ICLabel: An automated electroencephalographic independent component classifier, dataset, and website

EEG-derived brain connectivity in theta/alpha frequency bands increases during reading of individual words

Objective: Although extensive insights about the neural mechanisms of reading have been gained via magnetic and electrographic imaging, the temporal evolution of the brain network during sight reading remains unclear. We tested whether the temporal dynamics of the brain functional connectivity involved in sight reading can be tracked using high-density scalp EEG recordings. Approach: Twenty-eight healthy subjects were asked to read words in a rapid serial visual presentation task while recording scalp EEG, and phase locking value was used to estimate the functional connectivity between EEG channels in the theta, alpha, beta, and gamma frequency bands. The resultant networks were then tracked through time. Main results: The network's graph density gradually increases as the task unfolds, peaks 150-250-ms after the appearance of each word, and returns to resting-state values, while the shortest path length between non-adjacent functional areas decreases as the density increases, thus indicating that a progressive integration between regions can be detected at the scalp level. This pattern was independent of the word's type or position in the sentence, occurred in the theta/alpha band but not in beta/gamma band, and peaked earlier in the alpha band compared to the theta band (alpha: 184 ± 61.48-ms; theta: 237 ± 65.32-ms, P-value P < 0.01). Nodes in occipital and frontal regions had the highest eigenvector centrality throughout the word's presentation, and no significant lead-lag relationship between frontal/occipital regions and parietal/temporal regions was found, which indicates a consistent pattern in information flow. In the source space, this pattern was driven by a cluster of nodes linked to sensorimotor processing, memory, and semantic integration, with the most central regions being similar across subjects. Significance: These findings indicate that the brain network connectivity can be tracked via scalp EEG as reading unfolds, and EEG-retrieved networks follow highly repetitive patterns lateralized to frontal/occipital areas during reading.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-025-10280-8.

Measuring attentional bias using the dot-probe task in young women: Psychometric properties and feasibility of response-based computations, dwell time, and the N2pc component

BACKGROUND

Attentional bias (AB) is characterized by preferential cognitive and emotional processing of mood-congruent stimuli and considered a central mechanism in mood disorders. Considerable research has focused on improving AB measures to enhance mechanistic understanding and clinical utility. The present study examines psychometric properties of a range of AB measures with a multimodal setup.

METHODS

A nonclinical sample of 62 women aged 20-30 years completed the facial dot-probe task while behavioral responses (reaction time), eye-gaze patterns (eye tracking), and electrical brain potentials (electroencephalography) were recorded. AB metrics from four types of AB measures - traditional, response-based, dwell time, and the N2pc component- were examined with internal consistency and short-term test-retest calculations. AB metrics with an internal consistency score over .4 were considered reliable, and their validity were explored by examining relations to depression and anxiety symptoms. In addition, the consistency between reliable metrics across trials were examined.

RESULTS

Findings show that traditional AB metrics exhibited no degree of reliability, whereas response-based and dwell time metrics overall demonstrated better internal consistencies. Response-based metrics also had higher test-retest reliability in all but one metric. The previously reported reliability of the N2pc component was not observed. As for validity, no linear associations were found between the reliable measures, depression, and anxiety. There were no relations between metrics across trials.

CONCLUSIONS

This study provides insights for future AB research, emphasizing the potential of novel metrics over traditional ones and the use of multimodal setups to develop reliable and potentially hybrid measurements for clinical assessment.

Juggler's ASR: Unpacking the principles of artifact subspace reconstruction for revision toward extreme MoBI

BACKGROUND

To improve the Artifact Subspace Reconstruction (ASR) algorithm's performance for real-world EEG data by addressing the problem of low-quality or no calibration data identification in the original ASR (ASRoriginal) algorithm.

NEW METHOD

We proposed a new method for defining high-quality calibration data using point-by-point amplitude evaluation to eliminate collateral rejection of clean data, which is identified as the major cause of the problem with ASRoriginal. We compared non-parametric and parametric approaches, namely Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and the Generalized Extreme Value (GEV) distribution (ASRDBSCAN and ASRGEV, respectively).

RESULTS (COMPARISON WITH EXISTING METHODS)

We demonstrated the effectiveness of these approaches on simulated and real EEG data. Simulation results showed that ASRDBSCAN and ASRGEV removed simulated artifacts completely where ASRoriginal failed, both in time- and frequency-domain evaluations. In empirical data from 205-channel EEG recordings during a three-ball juggling task (n = 13), ASRDBSCAN found 42 % and ASRGEV found 24 % of data usable for calibration on average, compared to only 9 % by ASRoriginal. Subsequent Independent Component Analysis (ICA) showed that data preprocessed with ASRDBSCAN and ASRGEV produced brain ICs that accounted for more variance of the original data (30 % and 29 %) compared to ASRoriginal (26 %).

CONCLUSIONS

The proposed ASRDBSCAN and ASRGEV methods handle motion-related artifacts better than the original ASR algorithm, enabling researchers to better extract brain activity during real-world motor tasks. These methods provide a practical advantage in processing EEG data from experiments involving high-intensity motor activities, advancing biomedical research capabilities.

Resting EEG source-level connectivity pattern to predict anhedonia improvement with agomelatine treatment in patients with major depression

BACKGROUND

Neuroimaging studies have revealed that dysfunction of reward circuitry in the brain underlies anhedonia, a core symptom of major depressive disorder (MDD) that is related to treatment outcomes. However, the relationship between the brain network at the level of neuronal oscillations and the longitudinal improvement in the severity of anhedonia is still unknown.

METHODS

The study enrolled 84 unmedicated patients with MDD. Anhedonia severity was measured using the Snaith-Hamilton Pleasure Scale (SHAPS). EEG data in the resting state was obtained both at baseline and following an 8-week course of agomelatine 25 mg taken once daily. Whole-brain functional connectivity (FC) of source-level resting-state EEG and FC-derived graph metrics (i.e., global topological properties: global efficiency and local efficiency) were calculated in distinct frequency bands.

RESULTS

SHAPS scores were significantly improved from baseline to 8 weeks. Concurrently, there was a decrease in alpha-1 (8.5-10 Hz) connectivity between the right-hemisphere precuneus (PreC) and the left-hemisphere inferior frontal gyrus (IFG). Reduced alpha-2 (10.5-12 Hz) connectivity between the right-hemisphere transverse temporal gyrus (TTG) and the left-hemisphere superior frontal gyrus (SFG) and middle frontal gyrus (MFG) was observed. Global efficiency in the alpha-1 (p < 0.001) and alpha-2 (p = 0.003) frequency bands and local efficiency in the alpha-1 frequency band (p = 0.003) were reduced. Correlation analyses showed that alpha-1 local efficiency at baseline predicted improvement in SHAPS scores (r = -0.261, p = 0.017).

CONCLUSION

Global topological properties of source-level EEG FC can predict anhedonia improvement during antidepressant treatment, which might help guide treatment decisions and advance precision psychopharmacology.

Sensing the beauty of interface: Neural oscillatory correlates of visual aesthetic judgment

It is critical for manufacturers to assess customers' aesthetic preferences for various interfaces. However, few studies on neural oscillations for aesthetic judgment have yielded inconsistent results. In this study, we explored the EEG oscillations linked to aesthetic judgments using interface materials (from aesthetic to medium and unaesthetic) along with corresponding scrambled images. Present findings showed that theta-band synchronization to interface were significantly higher for aesthetic interfaces than unaesthetic ones during 200-240 ms at the bilateral occipitotemporal electrodes. However, no significant differences in theta-band oscillations were observed between scrambled images of aesthetic and unaesthetic interfaces. During 250-300 ms, similar theta oscillation patterns were found only at the right occipitotemporal electrodes. Meanwhile, during 220-270 ms, aesthetic interfaces induced stronger alpha-beta desynchronization than unaesthetic ones at the left frontal electrodes, and still no such significant differences were observed in scrambled images. These EEG oscillations could serve as valuable real-time indicators for assessing individual aesthetic judgments.

Lower aperiodic EEG activity is associated with reduced verbal fluency performance across adulthood

Age-related cognitive decline associations with human electroencephalography (EEG) have previously focused on periodic activity. However, EEG primarily consists of non-oscillatory aperiodic activity, characterised with an exponent and offset value. In a secondary analysis of a cohort of 111 healthy participants aged 17 - 71 years, we examined the associations of the aperiodic exponent and offset in resting EEG with a battery of cognitive tests consisting of the Colour-Word Interference Test, Wechsler Adult Intelligence Scale IV Digit Span Test, Rey Auditory Learning Test, Delis-Kaplan Executive Function System Trail Making Test, and the Verbal Fluency Test. Using Principal Component Analysis and K-Means Clustering, we identified clusters of electrodes that exhibited similar aperiodic exponent and offset activity during resting-state eyes-closed EEG. Robust linear models were then used to model how aperiodic activity interacted with age and their associations with performance during each cognitive test. Offset by age interactions were identified for the Verbal Fluency Test, where smaller offsets were associated with poorer performance in adults as early as 33 years of age. Greater aperiodic activity is increasingly related to better verbal fluency performance with age in adulthood.

