adjustable threshold of Hilbert envelopes is proposed to isolate the events of interest (EoIs) from background activities. Then, the SDAE network is utilized to automatically extract features of EoIs in the time-frequency domain. Finally, the AdaBoost-based support vector machine ensemble classifier with sample weight adjusting factors is devised to separate HFOs from EoIs by using the extracted features. These adjusting factors are used to solve the class imbalance problem by adjusting sample weights when learning the base classifiers. Our HFO detection method is evaluated by using clinical iEEG data recorded from 20 patients with medically refractory epilepsy. The experimental results show that our detection method outperforms some existing methods in terms of sensitivity and false discovery rate. In addition, the HFOs detected by our method are effective for localizing seizure onset zones.

Identification of EEG Dynamics During Freezing of Gait and Voluntary Stopping in Patients With Parkinson's Disease

Mobility is severely impacted in patients with Parkinson's disease (PD), who often experience involuntary stopping from the freezing of gait (FOG). Understanding the neurophysiological difference between "voluntary stopping" and "involuntary stopping" caused by FOG is vital for the detection of and potential intervention for FOG in the daily lives of patients. This study characterised the electroencephalographic (EEG) signature associated with FOG in contrast to voluntary stopping. The protocol consisted of a timed up-and-go (TUG) task and an additional TUG task with a voluntary stopping component, where participants reacted to verbal "stop" and "walk" instructions by voluntarily stopping or walking. Event-related spectral perturbation (ERSP) analysis was performed to study the dynamics of the EEG spectra induced by different walking phases, including normal walking, voluntary stopping and episodes of involuntary stopping (FOG), as well as the transition windows between normal walking and voluntary stopping or FOG. These results demonstrate for the first time that the EEG signal during the transition from walking to voluntary stopping is distinguishable from that during the transition to involuntary stopping caused by FOG. The EEG signature of voluntary stopping exhibits a significantly decreased power spectrum compared with that of FOG episodes, with distinctly different patterns in the delta and low-beta power in the central area. These findings suggest the possibility of a practical EEG-based tool that can accurately predict FOG episodes, excluding the potential confounding of voluntary stopping.