Automatic classification of artifactual ICA-components for artifact removal in EEG signals

The Supplementary Motor Area as a Flexible Hub Mediating Behavioral and Neuroplastic Changes in Motor Sequence Learning: A TMS and TMS-EEG Study

Attempts have been made to modulate motor sequence learning (MSL) through repetitive transcranial magnetic stimulation, targeting different sites within the sensorimotor network. However, the target with the optimum modulatory effect on neural plasticity associated with MSL remains unclarified. This study was therefore designed to compare the role of the left primary motor cortex and the left supplementary motor area proper (SMAp) in modulating MSL across different complexity levels and for both hands, as well as the associated neuroplasticity by applying intermittent theta burst stimulation together with the electroencephalogram and concurrent transcranial magnetic stimulation. Our data demonstrated the role of SMAp stimulation in modulating neural communication to support MSL, which is achieved by facilitating regional activation and orchestrating neural coupling across distributed brain regions, particularly in interhemispheric connections. These findings may have important clinical implications, particularly for motor rehabilitation in populations such as post-stroke patients.

AADNet: Exploring EEG Spatiotemporal Information for Fast and Accurate Orientation and Timbre Detection of Auditory Attention Based on a Cue-Masked Paradigm

Auditory attention decoding from electroencephalogram (EEG) could infer to which source the user is attending in noisy environments. Decoding algorithms and experimental paradigm designs are crucial for the development of technology in practical applications. To simulate real-world scenarios, this study proposed a cue-masked auditory attention paradigm to avoid information leakage before the experiment. To obtain high decoding accuracy with low latency, an end-to-end deep learning model, AADNet, was proposed to exploit the spatiotemporal information from the short time window of EEG signals. The results showed that with a 0.5-second EEG window, AADNet achieved an average accuracy of 93.46% and 91.09% in decoding auditory orientation attention (OA) and timbre attention (TA), respectively. It significantly outperformed five previous methods and did not need the knowledge of the original audio source. This work demonstrated that it was possible to detect the orientation and timbre of auditory attention from EEG signals fast and accurately. The results are promising for the real-time multi-property auditory attention decoding, facilitating the application of the neuro-steered hearing aids and other assistive listening devices.

Peak alpha frequency is related to the degree of widespread pain, but not pain intensity or duration, among people with urologic chronic pelvic pain syndrome

Introduction

Effective prevention and management strategies for chronic pain remain elusive. This has prompted investigations into biomarkers to better understand the mechanisms underlying pain development and persistence. One promising marker is low peak alpha frequency (PAF), an electroencephalography (EEG) measure that has been associated with increased sensitivity during acute experimental pain. However, findings regarding the relationship between PAF and chronic pain are variable, potentially due to disparate levels of central sensitization among chronic pain populations. This is evidenced by the variable extent of widespread pain, a phenotypic marker for central sensitization, observed across individuals with chronic pain.

Objective

To explore the impact of widespread pain on PAF among people with chronic pain.

Method

Thirty-eight individuals with urologic chronic pelvic pain syndrome were categorized as having widespread (n = 24) or localized (n = 14) pain based upon self-reported body maps. Electroencephalography data were collected under resting conditions, and PAF was determined using spectral analysis.

Results

Participants with widespread pain had a significantly lower global average PAF than those with localized pain, after controlling for age and sex. This relationship persisted even when accounting for pain intensity and duration. Peak alpha frequency differences were observed across all EEG electrodes, particularly in the sensorimotor and occipital regions.

Conclusion

Preliminary findings suggest that PAF may represent a potential biomarker for central sensitization in chronic pain, highlighting the importance of considering pain distribution in chronic pain research. Future studies with larger samples should investigate the neural mechanisms underlying these observations and the clinical utility of PAF in diverse chronic pain populations.

Protocol for semi-automatic EEG preprocessing incorporating independent component analysis and principal component analysis

Preprocessing is a critical yet challenging step in electroencephalography (EEG) research due to its significant potential impact on results. We present a protocol for semi-automatic EEG preprocessing incorporating independent component analysis (ICA) and principal component analysis (PCA) with step-by-step quality checking to ensure removal of large-amplitude artifacts. We describe steps for interpolating bad channels, removal of major artifacts by ICA and PCA correction, and exporting processed data. This protocol produced consistent results from users with a broad range of experience.

How much data is enough? Optimization of data collection for artifact detection in EEG recordings

Objective.Electroencephalography (EEG) is a widely used neuroimaging technique known for its cost-effectiveness and user-friendliness. However, the presence of various artifacts leads to a poor signal-to-noise ratio, limiting the precision of analyses and applications. The proposed work focuses on the electromyography (EMG) artifacts, which are among the most challenging biological artifacts. The currently reported EMG artifact cleaning performance largely depends on the data used for validation, and in the case of machine learning approaches, also on the data used for training. The data are typically gathered either by recruiting subjects to perform specific EMG artifact tasks or by integrating existing datasets. Prevailing approaches, however, tend to rely on intuitive, concept-oriented data collection with minimal justification for the selection of artifacts and their quantities. Given the substantial costs associated with biological data collection and the pressing need for effective data utilization, we propose an optimization procedure for data-oriented data collection design using deep learning-based artifact detection.Approach.We apply a binary classification differentiating between artifact epochs (time intervals containing EMG artifacts) and non-artifact epochs (time intervals containing no EMG artifact) using three different neural architectures. Our aim is to minimize data collection efforts while preserving the cleaning efficiency.Main results.We were able to reduce the number of EMG artifact tasks from twelve to three and decrease repetitions of isometric contraction tasks from ten to three or sometimes even just one.Significance.Our work addresses the need for effective data utilization in biological data collection, offering a systematic and dynamic quantitative approach. By providing clear justifications for the choices of artifacts and their quantity, we aim to guide future studies toward more effective and economical data collection in EEG and EMG research.

A human electrophysiological biomarker of Fragile X Syndrome is shared in V1 of Fmr1 KO mice and caused by loss of FMRP in cortical excitatory neurons

Predicting clinical therapeutic outcomes from preclinical animal studies remains an obstacle to developing treatments for neuropsychiatric disorders. Electrophysiological biomarkers analyzed consistently across species could bridge this divide. In humans, alpha oscillations in the resting state electroencephalogram (rsEEG) are altered in many disorders, but these disruptions have not yet been characterized in animal models. Here, we employ a uniform analytical method to show in males with fragile X syndrome (FXS) that the slowed alpha oscillations observed in adults are also present in children and in visual cortex of adult and juvenile Fmr1 -/y mice. We find that alpha-like oscillations in mice reflect the differential activity of two classes of inhibitory interneurons, but the phenotype is caused by deletion of Fmr1 specifically in cortical excitatory neurons. These results provide a framework for studying alpha oscillation disruptions across species, advance understanding of a critical rsEEG signature in the human brain and inform the cellular basis for a putative biomarker of FXS.