Neural entrainment to pitch changes of auditory targets in noise

Neural entrainment to certain acoustic features can predict speech-in-noise perception, but these features are difficult to separate. We measured neural responses to both natural speech-in-noise and stimuli (auditory figure-ground) that simulate speech-in-noise without any acoustic or linguistic confounds such as stress contour and semantics. The figure-ground stimulus is formed by multiple temporally coherent pure-tone components embedded in a random tone cloud. Previous work has shown that discrimination of dynamic figure-ground based on the fundamental frequency (F0) of natural speech predicts speech-in-noise recognition independent of hearing and age. In this study, we compared the brain substrate for the figure-ground analysis based on the F0 contour and a statistically similar '1/f' contour with speech-in-noise. We used the temporal response function to predict the electroencephalography responses to the frequency trajectories of the auditory targets. We demonstrate that the brain significantly tracked the pitch changes in both AFG conditions (F0 and 1/F tracking) and a sentence-in-noise condition (F0 tracking) at similar latencies, but at similar magnitudes only when tracking the F0 contour. The pitch-tracking accuracy was consistently high across the delta and theta bands for the AFG condition but not for speech. Sensor-space analysis revealed that speech-in-noise performance correlated with the positive peak amplitude of the F0 figure-ground at 100 ms. Source-space analysis revealed bilateral temporal lobe and hippocampal generators, and strong tracking in the superior parietal lobe for auditory figures and natural speech. In conclusion, our findings demonstrate that the human brain reliably tracks the F0 trajectory of both speech and a non-linguistic figure in noise, with speech tracking showing reduced accuracy in the theta band compared to figure-ground tracking. Despite the difference in prediction accuracy, we reveal striking similarities in neural entrainment patterns and source locations between the two paradigms. These results suggest that neural entrainment engages high-level cortical mechanisms independent of linguistic content. Furthermore, we show that TRF peak amplitude serves as a potential biomarker for speech-in-noise ability, highlighting possible clinical applications.

Perceived levels of environmental unpredictability and changes in visual attention mechanisms in adults

Selective attention mechanisms change in response to variations in sensory experiences and environmental demands. In other words, they are influenced not only by favorable contextual experiences but also by unfavorable ones. Therefore, exposure to environmental unpredictability and chaos could influence selective attention. However, there is a lack of studies directly investigating this relationship. This study examined how household chaos and daily unpredictability relate to selective attention at behavioral and neural levels in young adults (n = 39). Participants were categorized as experiencing high or low unpredictability and chaos based on their scores on respective scales. Using EEG recordings, we measured the amplitude of the N2pc and Pd components, along with accuracy and reaction times, during the performance in two visual search tasks that varied in the level of interference from distracting stimuli (presence vs. absence of a color singleton distractor). The results revealed differences in neural activity related to unpredictability but not chaos. Specifically, in the high-interference visual search task, both groups exhibited an N2pc component associated with the singleton distractor, reflecting attentional capture by distracting information. However, the high-unpredictability group showed a larger N2pc amplitude associated with the target and a larger Pd amplitude associated with the distractor. These findings suggest greater engagement of reactive attentional resources to suppress distractors and select the target, and support hypotheses suggesting that adverse contexts involving unpredictability or chaos relate to changes in how individuals process distracting or irrelevant information.

Personalized stimulation therapies for disorders of consciousness: a computational approach to inducing healthy-like brain activity based on neural field theory

Objective.Disorders of consciousness (DoC) remain a significant challenge in neurology, with traditional brain stimulation therapies showing limited and inconsistent efficacy across patients. This study presents a novel computational approach grounded in neural field theory for constructing personalized stimulus signals designed to induce healthy-like neural activity patterns in individuals with DoC.Approach.We employ a simplified brain model fitted to the electroencephalogram (EEG) power spectrum of a DoC patient, simulating the individual's neural dynamics. Using model equations and fitted parameters, we mathematically derive stimuli time series that cause the model to generate power spectra typical of healthy individuals. These stimuli are tailored for brain regions typically targeted by neuromodulation therapies, such as deep brain stimulation and repetitive transcranial magnetic stimulation.Main results.In silico simulations demonstrate that our method successfully induces healthy-like EEG power spectra in models fitted to DoC patients. Furthermore, when the model parameters were near a stability boundary, stimulation led to a bifurcation and lasting changes in the model's activity beyond the stimulation period.Significance.By inducing healthy-like neural activity, this approach may effectively activate plasticity mechanisms during long-term treatment, potentially leading to sustained improvements in a patient's condition. While further clinical adjustments and validation are needed, this method holds promise for improving therapeutic outcomes in DoC. Moreover, it offers potential extensions to other neurological conditions that could benefit from personalized brain stimulation therapies.

The electroencephalography protocol for the Accelerating Medicines Partnership® Schizophrenia Program: Reliability and stability of measures

Individuals at clinical high risk for psychosis (CHR) have variable clinical outcomes and low conversion rates, limiting development of novel and personalized treatments. Moreover, given risks of antipsychotic drugs, safer effective medications for CHR individuals are needed. The Accelerating Medicines Partnership® Schizophrenia (AMP® SCZ) Program was launched to address this need. Based on past CHR and schizophrenia studies, AMP SCZ assessed electroencephalography (EEG)-based event-related potential (ERP), event-related oscillation (ERO), and resting EEG power spectral density (PSD) measures, including mismatch negativity (MMN), auditory and visual P300 to target (P3b) and novel (P3a) stimuli, 40-Hz auditory steady state response, and resting EEG PSD for traditional frequency bands (eyes open/closed). Here, in an interim analysis of AMP SCZ EEG measures, we assess test-retest reliability and stability over sessions (baseline, month-2 follow-up) in CHR (n = 654) and community control (CON; n = 87) participants. Reliability was calculated as Generalizability (G)-coefficients, and changes over session were assessed with paired t-tests. G-coefficients were generally good to excellent in both groups (CHR: mean = 0.72, range = 0.49-0.85; CON: mean = 0.71, range = 0.44-0.89). Measure magnitudes significantly (p < 0.001) decreased over session (MMN, auditory and visual target P3b, visual novel P3a, 40-Hz ASSR) and/or over runs within sessions (MMN, auditory/visual novel P3a and target P3b), consistent with habituation effects. Despite these small systematic habituation effects, test-retest reliabilities of the AMP SCZ EEG-based measures are sufficiently strong to support their use in CHR studies as potential predictors of clinical outcomes, markers of illness progression, and/or target engagement or secondary outcome measures in controlled clinical trials.

Seeing things in psychosis: Reduced theta power in early neural responses to ambiguous visual stimuli predicts perceptual distortions

OBJECTIVE

In this study we aimed to identify possible neural origins of perceptual disturbances in psychotic disorders.

METHODS

Individuals with schizophrenia and bipolar disorder with psychosis (n = 40), their biological siblings (n = 17), and healthy controls (n = 27) viewed ambiguous object stimuli equivalent in primary visual cortical processing demands, allowing for identification of neural abnormalities occurring beyond basic sensory processing. Magnetoencephalography was collected and neural oscillations were quantified using time-frequency analysis.

RESULTS

Individuals with schizophrenia showed reduced early theta responses over occipital cortex and diminished late desynchronization of alpha/beta in select conditions over parietal cortex. Reduced theta was associated with more schizotypal traits and self-reported perceptual anomalies. Less alpha/beta desynchronization was marginally associated with greater negative symptoms.

CONCLUSIONS

Visual cortical anomalies in schizophrenia beyond primary visual cortex are reflected in reduced early occipital theta oscillations. This impaired bottom-up sensory processing is related to everyday perceptual abnormalities. Diminished later alpha/beta desynchronization in schizophrenia may reflect difficulty disengaging from default mode to access top-down mechanisms that facilitate perception.

SIGNIFICANCE

Early sensory signals, communicated through theta-band oscillations, and later semantic processing, engaged through the desynchronization of alpha/beta oscillations, contribute to ambiguous object detection as well as perceptual disturbances in schizophrenia.