Neurophysiological insights into catecholamine-dependent tDCS modulation of cognitive control

Goal-directed behavior requires resolving both consciously and subconsciously induced response conflicts. Neuronal gain control, which enhances processing efficacy, is crucial for conflict resolution and can be increased through pharmacological or brain stimulation interventions, though it faces inherent physical limits. This study examined the effects of anodal transcranial direct current stimulation (atDCS) and methylphenidate (MPH) on conflict processing. Healthy adults (n = 105) performed a flanker task, with electroencephalography (EEG) used to assess alpha and theta band activity (ABA, TBA). Results showed that combining atDCS with MPH enhanced cognitive control and reduced response conflicts more effectively than atDCS alone, particularly when both conflict types co-occurred. Both atDCS and atDCS + MPH exhibited similar task-induced ABA and TBA modulations in the (pre)supplementary motor area, indicating heightened gain control. Overlapping neuroanatomical effects in mid-superior frontal areas suggest that atDCS and MPH share a common neuronal mechanism of gain control, especially in high-conflict/-demand situations.

Correlations of pilot trainees' brainwave dynamics with subjective performance evaluations: insights from EEG microstate analysis

Objective

This study aims to investigate the relationship between the subjective performance evaluations on pilot trainees' aircraft control abilities and their brainwave dynamics reflected in the results from EEG microstate analysis. Specifically, we seek to identify correlations between distinct microstate patterns and each dimension included in the subjective flight control evaluations, shedding light on the neurophysiological mechanisms underlying aviation expertise and possible directions for future improvements in pilot training.

Background

Proficiency in aircraft control is crucial for aviation safety and modern aviation where pilots need to maneuver aircraft through an array of situations, ranging from routine takeoffs and landings to complex weather conditions and emergencies. However, the neurophysiological aspects of aviation expertise remain largely unexplored. This research bridges the gap by examining the relationship between pilot trainees' specific brainwave patterns and their subjective evaluations of flight control levels, offering insights into the cognitive underpinnings of pilot skill efficiency and development.

Method

EEG microstate analysis was employed to examine the brainwave dynamics of pilot trainees while they performed aircraft control tasks under a flight simulator-based pilot training process. Trainees' control performance was evaluated by experienced instructors across five dimensions and their EEG data were analyzed to investigate the associations between the parameters of specific microstates with successful aircraft control.

Results

The experimental results revealed significant associations between aircraft control levels and the parameters of distinct EEG microstates. Notably, these associations varied across control dimensions, highlighting the multifaceted nature of control proficiency. Noteworthy correlations included positive correlations between microstate class E and class G with aircraft control, emphasizing the role of attentional processes, perceptual integration, working memory, cognitive flexibility, decision-making, and executive control in aviation expertise. Conversely, negative correlations between microstate class C and class F with aircraft control indicated links between pilot trainees' cognitive control and their control performance on flight tasks.

Conclusion

The findings underscore the multidimensional nature of aircraft control proficiency and emphasize the significance of attentional and cognitive processes in achieving aviation expertise. These neurophysiological markers offer a basis for designing targeted pilot training programs and interventions to enhance trainees' aircraft control skills.

Event-related theta synchronization over sensorimotor areas differs between younger and older adults and is related to bimanual motor control

When engaged in dynamic or continuous movements, action initiation involves modifying an ongoing motor program rather than initiating it from rest. Event-related theta synchronization over sensorimotor areas is a neurophysiological marker for modifying motor programs. We used electroencephalography (EEG) to examine how task complexity and age affect event-related synchronization (ERS) in the theta band during a dynamic bimanual, visuomotor pinch force task. Older (mean age = 68) and younger (mean age = 26) participants performed symmetric (SYM) and asymmetric (ASYM) bimanual pinch force adjustments. Trials began with a visually cued contraction from a baseline force to a novel target force (P1). Force had to be maintained at the target until a visually cued return to the familiar baseline (P2). Older adults reacted slower across task conditions, and their accuracy decreased more when shifting from the SYM to the ASYM condition. Older adults also displayed lower theta ERS across conditions. Additionally, older adults were not able to modulate theta expression based on whether a force change was initiated to a novel target or back to baseline. Younger adults showed significantly stronger theta ERS after P1-cues compared to P2-cues, while the theta response to P1 and P2 cues was not different in older adults. Older adults also showed stronger lateralization, displaying higher theta ERS over the dominant motor cortex. Finally, event-related theta synchronization appeared to be behaviorally relevant across groups and correlated with task performance. Together, the results show that theta ERS over sensorimotor areas is a strong, age-sensitive marker of dynamic pinch force adjustments showing an age-related reduction in specificity with reduced context-dependent modulations and more imbalanced bimanual activation.

Peak alpha frequency differs between chronic back pain and chronic widespread pain

BACKGROUND

Low peak alpha frequency (PAF) is an electroencephalography (EEG) outcome associated reliably with high acute pain sensitivity. However, existing research suggests that the relationship between PAF and chronic pain is more variable. This variability could be attributable to chronic pain groups typically being examined as homogenous populations, without consideration being given to potential diagnosis-specific differences. Indeed, while emerging work has compared individuals with chronic pain to healthy controls, no previous studies have examined differences in PAF between diagnoses or across chronic pain subtypes.

METHODS

To address this gap, we reanalysed a dataset of resting state EEG previously used to demonstrate a lack of difference in PAF between individuals with chronic pain and healthy controls. In this new analysis, we separated patients by diagnosis before comparing PAF across three subgroups: chronic widespread pain (n = 30), chronic back pain (n = 38), and healthy controls (n = 87).

RESULTS

We replicate the original finding of no significant difference between chronic pain groups and controls, but also find that individuals with widespread pain had significantly higher global average PAF values than those of people with chronic back pain [p = 0.028, β = 0.25 Hz] after controlling for age, sex, and depression.

CONCLUSIONS

These novel findings reveal PAF values in individuals with chronic pain may be diagnosis-specific and not uniformly shifted from the values of healthy controls. Future studies should account for diagnosis and be cautious with exploring homogenous 'chronic pain' classifications during investigations of PAF.

SIGNIFICANCE

Our work suggests that, contrary to previous hypotheses, inter-individual differences in PAF reflect diagnosis-specific mechanisms rather than the general presence of chronic pain, and therefore may have important implications for future work regarding individually-tailored pain management strategies.

Reduced temporal variability of cortical excitation/inhibition ratio in schizophrenia

Altered neural excitation/inhibition (E/I) balance has long been suspected as a potential underlying cause for clinical symptoms in schizophrenia (SZ). Recent methodological advancements linking the spectral slope (β) of neurophysiological recordings - such as them electroencephalogram (EEG) - to E/I ratio provided much-needed tools to better understand this plausible relationship. Importantly, most approaches treat E/I ratio as a stationary feature in a single scaling range. On the other hand, previous research indicates that this property might change over time, as well as it can express different characteristics in low- and high-frequency regimes. In line, in this study we analyzed resting-state EEG recordings from 30 patients with SZ and 31 healthy controls (HC) and characterized E/I ratio via β separately for low- (1-4 Hz) and high- (20-45 Hz) frequency regimes in a time-resolved manner. Results from this analysis confirmed the bimodal nature of power spectra in both HC and SZ, with steeper spectral slopes in the high- compared to low-frequency ranges. We did not observe any between-group differences in stationary (i.e., time-averaged) neural signatures, however, the temporal variance of β in the 20-45 Hz regime was significantly reduced in SZ patients when compared to HC, predominantly over regions corresponding to the dorsal attention network. Furthermore, this alteration was found correlated to positive clinical symptom scores. Our study indicates that altered E/I ratio dynamics are a characteristic trait of SZ that reflect pathophysiological processes involving the parietal and occipital cortices, potentially responsible for some of the clinical features of the disorder.

Impact of EEG Reference Schemes on Event-Related Potential Outcomes: A Corollary Discharge Study Using a Talk/Listen Paradigm

The selection of an appropriate virtual reference schema is pivotal in determining the outcomes of event-related potential (ERP) studies, particularly within the widely utilized Talk/Listen ERP paradigm, which is employed to non-invasively explore the corollary discharge phenomenon in the speech-auditory system. This research centers on examining the effects of prevalent EEG reference schemas-linked mastoids (LM), common average reference (CAR), and reference electrode standardization technique (REST)-through statistical analysis, statistical parametric scalp mapping (SPSM), and source localization techniques. Our ANOVA findings indicate significant main effects for both the reference and the experimental condition on the amplitude of N1 ERPs. Depending on the reference used, the polarity and amplitude of the N1 ERPs demonstrate systematic variations: LM is associated with pronounced frontocentral activity, whereas both CAR and REST exhibit patterns of frontocentral and occipitotemporal activity. The significance of SPSM results is confined to regions exhibiting prominent N1 activity for each reference schema. Source analysis provides corroborative evidence more aligned with the SPSM results for CAR and REST than for LM, suggesting that results under CAR and REST are more objective and reliable. Therefore, the CAR and REST reference are recommended for future studies involving Talk/Listen ERP paradigms.

Neurophysiological profiles underlying action withholding and action discarding

Although inhibitory control is essential to goal-directed behavior, not all inhibition is the same: Previous research distinguished discarding an action plan from simply withholding it, suggesting separate neurophysiological mechanisms. This study tracks the neurophysiological signatures of both using time-frequency transformation and beamforming in n = 34 healthy individuals. We show that discarding an action plan reduces working memory load, with stronger initial theta band activity compared to withholding it. This oscillatory difference was localized in the (para-)hippocampus and anterior temporal lobe, likely reflecting the need to dissolve action plan features first to enable the following decrease of working memory load. Contrary, when exposed to the embedded stimulus, withholding was associated with higher theta, alpha, and beta band activity relative to discarding. This study advances our understanding of inhibition by revealing distinct neurophysiological mechanisms and functional neuroanatomical structures involved in withholding versus discarding an action.

Sequence-dependent predictive coding during the learning and rewiring of skills

In the constantly changing environment that characterizes our daily lives, the ability to predict and adapt to new circumstances is crucial. This study examines the influence of sequence and knowledge adaptiveness on predictive coding in skill learning and rewiring. Participants were exposed to two different visuomotor sequences with overlapping probabilities. By applying temporal decomposition and multivariate pattern analysis, we dissected the neural underpinnings across different levels of signal coding. The study provides neurophysiological evidence for the influence of knowledge adaptiveness on shaping predictive coding, revealing that these are intricately linked and predominantly manifest at the abstract and motor coding levels. These findings challenge the traditional view of a competitive relationship between learning context and knowledge, suggesting instead a hierarchical integration where their properties are processed simultaneously. This integration facilitates the adaptive reuse of existing knowledge in the face of new learning. By shedding light on the mechanisms of predictive coding in visuomotor sequences, this research contributes to a deeper understanding of how the brain navigates and adapts to environmental changes, offering insights into the foundational processes that underlie learning and adaptation in dynamic contexts.

Exploring Synergies in Brain-Machine Interfaces: Compression vs. Performance

Individuals with severe neurological injuries often rely on assistive technologies, but current methods have limitations in accurately decoding multi-degree-of-freedom (DoF) movements. Intracortical brain-machine interfaces (iBMIs) use neural signals to provide a more natural control method, but currently struggle with higher-DoF movements-something the brain handles effortlessly. It has been theorized that the brain simplifies high-DoF movement through muscle synergies, which link multiple muscles to function as a single unit. These synergies have been studied using dimensionality reduction techniques like principal component analysis (PCA), non-negative matrix factorization (NMF), and demixed PCA (dPCA) and successfully used to reduce noise and improve offline decoder stability in non-invasive applications. However, their effectiveness in improving decoding and generalizability for implanted recordings across varied tasks is unclear. Here, we evaluated if brain and muscle synergies can enhance iBMI performance in non-human primates performing a two-DoF finger task. Specifically, we tested if PCA, dPCA, and NMF could compress and denoise brain and muscle data and improve decoder generalization across tasks. Our results showed that while all methods effectively compressed data with minimal loss in decoding accuracy, none improved performance through denoising. Additionally, none of the methods enhanced generalization across tasks. These findings suggest that while dimensionality reduction can aid data compression, alone it may not reveal the "true" control space needed to improve decoder performance or generalizability. Further research is required to determine whether synergies are the optimal control framework or if alternative approaches are required to enhance decoder robustness in iBMI applications.