Conflict resolution and response inhibition: A simultaneous EEG-EMG-pupillometry study

Inhibition in cognitive control has many implications. Behaviorally, the stop signal task is supposedly capturing inhibition of already initiated responses (response inhibition). In contrast, the flanker paradigm supposedly captures the inhibition of several competing responses (competitive inhibition). As the neural mechanisms for these behavioral phenomena are not clear, it begs the question of whether both response inhibition and competitive inhibition draw from a similar inhibitory resource pool and to what extent they might interact. In the current study, the potential interplay between inhibitory mechanisms was investigated in a combined stop-signal flanker task where (in-)congruent flankers were occasionally followed by stop signals. A multimodal task-setup was implemented allowing for examination of behavior, electromyography (EMG), electroencephalography (EEG), and pupillometry to assess different inhibition-related outcome measures. Estimates of response inhibition speed (stop-signal reaction times; SSRTs) indicated an interaction with competitive inhibition, where stopping was faster in incongruent compared to congruent stop conditions. However, this was likely driven by differences at the short stop signal delays, which are susceptible to horse race model violations. Moreover, this interaction was not evident in physiological measures: neither stop-related EMG, EEG nor pupillometry measures showed such congruency modulations. Exploratory analyses showed that a larger pupillometry congruency effect was negatively associated with the congruency effect in SSRTs, suggesting that pupil dilation as a proxy for NE-LC activity might be linked to increased allocation of cognitive control. Taken together, our results do not provide clear evidence for an interaction between response inhibition and competitive inhibition.

Assessing the impact of artifact correction and artifact rejection on the performance of SVM- and LDA-based decoding of EEG signals

Numerous studies have demonstrated that eyeblinks and other large artifacts can decrease the signal-to-noise ratio of EEG data, resulting in decreased statistical power for conventional univariate analyses. However, it is not clear whether eliminating these artifacts during preprocessing enhances the performance of multivariate pattern analysis (MVPA; decoding), especially given that artifact rejection reduces the number of trials available for training the decoder. This study aimed to evaluate the impact of artifact-minimization approaches on the decoding performance of support vector machines. Independent component analysis (ICA) was used to correct ocular artifacts, and artifact rejection was used to discard trials with large voltage deflections from other sources (e.g., muscle artifacts). We assessed decoding performance in relatively simple binary classification tasks using data from seven commonly-used event-related potential paradigms (N170, mismatch negativity, N2pc, P3b, N400, lateralized readiness potential, and error-related negativity), as well as more challenging multi-way decoding tasks, including stimulus location and stimulus orientation. The results indicated that the combination of artifact correction and rejection did not improve decoding performance in the vast majority of cases. However, artifact correction may still be essential to minimize artifact-related confounds that might artificially inflate decoding accuracy. Researchers who are using similar methods to decode EEG data from paradigms, populations, and recording setups that are similar to those examined here may benefit from our recommendations to optimize decoding performance and avoid incorrect conclusions.

EEG Signatures and Effects of Mindfulness Approaches in Adolescents With Nonsuicidal Self-Injury

Nonsuicidal self-injury (NSSI) is a recurring behavior most prevalent among adolescents in which one intentionally harms one's tissues and organs without the intent of death, which has a complex pathophysiology and lacks established interventions. As NSSI has been linked to deficits in cognitive control, mindfulness training that enhances this process may be beneficial. In this study, using electroencephalography (EEG), we examined the neural mechanisms underpinning NSSI and the impact of mindfulness interventions by analyzing brain activity before, during, and after a 10-min brief breath-focused meditation session in adolescents with NSSI. We demonstrate that adolescent NSSI patients show a lower correct rejection rate and sensitivity in an emotional go/no-go task that reflects deficits in cognitive control compared to healthy controls, along with reduced P3 amplitude and theta power. A brief deep breath meditation intervention, but not natural breath meditation intervention, restored the decreased no-go theta power in NSSI patients. Analysis of microstates and neural network of resting-state EEG during meditation showed that properties of microstate D reflecting activation of the attention network differed between intervention strategies and predicted NSSI remission at 1-month follow-up. These findings provided evidence for inhibition deficits in adolescents with NSSI, suggest a role of P3 and theta power in identifying NSSI, support the therapeutic benefits of brief meditation, and reveal novel electrophysiological markers of NSSI diagnosis, intervention effects, and outcomes.

Optimizing the stimulus used to elicit the acoustic change complex: Evaluation of the pre-transition stimulus duration and stimulus complexity in normal hearing adults

INTRODUCTION

The Acoustic Change Complex (ACC), a cortical auditory evoked potential elicited by sound changes, is a promising measure of speech discrimination for populations unable to perform speech perception tests. However, its clinical utility is limited by long measurement times, which could be reduced by optimizing the signalto-noise ratio (SNR).

OBJECTIVES

To study the effect of 1) varying the pre-transition duration (PTD) and 2) tonal complexity on ACC outcomes, including N1-P2 amplitude, baseline noise, SNR, and efficiency (SNR divided by measurement time).

METHODS

ACC responses were measured in 18 normal-hearing adults using pure-tone stimuli with a frequency change (1 to 1.1 kHz) and PTDs of 0.25, 0.5, 1, 2 and 3 s, as well as a complex tone with a 1-second PTD.

RESULTS

N1-P2 amplitude increased with PTD up to 2 s. PTD 0.25 s was excluded due to response overlap. Increasing PTD from 0.5 s to 1 s increased efficiency and SNR, reducing estimated measurement time by 78.8 %. ACC presence increased with PTD (100 % for PTD 3 s), but SNR and efficiency gains were absent beyond PTD 1 s. The complex tone showed no difference in N1-P2 amplitude or SNR compared to the pure tone, but increased ACC presence by 22.5 % points.

CONCLUSION

A PTD of 1 s is recommended over 0.5 s. Increasing tonal complexity and the PTD beyond 1 s seems promising to enhance ACC specificity without compromising SNR or efficiency. These findings support stimulus optimization to improve clinical feasibility of ACC measurements.

Impaired rapid neural face categorization after reversing long-lasting congenital blindness

Transient early visual deprivation in humans impairs the processing of faces more than of other object categories. While configural face processing and face individuation appear to be largely impaired in sight recovery individuals following congenital visual deprivation, their behavioral ability to categorize stimuli as faces has been described as preserved. Here we thoroughly investigated rapid automatic face categorization in individuals who had recovered sight after congenital blindness. Eighteen participants (6 women, 12 men) who had undergone congenital cataract reversal surgery participated in a well-validated electroencephalographic (EEG) experiment with fast periodic visual stimulation (FPVS) to elicit automatic neural face-categorization responses from variable natural images. As normally sighted controls (N = 13) and individuals with reversed developmental cataracts (N = 16), congenital cataract reversal individuals exhibited clear neural face-categorization activity. However, their neural face categorization responses were significantly weaker and delayed. These observations show that previous behavioral studies with explicit tasks lacked sensitivity to uncover altered face categorization in sight-recovery individuals with a history of congenital cataracts. This indicates that early experience is necessary for categorization too. We speculate that altered neural correlates of face categorization result from a lower selectivity of face-selective areas of the ventral occipito-temporal cortex, impeding higher-order face processes such as face identity recognition.

A comprehensive exploration of motion sickness process analysis from EEG signal and virtual reality

BACKGROUND AND OBJECTIVE

Virtual reality motion sickness is a significant barrier to the widespread adoption of virtual reality technology. Current virtual reality motion sickness detection methods using EEG signals often fail to identify comprehensive neuro-markers and lack generalizability across multiple subjects.

METHODS

To address this issue, we analyzed the pre- and post-induction phases of virtual reality motion sickness, as well as the induction process, from multiple domain features. The features were extracted from time domain, frequency domain, spatial domain and Riemann space across delta, theta, beta, and all frequency bands. The neuro-markers selected have a correlation greater than 0.5 with behaviors information and showed significant changes in both phases. Five kinds of traditional machine learning methods were used to classify VR motion sickness states in within-in subjects and cross-subjects by using neuro-markers.

RESULTS

Traditional machine learning methods achieved a maximum accuracy of 92 % for within-subject classification and 68 % for cross-subject classification. Spectral entropy across all frequency bands yielded the highest classification accuracy during the pre- and post-induction phases, while spectral skew showed the most significant changes during the task phase.

CONCLUSION

These findings suggest that these features hold strong potential for future neurofeedback studies.

Eye-movement artifact correction in infant EEG: A systematic comparison between ICA and Artifact Blocking

BACKGROUND

Independent Component Analysis (ICA) is a well-established approach to clean EEG and remove the impact of signals of non-neural origin, such as those from muscular activity and eye movements. However, evidence suggests that ICA removes artifacts less effectively in infants than in adults. This study systematically compares ICA and Artifact Blocking (AB), an alternative approach proposed to improve eye-movement artifact correction in infant EEG.

METHODS

We analyzed EEG collected from 50 infants between 6 and 18 months of age as part of the International Infant EEG Data Integration Platform (EEG-IP), a longitudinal multi-study dataset. EEG was recorded while infants sat on their caregivers' laps and watched videos. We used ICA and AB to correct for eye-movement artifacts in the EEG and calculated the proportion of effectively corrected segments, signal-to-noise ratio (SNR), power-spectral density (PSD), and multiscale entropy (MSE) in manually selected EEG segments with and without eye-movement artifacts.

RESULTS

On the one hand, the proportion of effectively corrected segments indicated that ICA corrected eye-movement artifacts (sensitivity) better than AB. SNR and PSD indicated that both AB and ICA correct eye-movement artifacts with equal sensitivity. MSE gave mixed results. On the other hand, AB caused less distortion to the clean segments (specificity) for SNR, PSD, and MSE.