Chunk Duration Limits the Learning of Multiword Chunks: Behavioral and Electroencephalography Evidence from Statistical Learning

Language comprehension involves the grouping of words into larger multiword chunks. This is required to recode information into sparser representations to mitigate memory limitations and counteract forgetting. It has been suggested that electrophysiological processing time windows constrain the formation of these units. Specifically, the period of rhythmic neural activity (i.e., low-frequency neural oscillations) may set an upper limit of 2-3 sec. Here, we assess whether learning of new multiword chunks is also affected by this neural limit. We applied an auditory statistical learning paradigm of an artificial language while manipulating the duration of to-be-learned chunks. Participants listened to isochronous sequences of disyllabic pseudowords from which they could learn hidden three-word chunks based on transitional probabilities. We presented chunks of 1.95, 2.55, and 3.15 sec that were created by varying the pause interval between pseudowords. In a first behavioral experiment, we tested learning using an implicit target detection task. We found better learning for chunks of 2.55 sec as compared to longer durations in line with an upper limit of the proposed time constraint. In a second experiment, we recorded participants' electroencephalogram during the exposure phase to use frequency tagging as a neural index of statistical learning. Extending the behavioral findings, results show a significant decline in neural tracking for chunks exceeding 3 sec as compared to both shorter durations. Overall, we suggest that language learning is constrained by endogenous time constraints, possibly reflecting electrophysiological processing windows.

An early effect of the parafoveal preview on post-saccadic processing of English words

A key aspect of efficient visual processing is to use current and previous information to make predictions about what we will see next. In natural viewing, and when looking at words, there is typically an indication of forthcoming visual information from extrafoveal areas of the visual field before we make an eye movement to an object or word of interest. This "preview effect" has been studied for many years in the word reading literature and, more recently, in object perception. Here, we integrated methods from word recognition and object perception to investigate the timing of the preview on neural measures of word recognition. Through a combined use of EEG and eye-tracking, a group of multilingual participants took part in a gaze-contingent, single-shot saccade experiment in which words appeared in their parafoveal visual field. In valid preview trials, the same word was presented during the preview and after the saccade, while in the invalid condition, the saccade target was a number string that turned into a word during the saccade. As hypothesized, the valid preview greatly reduced the fixation-related evoked response. Interestingly, multivariate decoding analyses revealed much earlier preview effects than previously reported for words, and individual decoding performance correlated with participant reading scores. These results demonstrate that a parafoveal preview can influence relatively early aspects of post-saccadic word processing and help to resolve some discrepancies between the word and object literatures.

Directed connectivity in theta networks supports action-effect integration

The ability to plan and carry out goal-directed behavior presupposes knowledge about the contingencies between movements and their effects. Ideomotor accounts of action control assume that agents integrate action-effect contingencies by creating action-effect bindings, which associate movement patterns with their sensory consequences. However, the neurophysiological underpinnings of action-effect binding are not yet well understood. Given that theta band activity has been linked to information integration, we thus studied action-effect integration in an electrophysiological study with N = 31 healthy individuals with a strong focus on theta band activity. We examined how information between functional neuroanatomical structures is exchanged to enable action planning. We show that theta band activity in a network encompassing the insular cortex (IC), the anterior temporal lobe (ATL), and the inferior frontal cortex (IFC) supports the establishment of action-effect bindings. All regions revealed bi-directional effective connectivities, indicating information transfer between these regions. The IC and ATL create a loop for information integration and the conceptual abstraction of it. The involvement of anterior regions of the IFC, particularly during the acquisition phase of the action-effect, likely reflects episodic control mechanisms in which a past event defines a "template" of what action-effect is to be expected. Taken together, the current findings connect well with major cognitive concepts. Our study suggests a functional relevance of theta band activity in an IC-ATL-IFC network, which in turn implies that basic ideomotor action-effect integration is implemented through theta band activity and effective connectivities between temporo-frontal structures.

Saccade execution increases the preview effect with faces: An EEG and eye-tracking coregistration study

Under naturalistic viewing conditions, humans conduct about three to four saccadic eye movements per second. These dynamics imply that in real life, humans rarely see something completely new; there is usually a preview of the upcoming foveal input from extrafoveal regions of the visual field. In line with results from the field of reading research, we have shown with EEG and eye-tracking coregistration that an extrafoveal preview also affects postsaccadic visual object processing and facilitates discrimination. Here, we ask whether this preview effect in the fixation-locked N170, and in manual responses to the postsaccadic target face (tilt discrimination), requires saccade execution. Participants performed a gaze-contingent experiment in which extrafoveal face images could change their orientation during a saccade directed to them. In a control block, participants maintained stable gaze throughout the experiment and the extrafoveal face reappeared foveally after a simulated saccade latency. Compared with this no-saccade condition, the neural and the behavioral preview effects were much larger in the saccade condition. We also found shorter first fixation durations after an invalid preview, which is in contrast to reading studies. We interpret the increased preview effect under saccade execution as the result of the additional sensorimotor processes that come with gaze behavior compared with visual perception under stable fixation. In addition, our findings call into question whether EEG studies with fixed gaze capture key properties and dynamics of active, natural vision.

A New Perspective in Epileptic Seizure Classification: Applying the Taxonomy of Seizure Dynamotypes to Noninvasive EEG and Examining Dynamical Changes across Sleep Stages

Epilepsy, a neurological disorder characterized by recurrent unprovoked seizures, significantly impacts patient quality of life. Current classification methods focus primarily on clinical observations and electroencephalography (EEG) analysis, often overlooking the underlying dynamics driving seizures. This study uses surface EEG data to identify seizure transitions using a dynamical systems-based framework-the taxonomy of seizure dynamotypes-previously examined only in invasive data. We applied principal component and independent component (IC) analysis to surface EEG recordings from 1,177 seizures in 158 patients with focal epilepsy, decomposing the signals into ICs. The ICs were visually labeled for clear seizure transitions and bifurcation morphologies (BifMs), which were then examined using Bayesian multilevel modeling in the context of clinical factors. Our analysis reveals that certain onset bifurcations (saddle node on invariant circle and supercritical Hopf) are more prevalent during wakefulness compared with their reduced rate during nonrapid eye movement (NREM) sleep, particularly NREM3. We discuss the possible implications of our results in the context of modeling approaches and suggest additional avenues to continue this exploration. Furthermore, we demonstrate the feasibility of automating this classification process using machine learning, achieving high performance in identifying seizure-related ICs and classifying interspike interval changes. Our findings suggest that the noise in surface EEG may obscure certain BifMs, and we suggest technical improvements that could enhance detection accuracy. Expanding the dataset and incorporating long-term biological rhythms, such as circadian and multiday cycles, may provide a more comprehensive understanding of seizure dynamics and improve clinical decision-making.

EEG-based brain age prediction in infants-toddlers: Implications for early detection of neurodevelopmental disorders

The infant brain undergoes rapid developmental changes in the first three years of life. Understanding these changes through the prediction of chronological age using neuroimaging can provide insights into typical and atypical brain development. We utilized 938 resting-state EEG recordings from 457 typically developing infants, 2 to 38 months old, to develop age prediction models. The multilayer perceptron model demonstrated the highest accuracy with an R2 of 0.83 and a mean absolute error of 91.7 days. Feature importance analysis that combined hierarchical clustering and Shapley values identified two feature clusters describing periodic alpha and low beta activity as key predictors of age. Application of the model to EEG data from infants later diagnosed with autism or Down syndrome revealed significant underestimations of chronological age, supporting its potential as a clinical tool for early identification of alterations in brain development.

Exploring Machine Learning Classification of Movement Phases in Hemiparetic Stroke Patients: A Controlled EEG-tDCS Study

BACKGROUND/OBJECTIVES

Noninvasive brain stimulation (NIBS) can boost motor recovery after a stroke. Certain movement phases are more responsive to NIBS, so a system that auto-detects these phases would optimize stimulation timing. This study assessed the effectiveness of various machine learning models in identifying movement phases in hemiparetic individuals undergoing simultaneous NIBS and EEG recordings. We hypothesized that transcranial direct current stimulation (tDCS), a form of NIBS, would enhance EEG signals related to movement phases and improve classification accuracy compared to sham stimulation.

METHODS

EEG data from 10 chronic stroke patients and 11 healthy controls were recorded before, during, and after tDCS. Eight machine learning algorithms and five ensemble methods were used to classify two movement phases (hold posture and reaching) during each of these periods. Data preprocessing included z-score normalization and frequency band power binning.

RESULTS

In chronic stroke participants who received active tDCS, the classification accuracy for hold vs. reach phases increased from pre-stimulation to the late intra-stimulation period (72.2% to 75.2%, p < 0.0001). Late active tDCS surpassed late sham tDCS classification (75.2% vs. 71.5%, p < 0.0001). Linear discriminant analysis was the most accurate (74.6%) algorithm with the shortest training time (0.9 s). Among ensemble methods, low gamma frequency (30-50 Hz) achieved the highest accuracy (74.5%), although this result did not achieve statistical significance for actively stimulated chronic stroke participants.

CONCLUSIONS

Machine learning algorithms showed enhanced movement phase classification during active tDCS in chronic stroke participants. These results suggest their feasibility for real-time movement detection in neurorehabilitation, including brain-computer interfaces for stroke recovery.

Using ERPs to unveil the authenticity evaluation and neural response to online rumors

The rapid propagation of information in the digital epoch has brought a surge of rumors, creating a significant societal challenge. While prior research has primarily focused on the psychological aspects of rumors-such as the beliefs, behaviors, and persistence they evoke-there has been limited exploration of how rumors are processed in the brain. In this study, we experimented to examine both behavioral responses and EEG data during rumor detection. Participants evaluated the credibility of 80 randomly presented rumors, and only 22% were able to identify false rumors more accurately than by random chance. Our ERP findings reveal that truth judgments elicit stronger negative ERP responses (N400) compared to false judgments, while false judgments are associated with larger positive ERP responses (P2, P3, and LPP). Additionally, we identified gender differences in brain activity related to rumor detection, suggesting distinct cognitive strategies. Men demonstrated greater P2 and enhanced N400 responses, while women exhibited larger P3 and LPP amplitudes. This study is among the first to investigate the neural patterns underlying rumors recognition and to highlight gender disparities in decision-making related to rumors.

Neural dynamics of personality trait perception and interaction preferences

According to recent research, self-reported Big Five personality traits are associated with preferences for faces that are representative of certain Big Five traits. Previous research has primarily focused on either preference for distinct prototypical personality faces or the accuracy of trait ratings for these faces. However, the underlying neural correlates involved in the processing of prototypical personality faces are unknown. In the present study, we aim to bridge this gap by investigating whether participants' Big Five personality traits predict preferences to interact with individuals represented by prototypical personality faces, as well as the neural processing of these facial features. Based on theoretical considerations and previous research, we focus on trait extraversion, agreeableness and neuroticism, and corresponding prototypical faces. Participants were asked to classify prototypical faces as above or below average representative of a certain trait, and then provide an interaction preference rating while face-sensitive event-related potentials (N170 and late positive potential) were measured. In line with our hypotheses, the results showed an interaction preference for faces that were perceived as high (vs. low) extraverted and agreeable and low (vs. high) neurotic. In addition, the preference for agreeable faces interacted with personality characteristics of the perceiver: The higher a persons' score on trait agreeableness, the higher the face preference ratings for both prototypical and perceived high agreeable faces. Analyses of ERP data showed that an increase in preference ratings for prototypical agreeable faces was paralleled by an increase of the late positive potential. Notably, the N170 did not show any neural signature of the hypothesized effects of personality faces. Together, these results highlight the importance of considering both perceiver characteristics as well as perceived features of an interaction partner when it comes to preference for social interaction.Protocol registration The stage 1 protocol for this Registered Report was accepted in principle on the 8th of May 2023. The protocol, as accepted by the journal, can be found at: https://doi.org/10.17605/OSF.IO/G8SCY .

Real-time sub-milliwatt epilepsy detection implemented on a spiking neural network edge inference processor

Analyzing electroencephalogram (EEG) signals to detect the epileptic seizure status of a subject presents a challenge to existing technologies aimed at providing timely and efficient diagnosis. In this study, we aimed to detect interictal and ictal periods of epileptic seizures using a spiking neural network (SNN). Our proposed approach provides an online and real-time preliminary diagnosis of epileptic seizures and helps to detect possible pathological conditions. To validate our approach, we conducted experiments using multiple datasets. We utilized a trained SNN to identify the presence of epileptic seizures and compared our results with those of related studies. The SNN model was deployed on Xylo, a digital SNN neuromorphic processor designed to process temporal signals. Xylo efficiently simulates spiking leaky integrate-and-fire neurons with exponential input synapses. Xylo has much lower energy requirements than traditional approaches to signal processing, making it an ideal platform for developing low-power seizure detection systems. Our proposed method has a high test accuracy of 93.3% and 92.9% when classifying ictal and interictal periods. At the same time, the application has an average power consumption of 87.4 μW (IO power) + 287.9 μW (compute power) when deployed to Xylo. Our method demonstrates excellent low-latency performance when tested on multiple datasets. Our work provides a new solution for seizure detection, and it is expected to be widely used in portable and wearable devices in the future.