CONCLUSION

Our results suggest that ICA is more sensitive (i.e., it better removes artifacts) but less specific (it distorts clean signals) than AB for correcting eye-movement artifacts in infant EEG.

Providing an alternative explanation improves misinformation rejection and alters event-related potentials during veracity judgements

The continued influence effect of misinformation (CIE) occurs when misinformation affects memory and decision making even after correction. Here, we examined the neurocognitive processes underlying the correction and subsequent veracity judgements of misinformation. Employing electroencephalography (EEG), we examined event-related potentials (ERPs): the P300 during encoding of corrections, and the P300 and FN400 during subsequent veracity judgement. We compared ERPs between three conditions: misinformation that was retracted (retraction only), misinformation that was retracted with a correct alternative cause provided (retraction + alternative), and true information that was later confirmed (confirmation). Results showed that alternatives reduced the CIE significantly. During veracity judgements, the retraction + alternative condition exhibited a higher P300 than the retraction only condition, suggesting enriched recollection processes when re-encountering misinformation if an alternative explanation existed. In contrast, both retraction only and retraction + alternative conditions elicited a less negative FN400 compared to the confirmation condition, suggesting higher conceptual processing fluency of misinformation. Moreover, we found that greater levels of P300 when encoding retraction and alternative causes in the retraction + alternative condition were associated with improved veracity judgement accuracy. Together, these findings suggested that when providing an alternative cause in correcting misinformation, both recollection and encoding processes contributed to reduced CIE.

Estimating sensor-space EEG connectivity: Identifying optimal artifact reduction techniques for functional connectivity in real data

OBJECTIVES

Electroencephalography (EEG) can be used to assess functional brain connectivity (FC). However, there is considerable variability in the methods used for FC measurement across different studies, which may contribute to heterogeneity in research outcomes. We aimed to assess how different EEG pre-processing steps impact EEG-FC measurement when applied to real EEG data.

METHODS

Using the BrainClinics.com open-source EEG data repository we investigated how different pre-processing steps impacted the ability to detect age-related differences in alpha band FC and the test-retest reliability of FC measures. The pre-processing steps tested included artifact reduction techniques (Independent Component Analysis (ICA), wavelet-enhanced ICA (wICA), and Multi-channel Wiener Filters (MWF)), different epoch lengths (epochs that were 2 s versus 6 s in length), and different re-referencing montages (the common average reference (CAR) versus current source density (CSD) re-referencing). We also assessed different FC metrics including imaginary coherence (iCOH), real magnitude squared coherence (rMSC), and weighted phase lag index (wPLI) metrics.

RESULTS

The best performing pipeline at detecting age-related differences in alpha FC and providing high test-retest reliability included artifact reduction by ICA or wICA, data re-referenced using the CSD method, and FC measured by rMSC.

CONCLUSION & SIGNIFICANCE

This paper presents evidence for an EEG pre-processing pipeline that provides good detection of meaningful effects and high test-retest reliability for sensor space EEG alpha frequency FC.

Real-time optimization to enhance noninvasive cortical excitability assessment in the human dorsolateral prefrontal cortex

OBJECTIVE

We currently lack a robust noninvasive method to measure prefrontal excitability in humans. Concurrent TMS and EEG in the prefrontal cortex is usually confounded by artifacts. Here we asked if real-time optimization could reduce artifacts and enhance a TMS-EEG measure of left prefrontal excitability.

METHODS

This closed-loop optimization procedure adjusts left dlPFC TMS coil location, angle, and intensity in real-time based on the EEG response to TMS. Our outcome measure was the left prefrontal early (20-60 ms) and local TMS-evoked potential (EL-TEP).

RESULTS

In 18 healthy participants, this optimization of coil angle and brain target significantly reduced artifacts by 63 % and, when combined with an increase in intensity, increased EL-TEP magnitude by 75 % compared to a non-optimized approach.

CONCLUSIONS

Real-time optimization of TMS parameters during dlPFC stimulation can enhance the EL-TEP.

SIGNIFICANCE

Enhancing our ability to measure prefrontal excitability is important for monitoring pathological states and treatment response.

Intersubject neural similarity reveals the development trajectory of recognition memory in children

Recognition memory improves with child development, but the neural mechanisms underlying such improvement and the developmental variation remain poorly understood. Herein, we investigated how the neural representations during the encoding and retrieval phases of recognition memory change with age, using representational similarity analysis in a sample of children aged 6-13 years (n = 137). Our results indicated that the encoding and retrieval phases have distinct neural patterns of development. Similarly, using a model-free approach, we confirmed that there is a key developmental stage (about 9-10 years old) for the neural representation during the encoding phase, whereas the neural representation during the retrieval phase tends to be stable with child development. Additionally, we identified that the neural similarity between the encoding and retrieval phases in children is primarily located in the left parietal-occipital region. Overall, these findings refine the developmental process underlying memory representation and enhance our understanding of the neural mechanisms of recognition memory.

EEG time-frequency dynamics of early cognitive control development

Cognitive control is crucial for goal-directed behavior, and essential for other aspects of cognitive and socioemotional development. This review examines when and how the neural dynamics of cognitive control emerge and develop, focusing on electroencephalography measures used to study cognitive control in infants and children. We argue that time-frequency analyses are uniquely able to capture two distinct components of cognitive control: 1) the detection that control is needed, and 2) the instantiation of control. Starting in infancy and increasing across childhood and adolescence, studies suggest the signal strength and consistency of midfrontal theta and delta oscillations are involved in processes that detect the need for control. For control instantiation, there is evidence that theta band connectivity between midfrontal and lateral-frontal cortices is present from early childhood. There is also evidence for the involvement of midfrontal theta power in the instantiation of control in infancy. We further review emerging evidence that indicates individual differences in midfrontal theta are not only proximally related to behavior, but also sensitive to variations in early experience and risk for psychopathology, providing a neural mechanism linking early adversity to future psychopathology. We discuss needed future steps, including novel paradigms, computational models, and aperiodic/periodic modeling of EEG.

From vestibular implant to cortex: electrically evoked vestibular responses

PURPOSE

Understanding central vestibular pathways remains challenging and requires innovative measurement approaches. A vestibular implant offers unique access through specific electrical stimulation of the vestibular end organ. This study explored the feasibility of using vestibular implant stimulation to obtain vestibular evoked potentials, using electroencephalography (EEG).

METHODS

A vestibular implant was used in nine participants to evoke vestibular potentials by targeting the ampullary nerves of the semicircular canals. Short latency potentials were recorded using one channel EEG on all participants. In three participants, long latency potentials were recorded with 128 channel EEG. Responses were analyzed in terms of latency, shape, and location, and tested for correlation with stimulus intensity. EEG thresholds were compared with vestibular outcome thresholds (i.e., perception and vestibulo-ocular reflexes).

RESULTS

The measurement setup proved feasible for obtaining vestibular potentials. A consistent short latency response, identified as the vestibular brainstem response, was identified in five participants and across targeted nerves. Long latency responses revealed defined and localized independent components, with amplitudes correlating with stimulus intensity. Electrically evoked response thresholds matched thresholds of patient perception and eye movement recordings.

CONCLUSIONS

Vestibular implant stimulation elicited reproducible short and long latency responses. This approach creates new opportunities for investigating vestibular processing and evaluating vestibular implant responses.

Cortical adaptations in Tai Chi practitioners during sensory conflict: an EEG-based effective connectivity analysis of postural control

BACKGROUND

Tai Chi (TC) is recognized for enhancing balance and postural control. However, studies on its effects on the central nervous system are limited and often involve static experiments despite the dynamic nature of TC. This study addressed that gap by examining cortical network activity during dynamic, multisensory conflict balance tasks. We aimed to determine whether long-term TC practice leads to neuroplastic changes in brain connectivity that improve sensory integration for postural control.

METHODS

Fifty-two young adult participants (long-term TC practitioners = 22; non-practitioners = 30) performed balance tasks under sensory congruent and conflict conditions using a virtual reality headset with a rotating supporting surface. EEG was performed, and generalized partial directed coherence was used to assess directed functional connectivity in the mu rhythm (8-13 Hz) between predefined regions of interest (ROIs) in the cortex implicated in sensory and motor integration. Graph-theoretic measures (in-strength and out-strength) indexed the total incoming and outgoing connection strengths for each region. Statistical analysis used mixed-design ANOVAs (Group × Condition) to compare balance and connectivity measures.

RESULTS

TC practitioners demonstrated significantly better postural stability under both sensory conditions, with a reduced sway area. EEG analysis revealed that increased sensory conflict decreased the global efficiency of the visual integration network but increased that of the somatosensory integration network. Furthermore, TC practitioners demonstrated enhanced out-strength of the somatosensory cortex and lower out-strength of the right posterior parietal cortex (PPC) compared to non-practitioners.