Socioeconomic status (SES) and cognitive outcomes are predicted by resting-state EEG in school-aged children

Children's socioeconomic status (SES) is related to patterns of intrinsic resting-state brain function that subserve relevant cognitive processes over the course of development. Although infant research has demonstrated the association between children's environments, cognitive outcomes, and resting-state electroencephalography (rsEEG), it remains unknown how these aspects of their environment, tied to SES, impact neural and cognitive development throughout the school years. To address this gap, we applied a multivariate pattern analysis (MVPA) to rsEEG data to identify which neural frequencies at rest are differentially associated with unique aspects of socioeconomic status (SES; income and maternal education) and cognitive (vocabulary, working memory) outcomes among school-aged children (8-15 years). We find that the alpha frequency is associated with both income and maternal education, while lower gamma and theta fluctuations are tied to dissociable aspects of SES and cognitive outcomes. Specifically, changes in the gamma frequency are predictive of both maternal education and vocabulary outcome, while changes in the theta frequency are related to both income and working memory ability. The current findings extend our understanding of unique pathways by which SES influences cognitive and neural development in school-aged children.

Refining ADHD diagnosis with EEG: The impact of preprocessing and temporal segmentation on classification accuracy

BACKGROUND

EEG signals are commonly used in ADHD diagnosis, but they are often affected by noise and artifacts. Effective preprocessing and segmentation methods can significantly enhance the accuracy and reliability of ADHD classification.

METHODS

We applied filtering, ASR, and ICA preprocessing techniques to EEG data from children with ADHD and neurotypical controls. The EEG recordings were segmented, and features were extracted and selected based on statistical significance. Classification was performed using various EEG segments and channels with Machine Learning models (SVM, KNN, and XGBoost) to identify the most effective combinations for accurate ADHD diagnosis.

RESULTS

Our findings show that models trained on later EEG segments achieved significantly higher accuracy, indicating the potential role of cognitive fatigue in distinguishing ADHD. The highest classification accuracy (86.1%) was achieved using data from the P3, P4, and C3 channels, with key features such as Kurtosis, Katz fractal dimension, and power spectrums in the Delta, Theta, and Alpha bands contributing to the results.

CONCLUSION

This study highlights the importance of preprocessing and segmentation in improving the reliability of ADHD diagnosis through EEG. The results suggest that further research on cognitive fatigue and segmentation could enhance diagnostic accuracy in ADHD patients.

Sensory Entrained TMS (seTMS) enhances motor cortex excitability

Transcranial magnetic stimulation (TMS) applied to the motor cortex has revolutionized the study of motor physiology in humans. Despite this, TMS-evoked electrophysiological responses show significant variability, due in part to inconsistencies between TMS pulse timing and ongoing brain oscillations. Variable responses to TMS limit mechanistic insights and clinical efficacy, necessitating the development of methods to precisely coordinate the timing of TMS pulses to the phase of relevant oscillatory activity. We introduce Sensory Entrained TMS (seTMS), a novel approach that uses musical rhythms to synchronize brain oscillations and time TMS pulses to enhance cortical excitability. Focusing on the sensorimotor alpha rhythm, a neural oscillation associated with motor cortical inhibition, we examine whether rhythm-evoked sensorimotor alpha phase alignment affects primary motor cortical (M1) excitability in healthy young adults (n=33). We first confirmed using electroencephalography (EEG) that passive listening to musical rhythms desynchronizes inhibitory sensorimotor brain rhythms (mu oscillations) around 200 ms before auditory rhythmic events (27 participants). We then targeted this optimal time window by delivering single TMS pulses over M1 200 ms before rhythmic auditory events while recording motor-evoked potentials (MEPs; 19 participants), which resulted in significantly larger MEPs compared to standard single pulse TMS and an auditory control condition. Neither EEG measures during passive listening nor seTMS-induced MEP enhancement showed dependence on musical experience or training. These findings demonstrate that seTMS effectively enhances corticomotor excitability and establishes a practical, cost-effective method for optimizing non-invasive brain stimulation outcomes.

Neurophysiological dynamics of metacontrol states: EEG insights into conflict regulation

Understanding the neural mechanisms underlying metacontrol and conflict regulation is crucial for insights into cognitive flexibility and persistence. This study employed electroencephalography (EEG), EEG-beamforming and directed connectivity analyses to explore how varying metacontrol states influence conflict regulation at a neurophysiological level. Metacontrol states were manipulated by altering the frequency of congruent and incongruent trials across experimental blocks in a modified flanker task, and both behavioral and electrophysiological measures were analyzed. Behavioral data confirmed the experimental manipulation's efficacy, showing an increase in persistence bias and a reduction in flexibility bias during increased conflict regulation. Electrophysiologically, theta band activity paralleled the behavioral data, suggesting that theta oscillations reflect the mismatch between expected metacontrol bias and actual task demands. Alpha and beta band dynamics differed across experimental blocks, though these changes did not directly mirror behavioral effects. Post-response alpha and beta activity were more pronounced in persistence-biased states, indicating a neural reset mechanism preparing for future cognitive demands. By using a novel artificial neural networks method, directed connectivity analyses revealed enhanced inter-regional communication during persistence states, suggesting stronger top-down control and sensorimotor integration. Overall, theta band activity was closely tied to metacontrol processes, while alpha and beta bands played a role in resetting the neural system for upcoming tasks. These findings provide a deeper understanding of the neural substrates involved in metacontrol and conflict monitoring, emphasizing the distinct roles of different frequency bands in these cognitive processes.

Potential Cognitive Decline Linked to Electronegative L5 in Type 2 Diabetes: A Holo-Hilbert Spectral Analysis

INTRODUCTION

Patients with type 2 diabetes mellitus (T2DM) have an increased risk of cognitive impairment. In this study, we investigated the effect of L5 - an electronegative subfraction of low-density lipoprotein cholesterol (LDL-C) - on the cognitive function of patients with T2DM.

METHODS

This cross-sectional study included 68 patients with T2DM: 15 with normal cognitive function, 39 with mild cognitive impairment (MCI), and 14 with Alzheimer disease (AD). Cognitive evaluation was performed using the Cognitive Abilities Screening Instrument. We developed a new method - Holo-Hilbert spectral analysis (HHSA) - for analyzing electroencephalography signals. Using HHSA, we investigated the effects of L5 on patients' neural activity.

RESULTS

Our findings suggested that a higher percentage of L5 in LDL-C (L5%) was independently associated with increased risks of MCI and AD in patients with T2DM. A negative correlation was observed between serum L5% and cognitive performance, particularly in the concentration subdomain, in patients with MCI. HHSA revealed that an elevated serum L5% value was correlated with an increase in low-frequency neural oscillations but a reduction in high-frequency oscillations in patients with MCI. However, no correlation was observed between L5, cognitive performance, and neural activity in patients with normal cognitive function or AD.