CONCLUSIONS

Long-term TC practice is associated with quantifiable neuroplastic changes in mu-band cortical effective connectivity, specifically enhanced information outflow from somatosensory reduce parietal influence regions. Our findings demonstrate central mechanisms by which TC practice may improve balance, providing neuroengineering evidence for TC as a neuroplasticity-driven balance intervention.

One size fits null: attentional brain responses differ depending on insomnia subtype

STUDY OBJECTIVES

Event-related potential (ERP) studies on attentional brain processes in insomnia disorder (ID) have yielded inconsistent findings. Such inconsistencies may relate to small sample sizes, limited corrections for multiple comparisons, and the possibility of heterogeneity within the clinical population. We aimed to overcome these limitations by studying ERP responses both across and within subtypes in a larger sample of ID.

METHODS

ERPs were recorded in 201 participants with ID and 70 normal sleeper controls (NS) with an auditory oddball task. Participants with ID were subtyped using a validated multivariate trait profile. Analyses evaluated subtype-specific and nonspecific deviations using both conventional ERP components as well as cluster-based permutation tests.

RESULTS

All five subtypes were well-represented in the ID sample (subtypes 1-5 respectively N = 31, 83, 28, 29 and 19). ERP component analyses with false discovery rate corrections revealed no evidence for differences between the heterogeneous ID group and NS. However, subtype-specific analyses revealed that ERPs were significantly altered, but in different ways for different subtypes. Specifically, ERP component analyses revealed stronger N100 amplitudes for standards and deviants both in subtypes 2 and 3, and a lower P300 amplitude and longer P300 latency for deviants in subtype 3. Cluster-based permutation tests on ERPs corroborated the P300 amplitude effect for deviants in subtype 3, with subtype 3 and 4 additionally showing a smaller difference between deviant and standard P300 amplitudes.

CONCLUSION

Our findings indicate that ID is a heterogeneous disorder. Ignoring subtype identity dilutes ERP alterations occurring only in specific insomnia subtypes.

Evaluating machine- and deep learning approaches for artifact detection in infant EEG: classifier performance, certainty, and training size effects

Electroencephalography (EEG) is essential for studying infant brain activity but is highly susceptible to artifacts due to infants' movements and physiological variability. Manual artifact detection is labor-intensive and subjective, underscoring the need for automated methods. This study evaluates the performance of three machine learning classifiers - Random Forest (RF), Support Vector Machine (SVM), and a deep learning (DL) model - in detecting artifacts in infant EEG data without prior feature extraction. EEG data were collected from 294 infants (mean age 8.34 months) as part of the Bremen Initiative to Foster Early Childhood Development (BRISE). After preprocessing and manual annotation by an expert, a total of 66,851 epochs were analyzed, with 45% labeled as artifacts. The classifiers were trained on filtered EEG data without further feature extraction to directly handle the complex and noisy signals characteristic of infant EEG. Results. indicated that both the RF classifier and the DL model achieved high balanced accuracy scores (.873 and .881, respectively), substantially outperforming the SVM (.756). Further analysis showed that increasing classifier certainty improved accuracy but reduced the amount of data classified, offering a trade-off between precision and data coverage. Additionally, the RF classifier outperformed the DL model with smaller training datasets, while the DL model required larger datasets to achieve optimal performance. These findings demonstrate that RF and DL classifiers can effectively automate artifact detection in infant EEG data, reducing preprocessing time and enhancing consistency across studies. Implementing such automated methods could facilitate the inclusion of EEG in large-scale developmental research and improve reproducibility by standardizing preprocessing pipelines.

Learning Modulates Early Encephalographic Responses to Distracting Stimuli: A Combined SSVEP and ERP Study

Through experience, humans can learn to suppress locations that frequently contain distracting stimuli. However, the neural mechanism underlying learned suppression remains largely unknown. In this study, we combined steady-state visually evoked potentials (SSVEPs) with event-related potentials (ERPs) to investigate the mechanism behind statistically learned spatial suppression. Twenty-four male and female human participants performed a version of the additional singleton search task in which one location contained a distractor stimulus frequently. The search stimuli constantly flickered on-and-off the screen, resulting in steady-state entrainment. Prior to search onset, no differences in the SSVEP response were found, though a post hoc analysis did reveal proactive alpha lateralization. Following search onset, clear evoked differences in both the SSVEP and ERP signals emerged at the suppressed location relative to all other locations. Crucially, the early timing of these evoked modulations suggests that learned distractor suppression occurs at the initial stages of visual processing.

Effects of Matched and Mismatched Visual Flow and Gait Speeds on Human Electrocortical Spectral Power

Background/Objectives: Visuomotor integration relies on synchronized proprioceptive and visual feedback during visually guided locomotion. How the human brain processes unimodal or asynchronous multimodal inputs during locomotion is unclear. Methods: Using high-density mobile electroencephalography (EEG) and motion capture in a virtual reality environment, we investigated electrocortical responses during altered treadmill gait speeds (0.5 and 1.5 m/s) and visual flow speeds (0.5×, 1×, and 1.5× gait speed) among 13 healthy human subjects. Experimental conditions included passive viewing of a moving virtual environment, walking in a stationary virtual environment, and walking in a moving environment with synchronous and asynchronous visual flow. Results: At faster gait speed, we identified reduced premotor, sensorimotor, and visual electrocortical beta-band spectral power (13-30 Hz) and greater premotor cortex theta power (4-8 Hz). At faster visual flow speeds, we identified reduced sensorimotor electrocortical beta-band spectral power, reduced alpha (8-13 Hz) and beta power, and greater gamma-band power (30-50 Hz) from the visual cortex. During visual flow and gait speed mismatches, sensorimotor and parietal alpha- and beta-band electrocortical spectral power decreased at faster gait speed. During treadmill walking at 1.5 m/s, parietal electrocortical spectral power increased when visual flow exceeded gait speed. Conclusions: Electrical brain dynamics during human gait identified distinct neural circuits for integrating kinesthetic and visual information during visuomotor conflicts, gated by the parietal cortex.

EEG of the Dancing Brain: Decoding Sensory, Motor, and Social Processes during Dyadic Dance

Real-world social cognition requires processing and adapting to multiple dynamic information streams. Interpreting neural activity in such ecological conditions remains a key challenge for neuroscience. This study leverages advancements in denoising techniques and multivariate modeling to extract interpretable EEG signals from pairs of (male and/or female) participants engaged in spontaneous dyadic dance. Using multivariate temporal response functions (mTRFs), we investigated how music acoustics, self-generated kinematics, other-generated kinematics, and social coordination uniquely contributed to EEG activity. Electromyogram recordings from ocular, face, and neck muscles were also modeled to control for artifacts. The mTRFs effectively disentangled neural signals associated with four processes: (I) auditory tracking of music, (II) control of self-generated movements, (III) visual monitoring of partner movements, and (IV) visual tracking of social coordination. We show that the first three neural signals are driven by event-related potentials: the P50-N100-P200 triggered by acoustic events, the central lateralized movement-related cortical potentials triggered by movement initiation, and the occipital N170 triggered by movement observation. Notably, the (previously unknown) neural marker of social coordination encodes the spatiotemporal alignment between dancers, surpassing the encoding of self- or partner-related kinematics taken alone. This marker emerges when partners can see each other, exhibits a topographical distribution over occipital areas, and is specifically driven by movement observation rather than initiation. Using data-driven kinematic decomposition, we further show that vertical bounce movements best drive observers' EEG activity. These findings highlight the potential of real-world neuroimaging, combined with multivariate modeling, to uncover the mechanisms underlying complex yet natural social behaviors.

Decoding the Narcissistic Brain

There is a substantial knowledge gap in the narcissism literature: Less than 1% of the nearly 12,000 articles on narcissism have addressed its neural basis. To help fill this gap, we asked whether the multifacetedness of narcissism could be decoded from spontaneous neural oscillations. We attempted to do so by applying a machine learning approach (multivariate pattern analysis) to the resting-state EEG data of 162 participants who also completed a comprehensive battery of narcissism scales assessing agentic, admirative, rivalrous, communal, and vulnerable forms. Consistent with the agency-communion model of narcissism, agentic and communal forms of grandiose narcissism were reflected in distinct, non-overlapping patterns of spontaneous neural oscillations. Furthermore, consistent with a narcissistic admiration and rivalry concept model of narcissism, we observed largely non-overlapping patterns of spontaneous neural oscillations for admirative and rivalrous forms of narcissism. Vulnerable narcissism was negatively associated with power across fast and slow wave frequency bands. Taken together, the results suggest that the diverse forms of narcissism can be reliably predicted from spontaneous neural oscillations. The findings contribute to the burgeoning field of personality neuroscience.

EEG-based assessment of long-term vigilance and lapses of attention using a user-centered frequency-tagging approach

Objective.Sustaining vigilance over extended periods is crucial for many critical operations but remains challenging due to the cognitive resources required. Fatigue and other factors contribute to fluctuations in vigilance, causing attentional focus to drift from task-relevant information. Such lapses of attention, common in prolonged tasks, lead to decreased performance and missed critical information, with potentially serious consequences. Identifying physiological markers that predict inattention is key to developing preventive strategies.Approach.Previous research has established electroencephalography (EEG) responses to periodic visual stimuli, known as steady-state visual evoked potentials (SSVEP), as sensitive markers of attention. In this study, we evaluated a minimally intrusive SSVEP-based approach for tracking vigilance in healthy participants (N= 16) during two sessions of a 45 min sustained visual attention task (Mackworth's clock task). A 14 Hz frequency-tagging flicker was either superimposed on the task or absent.Main results.Results revealed that SSVEP responses were lower prior to lapses of attention, while other spectral EEG markers, such as frontal theta and parietal alpha activity, did not reliably distinguish between detected and missed attention probes. Importantly, the flicker did not affect task performance or participant experience.Significance.This non-intrusive frequency-tagging method provides a continuous measure of vigilance, effectively detecting attention lapses in prolonged tasks. It holds promise for integration into passive brain-computer interfaces, offering a practical solution for real-time vigilance monitoring in high-stakes settings like air traffic control or driving.

Translational approach for dementia subtype classification using convolutional neural network based on EEG connectome dynamics

Dementia spectrum disorders, characterized by progressive cognitive decline, pose a significant global health burden. Early screening and diagnosis are essential for timely and accurate treatment, improving patient outcomes and quality of life. This study investigated dynamic features of resting-state electroencephalography (EEG) functional connectivity to identify characteristic patterns of dementia subtypes, such as Alzheimer's disease (AD) and frontotemporal dementia (FD), and to evaluate their potential as biomarkers. We extracted distinctive statistical features, including mean, variance, skewness, and Shannon entropy, from brain connectivity measures, revealing common alterations in dementia, specifically a generalized disruption of Alpha-band connectivity. Distinctive characteristics were found, including generalized Delta-band hyperconnectivity with increased complexity in AD and disrupted phase-based connectivity in Theta, Beta, and Gamma bands for FD. We also employed a convolutional neural network model, enhanced with these dynamic features, to differentiate between dementia subtypes. Our classification models achieved a multiclass classification accuracy of 93.6% across AD, FD, and healthy control groups. Furthermore, the model demonstrated 97.8% and 96.7% accuracy in differentiating AD and FD from healthy controls, respectively, and 97.4% accuracy in classifying AD and FD in pairwise classification. These establish a high-performance deep learning framework utilizing dynamic EEG connectivity patterns as potential biomarkers, offering a promising approach for early screening and diagnosis of dementia spectrum disorders using EEG. Our findings suggest that analyzing brain connectivity dynamics as a network and during cognitive tasks could offer more valuable information for diagnosis, assessing disease severity, and potentially identifying personalized neurological deficits.

Effects of theta burst stimulation on the Parkinsonian gait disorder and cortical gait-network activity

BackgroundThe Parkinsonian gait disorder and freezing of gait (FoG) are challenging symptoms of Parkinson's disease (PD).ObjectiveTo assess the effect of subthalamic theta burst deep brain stimulation (TBS-DBS) on the Parkinsonian gait performance in real-world conditions and cortical activity indexed by mobile EEG.MethodsIn this monocentric, randomised, double-blind, short-term study, 12 age-matched controls (11 male, age 59 ± 8 years) and 15 PD participants (14 male, age 62 ± 9 years, disease duration 15 ± 6 years) with subthalamic stimulation (76 ± 39 months) were assessed with clinical scores (FoG-Course, MDS-UPDRS) and a standardized gait course simulating everyday life situations. Three DBS algorithms were applied in a randomized order with intertrial waiting periods of 30 min: (1) OFF-DBS; (2) cDBS; (3) TBS-DBS (interburst frequency 5 Hz, intraburst frequency 200 Hz) with regular medication. During the standardized gait course a mobile, 24-channel EEG system and 6 wearable axial kinematic sensors were used.ResultsThe primary outcome, the relative change of FoG-Course by DBS, was not superior with TBS-DBS compared to cDBS in the entire sample. Seven of fifteen PD participants rated subjectively TBS-DBS equal or better than cDBS ("TBS-preference group"). EEG recordings revealed movement-induced alpha and beta suppression in premotor and motor cortex in both cDBS and TBS-DBS conditions in PD with slightly different patterns between the DBS modes.ConclusionsIn this pilot trial, TBS-DBS showed benefits in the subjective perception of gait in a subgroup of PD patients accompanied by specific cortical network changes. TBS-DBS merits further investigation in future larger cohort studies with longer observation periods.

Frontal midline theta power accounts for inter-individual differences in motor learning ability

Recent neurophysiological studies have demonstrated that frontal midline theta (FMT) activity plays a significant role in motor learning. One of the key challenges in motor learning is to understand the interindividual variability in learning proficiency rates, yet the underlying neural mechanisms remain unclear. To address this open question, this study recorded electroencephalogram activity from twenty-one healthy participants during a visuomotor tracking task to investigate whether modulation of FMT power and the theta phase synchronization across trials (theta phase consistency) during motor preparation could explain individual differences in learning proficiency. We found a significant positive correlation between increased FMT power during motor preparation and learning proficiency rates. Specifically, individuals with greater FMT power exhibited faster learning rates. In contrast, no significant correlation was observed between the consistency of the theta phase during motor preparation and learning proficiency. Together, these findings highlight that the FMT power, rather than phase synchrony, is closely associated with motor learning efficiency. This study provides a novel perspective for understanding the causes of individual differences in motor learning and further corroborates the previous evidence showing FMT power contributes to motor learning processes.

Spatiotemporal processing of real faces is modified by visual sensing

Live human faces, when engaged as visual stimuli, recruit unique and extensive patterns of neural activity. However, the underlying neural mechanisms that underly these live face-to-face processes are not known. We hypothesized that the neural correlates for live face processes are modulated by both spatial and temporal features of the live faces as well as visual sensing parameters. Hemodynamic signals detected by functional near infrared spectroscopy (fNIRS) were acquired concurrently with co-activated electroencephalographic (EEG) and eye-tracking signals during interactive gaze at a live human face or gaze at a human-like robot face. Regression of the fNIRS signals with two eye-gaze variables, fixation duration and dwell time, revealed separate regions of neural correlates, right supramarginal gyrus (lateral visual stream) and right inferior parietal sulcus (dorsal visual stream), respectively. These two areas served as the regions of interest for the EEG analysis. Standardized low-resolution brain electromagnetic tomography (sLORETA) was applied to determine theta (4 - 7 Hz) and alpha (8-13 Hz) oscillatory activity in these regions. Variations in oscillatory patterns corresponding to the neural correlates of the visual sensing parameters suggest an increase in spatial binding for the dorsal relative to the lateral regions of interest during live face-to-face visual stimulation.

The Administration of Ketamine Is Associated with Dose-Dependent Stabilization of Cortical Dynamics in Humans

During wakefulness, external stimuli elicit conscious experiences. In contrast, dreams and drug-induced dissociated states are characterized by vivid internally generated conscious experiences and reduced ability to perceive external stimuli. Understanding the physiological distinctions between normal wakefulness and dissociated states may therefore disambiguate signatures of responsiveness to external stimuli from those that underlie conscious experience. The hypothesis that conscious experiences are associated with brain criticality has received considerable theoretical and experimental support. Consistent with this hypothesis, statistical signatures of criticality are similar in normal wakefulness and dissociative states but are abolished in dreamless sleep and under anesthesia. Thus, while statistical measures of criticality are associated with the ability to have conscious experience, they do not readily distinguish between perception of the external world from internally generated percepts. Here, we investigate distinct, dynamical, signatures of criticality during escalating ketamine doses in high-density EEG in human male volunteers. We show that during normal wakefulness, EEG is found at a critical point between damped and exploding oscillations. With increasing doses of ketamine, as dissociative symptoms intensify, activity is progressively stabilized-most prominently at higher frequencies. We also show that stabilization is a more reliable marker of the effects of ketamine than conventional measures such as power spectra. These findings suggest that stabilization of cortical dynamics correlates with decreased ability to respond to and perceive external stimuli rather than the ability to have conscious experiences per se. Altogether, these results suggest that combining statistical and dynamical criticality measures may distinguish wakefulness, dissociation, and unconsciousness.