CONCLUSION

Our findings demonstrate L5 to be an efficient biomarker and electroencephalography/HHSA to be an innovative approach for assessing cognitive function in patients with T2DM. L5 may affect frontal lobe function, leading to concentration deficits. The correlation between L5 and cognitive impairment appears to vary depending on the stage of neurodegeneration.

Comparison of dry and wet electroencephalography for the assessment of cognitive evoked potentials and sensor-level connectivity

Background

Multipin dry electrodes (dry EEG) provide faster and more convenient application than wet EEG, enabling extensive data collection. This study aims to compare task-related time-frequency representations and resting-state connectivity between wet and dry EEG methods to establish a foundation for using dry EEG in investigations of brain activity in neuropsychiatric disorders.

Methods

In this counterbalanced cross-over study, we acquired wet and dry EEG in 33 healthy participants [n = 22 females, mean age (SD) = 24.3 (± 3.4) years] during resting-state and an auditory oddball paradigm. We computed mismatch negativity (MMN), theta power in task EEG, and connectivity measures from resting-state EEG using phase lag index (PLI) and minimum spanning tree (MST). Agreement between wet and dry EEG was assessed using Bland-Altman bias.

Results

MMN was detectable with both systems in time and frequency domains, but dry EEG underestimated MMN mean amplitude, peak latency, and theta power compared to wet EEG. Resting-state connectivity was reliably estimated with dry EEG using MST diameter in all except the very low frequencies (0.5-4 Hz). PLI showed larger differences between wet and dry EEG in all frequencies except theta.

Conclusion

Dry EEG reliably detected MMN and resting-state connectivity despite a lower signal-to-noise ratio. This study provides the methodological basis for using dry EEG in studies investigating the neural processes underlying psychiatric and neurological conditions.

Neurophysiological principles underlying predictive coding during dynamic perception-action integration

A major concept in cognitive neuroscience is that brains are "prediction machines". Yet, conceptual frameworks on how perception and action become integrated still lack the concept of predictability and it is unclear how neural processes may implement predictive coding during dynamic perception-action integration. We show that distinct neurophysiological mechanisms of nonlinearly directed connectivities in the theta and alpha band between cortical structures underlie these processes. During the integration of perception and motor codes, especially theta band activity in the insular cortex and temporo-hippocampal structures is modulated by the predictability of upcoming information. Here, the insular cortex seems to guide processes. Conversely, the retrieval of such integrated perception-action codes during actions heavily relies on alpha band activity. Here, directed top-down influence of alpha band activity from inferior frontal structures on insular and temporo-hippocampal structures is key. This suggests that these top-down effects reflect attentional shielding of retrieval processes operating in the same neuroanatomical structures previously involved in the integration of perceptual and motor codes. Through neurophysiology, the present study connects predictive coding mechanisms with frameworks specifying the dynamic integration of perception and action.

Virtual reality-enabled high-performance emotion estimation with the most significant channel pairs

Human-computer interface (HCI) and electroencephalogram (EEG) signals are widely used in user experience (UX) interface designs to provide immersive interactions with the user. In the context of UX, EEG signals can be used within a metaverse system to assess user engagement, attention, emotional responses, or mental workload. By analyzing EEG signals, system designers can tailor the virtual environment, content, or interactions in real time to optimize UX, improve immersion, and personalize interactions. However, in this case, in addition to the signals' processing cost and classification accuracy, cybersickness in Virtual Reality (VR) systems needs to be resolved. At this point, channel selection methods can perform better for HCI and UX applications by reducing noisy and redundant information in generally unrelated EEG channels. For this purpose, a new method for EEG channel selection based on phase-locking value (PLV) analysis is proposed. We hypothesized that there are interactions between EEG channels in terms of PLV in repeated tasks in different trials of the emotion estimation experiment. Subsequently, frequency-based features were extracted. The features were classified by dividing them into bags using the Multiple-Instance Learning (MIL) variant. This study provides higher classification performance using fewer EEG channels for emotion prediction. The performance rate obtained in binary classification with the Random Forests (RF) algorithm is at a promising level of 99%. The proposed method achieved an accuracy of 99.38% for valence using all channels on the new dataset (VREMO) and 98.13% with channel selection. The benchmark dataset (DEAP) achieved accuracies of 98.16% using all channels and 98.13% with selected channels.

Investigating Brain Responses to Transcutaneous Electroacupuncture Stimulation: A Deep Learning Approach

This study investigates the neurophysiological effects of transcutaneous electroacupuncture stimulation (TEAS) on brain activity, using advanced machine learning techniques. This work analyzed the electroencephalograms (EEG) of 48 study participants, in order to analyze the brain’s response to different TEAS frequencies (2.5, 10, 80, and sham at 160 pulses per second (pps)) across 48 participants through pre-stimulation, during-stimulation, and post-stimulation phases. Our approach introduced several novel aspects. EEGNet, a convolutional neural network specifically designed for EEG signal processing, was utilized in this work, achieving over 95% classification accuracy in detecting brain responses to various TEAS frequencies. Additionally, the classification accuracies across the pre-stimulation, during-stimulation, and post-stimulation phases remained consistently high (above 92%), indicating that EEGNet effectively captured the different time-based brain responses across different stimulation phases. Saliency maps were applied to identify the most critical EEG electrodes, potentially reducing the number needed without sacrificing accuracy. A phase-based analysis was conducted to capture time-based brain responses throughout different stimulation phases. The robustness of EEGNet was assessed across demographic and clinical factors, including sex, age, and psychological states. Additionally, the responsiveness of different EEG frequency bands to TEAS was investigated. The results demonstrated that EEGNet excels in classifying EEG signals with high accuracy, underscoring its effectiveness in reliably classifying EEG responses to TEAS and enhancing its applicability in clinical and therapeutic settings. Notably, gamma band activity showed the highest sensitivity to TEAS, suggesting significant effects on higher cognitive functions. Saliency mapping revealed that a subset of electrodes (Fp1, Fp2, Fz, F7, F8, T3, T4) could achieve accurate classification, indicating potential for more efficient EEG setups.

Monitoring pilot trainees' cognitive control under a simulator-based training process with EEG microstate analysis

The objective of pilot training is to equip trainees with the knowledge, judgment, and skills to maintain control of an aircraft and respond to critical flight tasks. The present research aims to investigate changes in trainees' cognitive control levels during a pilot training process while they underwent basic flight maneuvers. EEG microstate analysis was applied together with spectral power features to quantitatively monitor trainees' cognitive control under varied flight tasks during different training sessions on a flight simulator. Not only could EEG data provide an objective measure of cognitive control to complement the current subjective assessments, but the application of EEG microstate analysis is particularly well-suited for capturing rapid dynamic changes in cognitive states that may happen under complex human activities in conducting flight maneuvers. Comparisons were conducted between two types of tasks and across different training stages to monitor how pilot trainees' cognitive control responds to varied flight task types and training stages. The present research provides insights into the changes in trainees' cognitive control during a pilot training process and highlights the potential of EEG microstate analysis for monitoring cognitive control.