Dispositional empathy as a driver of inter-individual neural phase synchrony

In social neuroscience inter-individual neural phase synchrony has become a widely studied phenomenon, and has been linked to a variety of social outcomes. However, the cognitive processes underlying the emergence of this synchrony remain largely unknown. In this study, we used a two-person face-to-face collaborative task to investigate the potential of dispositional empathy-the general tendency of an individual to imagine and experience the feelings and experiences of others-as a driver of inter-individual neural synchrony during collaboration. Electroencephalography from 46 participants was used to examine phase synchrony, measured as circular correlation coefficients, between the two interacting individuals' brain signals. We found significant inter-brain synchrony in the high alpha, beta, and gamma frequency bands. This synchrony was particularly prominent in the inter-brain connectivity measured between central regions and a range of other regions. Furthermore, a specific dimension of dispositional empathy, namely the collaborators' tendency to transpose themselves imaginatively into the feelings and actions of others, predicted the level of synchrony in the beta and gamma frequency bands. Hence, we demonstrate that dispositional empathy plays a significant role in the emergence of inter-individual neural phase synchrony.

Identifying EEG biomarkers of sense of embodiment in virtual reality: insights from spatio-spectral features

The Sense of Embodiment (SoE) refers to the subjective experience of perceiving a non-biological body part as one's own. Virtual Reality (VR) provides a powerful platform to manipulate SoE, making it a crucial factor in immersive human-computer interaction. This becomes particularly relevant in Electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs), especially motor imagery (MI)-BCIs, which harness brain activity to enable users to control virtual avatars in a self-paced manner. In such systems, a strong SoE can significantly enhance user engagement, control accuracy, and the overall effectiveness of the interface. However, SoE assessment remains largely subjective, relying on questionnaires, as no definitive EEG biomarkers have been established. Additionally, methodological inconsistencies across studies introduce biases that hinder biomarker identification. This study aimed to identify EEG-based SoE biomarkers by analyzing frequency band changes in a combined dataset of 41 participants under standardized experimental conditions. Participants underwent virtual SoE induction and disruption using multisensory triggers, with a validated questionnaire confirming the illusion. Results revealed a significant increase in Beta and Gamma power over the occipital lobe, suggesting these as potential EEG biomarkers for SoE. The findings underscore the occipital lobe's role in multisensory integration and sensorimotor synchronization, supporting the theoretical framework of SoE. However, no single frequency band or brain region fully explains SoE. Instead, it emerges as a complex, dynamic process evolving across time, frequency, and spatial domains, necessitating a comprehensive approach that considers interactions across multiple neural networks.

Characterizing the heterogeneity of neurodegenerative diseases through EEG normative modeling

Neurodegenerative diseases like Parkinson's (PD) and Alzheimer's (AD) exhibit considerable heterogeneity of functional brain features within patients, complicating diagnosis and treatment. Here, we use electroencephalography (EEG) and normative modeling to investigate neurophysiological mechanisms underpinning this heterogeneity. Resting-state EEG data from 14 clinical units included healthy adults (n = 499) and patients with PD (n = 237) and AD (n = 197), aged over 40. Spectral and source connectivity analyses provided features for normative modeling, revealing significant, frequency-dependent EEG deviations with high heterogeneity in PD and AD. Around 30% of patients exhibited spectral deviations, while ~80% showed functional source connectivity deviations. Notably, the spatial overlap of deviant features did not exceed 60% for spectral and 25% for connectivity analysis. Furthermore, patient-specific deviations correlated with clinical measures, with greater deviations linked to worse UPDRS for PD (⍴ = 0.24, p = 0.025) and MMSE for AD (⍴ = -0.26, p = 0.01). These results suggest that EEG deviations could enrich individualized clinical assessment in Precision Neurology.

Contextual expectations in the real-world modulate low-frequency neural oscillations

Objects in expected locations are recognised faster and more accurately than objects in incongruent environments. This congruency effect has a neural component, with increased activity for objects in incongruent environments. Studies have increasingly shown differences between neural processes in realistic environments and tasks, and neural processes in the laboratory. Here, we aimed to push the boundaries of traditional cognitive neuroscience by tracking the congruency effect for objects in real-world environments, outside of the laboratory. We investigated how neural activity is modulated when objects are placed in real environments using augmented reality while recording mobile EEG. Participants approached, viewed, and rated how congruent they found the objects with the environment. We found significant differences in ERPs and higher theta-band power for objects in incongruent contexts than objects in congruent contexts. This demonstrates that real-world contexts impact how objects are processed, and that mobile brain imaging and augmented reality are effective tools to study cognition in the wild.

Single-microphone deep envelope separation based auditory attention decoding for competing speech and music

Objective.In this study, we introduce an end-to-end single microphone deep learning system for source separation and auditory attention decoding (AAD) in a competing speech and music setup. Deep source separation is applied directly on the envelope of the observed mixed audio signal. The resulting separated envelopes are compared to the envelope obtained from the electroencephalography (EEG) signals via deep stimulus reconstruction, where Pearson correlation is used as a loss function for training and evaluation.Approach.Deep learning models for source envelope separation and AAD are trained on target/distractor pairs from speech and music, covering four cases: speech vs. speech, speech vs. music, music vs. speech, and music vs. music. We convolve 10 different HRTFs with our audio signals to simulate the effects of head, torso and outer ear, and evaluate our model's ability to generalize. The models are trained (and evaluated) on 20 s time windows extracted from 60 s EEG trials.Main results.We achieve a target Pearson correlation and accuracy of 0.122% and 82.4% on the original dataset and an average target Pearson correlation and accuracy of 0.106% and 75.4% across the 10 HRTF variants. For the distractor, we achieve an average Pearson correlation of 0.004. Additionally, our model gives an accuracy of 82.8%, 85.8%, 79.7% and 81.5% across the four aforementioned cases for speech and music. With perfectly separated envelopes, we can achieve an accuracy of 83.0%, which is comparable to the case of source separated envelopes.Significance.We conclude that the deep learning models for source envelope separation and AAD generalize well across the set of speech and music signals and HRTFs tested in this study. We notice that source separation performs worse for a mixed music and speech signal, but the resulting AAD performance is not impacted.

Cortical Processing and Lower Limb Muscle Activity Increase During Bodyweight Supported Treadmill Locomotion Underwater Compared to On-Land

Body weight support (BWS) systems are commonly used during gait rehabilitation to assist individuals with motor impairments. Traditional approaches involve mechanical unloading through overhead harness systems or buoyancy-assisted underwater walking, each providing unique biomechanical and neuromuscular advantages. The effects of external loading conditions on neural and muscular dynamics are not well understood. We evaluated electrical brain and lower limb muscle activities during treadmill walking with mechanical BWS on-land and underwater. Here, we show that contrasting BWS mechanisms modulate frontoparietal electrocortical spectral power and lower limb myoelectric activity. Underwater walking reduced frontoparietal alpha (8-13 Hz) and beta band power (13-30 Hz) and increased rectus femoris, biceps femoris, tibialis anterior, and lateral gastrocnemius muscle activities compared to walking on-land treadmill, with and without mechanical unloading. Discernible changes in sensorimotor processing and muscle activations during bodyweight supported treadmill walking can provide objective biomarkers to help refine personalized rehabilitation strategies.

Did you say brain or brave? event-related potentials reveal the central role of phonological prediction in false hearing

In the current paper, we report the results from two event-related brain potential (ERP) experiments that examined the time-course of false hearing (i.e., hearing one word when a different one was presented). Target words were presented in background noise whereas the preceding context (either a semantic prime word or a constraining sentence) was not. Participants routinely experienced false hearing, reporting a predictable word when an incongruent, phonological lure was presented. We found that the N400 to falsely heard words was similar to when a predictable word was presented even though a phonological lure was presented. Additionally, the N400 response to correctly identified phonological lures was significantly delayed compared to the response to incongruent words that shared no phonological relation to predictable words, suggesting the listeners engaged in phonological prediction. Altogether, the findings from the current study provide evidence that listeners' engagement in phonological prediction can lead to misperception.

SAGN: Sparse Adaptive Gated Graph Neural Network With Graph Regularization for Identifying Dual-View Brain Networks

Due to the absence of a gold standard for threshold selection, brain networks constructed with inappropriate thresholds risk topological degradation or contain noise connections. Therefore, graph neural networks (GNNs) exhibit weak robustness and overfitting problems when identifying brain networks. Furthermore, existing studies have predominantly focused on strongly coupled connections, neglecting substantial evidence from other intricate systems that highlight the value of weakly coupled connections. Consequently, the potential of weakly coupled brain networks remains untapped. In this study, we pioneeringly construct weakly coupled brain networks and validate their values in emotion identification tasks. Subsequently, we propose a sparse adaptive gated GNN (SAGN) that can simultaneously perceive the valuable topology of dual-view (i.e., strongly coupled and weakly coupled) brain networks. The SAGN contains a sparse adaptive global receptive field. Moreover, SAGN employs a gated mechanism with feature enhancement and adaptive noise suppression capabilities. To address the lack of inductive bias and the large capacity of SAGN, a graph regularization term built with prior topology of dual-view brain networks is introduced to enhance generalization. Besides a public dataset (SEED), we also built a custom dataset (MuSer) with 60 subjects to evaluate weakly coupled brain networks' value and validate the SAGN's performance. Experiments demonstrate that brain physiological patterns associated with different emotional states are separable and rooted in weakly coupled brain networks. In addition, SAGN exhibits excellent generalization and robustness in identifying brain networks.

Exploring the impact of manual and automatic EEG pre-processing methods on interpersonal neural synchrony measures in parent-infant hyperscanning studies

BACKGROUND

Electroencephalograph (EEG) hyperscanning allows studying Interpersonal Neural Synchrony (INS) between two or more individuals across different social conditions, including parent-infant interactions. Signal pre-processing is crucial to optimize computation of INS estimates; however, few attempts have been made at comparing the impact of different dyadic EEG data pre-processing methods on INS estimates.

NEW METHODS

EEG data collected on 31 mother-infant dyads (8-10 months) engaged in a Face-to-Face Still-Face Procedure were pre-processed with two versions of the same pipeline, the "automated" and the "manual". Cross-frequency PLV in the theta (3-5 Hz, 4-7 Hz) and alpha (6-9 Hz, 8-12 Hz) frequency bands were computed after automated and manual pre-processing and compared through Pearson's correlations and Repeated Measures ANOVAs.

RESULTS

PLVs computed in the theta, but not alpha, frequency band were significantly higher after automated pre-processing than after manual pre-processing. Moreover, the automated pipeline rejected a significantly lower percentage of ICs and epochs compared to the manual pipeline.

COMPARISON WITH EXISTING METHODS

While no direct comparison with existing dyadic EEG data pre-processing pipelines was made, this is the first study assessing the impact of different methodological decisions, particularly of the degree of pre-processing automatization, on cross-frequency PLV computed on a dataset of parent-infant dyads.

CONCLUSIONS

Non-directional phase-based INS indexes such as the PLV seem to be affected by the degree of automatization of the pre-processing pipeline. Future research should strive for standardization of dyadic EEG pre-processing methods.

Probing the Neurodynamic Mechanisms of Cognitive Flexibility in Depressed Individuals with Autism Spectrum Disorder

Introduction: Autism spectrum disorder (ASD) is characterized by deficits in social behavior and executive function (EF), particularly in cognitive flexibility. Whether transcranial magnetic stimulation (TMS) can improve cognitive outcomes in patients with ASD remains an open question. We examined the acute effects of prefrontal TMS on cortical excitability and fluid cognition in individuals with ASD who underwent TMS for refractory major depression. Methods: We analyzed data from an open-label pilot study involving nine participants with ASD and treatment-resistant depression who received 30 sessions of accelerated theta burst stimulation of the dorsolateral prefrontal cortex, either unilaterally or bilaterally. Electroencephalography data were collected at baseline and 1, 4, and 12-weeks posttreatment and analyzed using a mixed-effects linear model to assess changes in regional cortical excitability using three models of spectral parametrization. Fluid cognition was measured using the National Institutes of Health Toolbox Cognitive Battery. Results: Prefrontal TMS led to a decrease in prefrontal cortical excitability and an increase in right temporoparietal excitability, as measured using spectral exponent analysis. This was associated with a significant improvement in the NIH Toolbox Fluid Cognition Composite score and the Dimensional Change Card Sort subtest from baseline to 12 weeks posttreatment (t = 3.79, p = 0.005, n = 9). Improvement in depressive symptomatology was significant (HDRS-17, F (3, 21) = 28.49, p < 0.001) and there was a significant correlation between cognitive improvement at week 4 and improvement in depression at week 12 (r = 0.71, p = 0.05). Conclusion: These findings link reduced prefrontal excitability in patients with ASD and improvements in cognitive flexibility. The degree to which these mechanisms can be generalized to ASD populations without Major Depressive Disorder remains a compelling question for future research.

Juvenile Myoclonic Epilepsy Imaging Endophenotypes and Relationship With Cognition and Resting-State EEG

Structural neuroimaging studies of patients with Juvenile Myoclonic Epilepsy (JME) typically present two findings: 1-volume reduction of subcortical gray matter structures, and 2-abnormalities of cortical thickness. The general trend has been to observe increased cortical thickness primarily in medial frontal regions, but heterogeneity across studies is common, including reports of decreased cortical thickness. These differences have not been explained. The cohort of patients investigated here originates from the Juvenile Myoclonic Epilepsy Connectome Project, which included comprehensive neuropsychological testing, 3 T MRI, and high-density 256-channel EEG. 64 JME patients aged 12-25 and 41 age and sex-matched healthy controls were included. Data-driven approaches were used to compare cortical thickness and subcortical volumes between the JME and control participants. After differences were identified, supervised machine learning was used to confirm their classification power. K-means clustering was used to generate imaging endophenotypes, which were then correlated with cognition, EEG frequency band lagged coherence from resting state high-density EEG, and white and grey matter based spatial statistics from diffusion imaging. The volumes of subcortical gray matter structures, particularly the thalamus and the motor-associated thalamic nuclei (ventral anterior), were found to be smaller in JME. In addition, the right hemisphere (primarily) sulcal pre-motor cortex was abnormally thicker in an age-dependent manner in JME with an asymmetry in the pre-motor cortical findings. These results suggested that for some patients JME may be an asymmetric disease, at least at the cortical level. Cluster analysis revealed three discrete imaging endophenotypes (left, right, symmetric). Clinically, the groups were not substantially different except for cognition, where left hemisphere disease was linked with a lower performance on a general cognitive factor ("g"). HD-EEG demonstrated statistically significant differences between imaging endophenotypes. Tract-based spatial statistics showed significant changes between endophenotypes as well. The left dominant disease group exhibited diffuse white matter changes. JME patients present with heterogeneity in underlying imaging endophenotypes that are defined by the presence and laterality of asymmetric abnormality at the level of the pre-motor sulcal cortex; these endophenotypes are linked to orderly relationships with cognition, EEG, and white matter pathology. The relationship of JME's adolescent onset, age-dependent cortical thickness loss, and seizure upon awakening all suggest that synaptic pruning may be a key element in the pathogenesis of JME. Individualized treatment approaches for neuromodulation are needed to target the most relevant cortical and subcortical structures as well as develop disease-modifying and neuroprotective strategies.

Human Attachment and the Electrophysiological Dynamics of Emotion Regulation: An Event-Related Potential Study

Emotion regulation is pivotal in human interactions and well-being. Modulating one's emotional state is intricately linked with psychological, behavioral, and physiological responses. Extensive research has explored how individuals with varying attachment orientations manage emotions, predominantly through self-report measures and behavioral assessments. However, the influence of attachment orientations on temporal electrophysiological dynamics during emotion regulation tasks remains underexplored. Here, 90 adults' EEG brain activity was recorded while they engaged in tasks of attending to, reappraising, or suppressing emotions elicited by unpleasant images. Their attachment orientations were assessed using the Experiences in Close Relationships-12 (ECR-12) questionnaire to explore the association between Late Positive Potential (LPP) and attachment anxiety and avoidance amidst the deployment of emotion regulation strategies. Using Linear Mixed-Effects Model analysis, our results revealed a lower amplitude of the LPP during cognitive reappraisal, suggesting the efficacy of this strategy in diminishing emotional intensity. Moreover, higher attachment anxiety exhibited increased LPP amplitude during both Reappraisal and Suppression, as well as during the negative natural condition, indicating heightened emotional responses. This study provides novel insights into the relationship between attachment orientations and emotion regulation, as evidenced by EEG-based measurements of the LPP. The findings indicate that individuals with higher attachment anxiety display distinct electrophysiological responses, particularly in emotional scenarios.

Age-Related Differences in Neural Correlates of Auditory Spatial Change Detection in Real and Virtual Environments

Although virtual environments are increasingly used in research, their ecological validity in simulating real-life scenarios, for example, to investigate cognitive changes in aging populations, remains relatively unexplored. This study aims to evaluate the validity of a virtual environment for investigating auditory spatial change detection in younger and older adults. This evaluation was performed by comparing behavioral and neurophysiological responses between real and virtual environments. Participants completed an auditory change detection task, identifying sound source position changes relative to a reference position. In the real environment, sounds were presented through physical loudspeakers in a reverberant room. In the virtual environment, stimuli were delivered through headphones, accompanied by a head-mounted display showing a visual replica of the room. Participants showed higher accuracy for azimuth than for distance changes, regardless of age or environment, emphasizing humans' larger sensitivity to lateralized sounds. Event-related potentials were mostly consistent across environments, with significantly higher N1 and P2 amplitudes in older compared with younger adults. Mismatch negativity was reduced in older adults, and both reduced and delayed in the virtual environment. The P3b showed larger amplitudes and shorter latencies for azimuth changes, reflecting greater salience of directional cues, whereas responses in the virtual environment were slightly diminished, especially among older adults. Bayesian analyses validated the observed effects. Results support virtual environments as reliable tools for exploring spatial perception and underlying neural and behavioral processes in realistic contexts. Furthermore, differences in the processing of spatial changes in azimuth and distance, as well as age-related effects, could be highlighted.