Neural mechanisms of adaptive behavior: Dissociating local cortical modulations and interregional communication patterns

Adaptive behavior is based on flexibly managing and integrating perceptual and motor processes, and the reconfiguration thereof. Such adaptive behavior is also relevant during inhibitory control. Although research has demonstrated local activity modulations in theta and alpha frequency bands during behavioral adaptation, the communication of brain regions is insufficiently studied. Examining directed connectivity between brain regions using a machine learning approach, a generally increased activity, but decreased connectivity within a temporo-occipital theta band network was revealed during the reconfiguration of perception-action associations during inhibitory control. Additionally, a fronto-occipital alpha-theta interplay yielded a decrease in directed connectivity during reconfiguration processes, which was associated with lower error rates in behavior. Thus, adaptive behavior relies on both local increases and decreases of activity depending on the frequency band, and concomitant decreases in communication between frontal and sensory cortices. The findings reframe common conceptualizations about how adaptive behavior is supported by neural processes.

Receivers' responses are integrated into costly third-party punishment in a way that interacts with the unfairness of allocations

Costly third-party punishment (TPP) is an effective way to enforce fairness norms and promote cooperation. Recent studies have shown that the third party considers not only the proposer's suggested allocation but also the receiver's response to the allocation, which was typically ignored in traditional TPP studies when making punishment decisions. However, it remains unclear whether and how the varying unfair allocations and receivers' responses are integrated into third-party punishment. The current study addressed these issues at behavioral and electrophysiological levels by employing a modified third-party punishment task involving proposers' highly or moderately unfair allocations and the receivers' acceptance or rejection responses. At the behavioral level, participants punished proposers more often when receivers rejected relative to accepted unfair allocations. This effect was further modulated by the unfairness degree of allocations, indicated by a more pronounced rejection-sensitive effect when participants observed the moderately unfair offers. Electrophysiologically, when the receiver rejected the moderately unfair allocations, a stronger late-stage component P300/LPP, which was considered to be involved in allocations of attention resources, was found. Meanwhile, separated from the P300/LPP, the P200 associated with early attention capture demonstrated a rejection-sensitive effect. Together, in the costly TPP studies, the receiver is typically designated as passive and silent, and her/his responses to unfairness are conventionally ignored. However, our results indicate that except for the proposer's distribution behavior, the receiver's response does have an impact on third-party punishment in a way that interacts with the unfairness of allocations.

Does Action Observation of the Whole Task Influence Mirror Neuron System and Upper Limb Muscle Activity Better Than Part Task in People With Stroke?

Background: Task-based action observation and imitation (AOI) is a promising intervention to enhance upper limb (UL) motor function poststroke. However, whether whole/part task must be trained in the AOI therapy needs further substantiation. Objective: The objective of this study is to assess and compare the mirror neuron activity and UL muscle activity during AOI of reaching task in terms of whole task (complete movement) and part task (proximal arm movements and distal arm movements). Methods: In this cross-sectional study, 26 participants with first-time unilateral stroke were asked to observe the prerecorded videos of a reaching task in terms of a whole task and proximal and distal components, followed by imitation of the task, respectively. Electroencephalographic (EEG) mu rhythm suppression and electromyographic amplitude of six UL muscles were measured during the task. Results: The analysis of EEG revealed a statistically significant mu suppression score, indicating mirror neuron system activity, during AOI of the whole task in C3 (p = <0.001) and C4 (p = <0.001) electrodes compared to the part task. Percentage maximum voluntary contraction amplitudes of the deltoid (p = 0.002), supraspinatus (p = <0.001), triceps brachii (p = 0.002), brachioradialis (p = 0.006), and extensor carpi radialis (p = <0.001) muscles showed a significant increase in muscle activity during AOI of the whole task. Also, there seems to be a task observation-specific activation of muscles following AOI of proximal or distal tasks. Conclusion: The practice of the whole task should be given emphasis while framing the AOI treatment module to enhance reaching in people with stroke. Trial registration: Clinical Trials Registry-India (CTRI) identifier: CTRI/2018/04/013466.

Investigating causal effects of pupil size on visual discrimination and visually evoked potentials in an optotype discrimination task

Pupil size primarily changes to regulate the amount of light entering the retina, optimizing the balance between visual acuity and sensitivity for effective visual processing. However, research directly examining the relationship between pupil size and visual processing has been limited. While a few studies have recorded pupil size and EEG signals to investigate the role of pupil size in visual processing, these studies have predominantly focused on the domain of visual sensitivity. Causal effects of pupil size on visual acuity, therefore, remain poorly understood. By manipulating peripheral background luminance levels and target stimulus contrast while simultaneously recording pupillometry and EEG signals, we examined how absolute pupil size affects visual discrimination and visually evoked potentials (VEP) in a task using optotype mimicking the Snellen eye chart, the most common assessment of visual acuity. Our findings indicate that both higher background luminance levels and higher target contrast were associated with improved target discrimination and faster correct reaction times. Moreover, while higher contrast visual stimuli evoked larger VEPs, the effects of pupil size on VEPs were not significant. Additionally, we did not observe inter-individual correlations between absolute pupil size and discrimination performance or VEP amplitude. Together, our results demonstrate that absolute pupil size, regulated by global luminance level, played a functional role in enhancing visual discrimination performance in an optotype discrimination task. The differential VEP effects of pupil size compared to those of stimulus contrast further suggested distinct neural mechanisms involved in facilitating visual acuity under small pupils.

Alterations in aperiodic and periodic EEG activity in young children with Down syndrome

Down syndrome (DS) is the most common cause of intellectual disability, yet little is known about the neurobiological pathways leading to cognitive impairments. Electroencephalographic (EEG) measures are commonly used to study neurodevelopmental disorders, but few studies have focused on young children with DS. Here we assess resting state EEG data collected from toddlers/preschoolers with DS (n = 29, age 13-48 months old) and compare their aperiodic and periodic EEG features with both age-matched (n = 29) and developmental-matched (n = 58) comparison groups. DS participants exhibited significantly reduced aperiodic slope, increased periodic theta power, and decreased alpha peak amplitude. A majority of DS participants displayed a prominent peak in the theta range, whereas a theta peak was not present in age-matched participants. Overall, similar findings were also observed when comparing DS and developmental-matched groups, suggesting that EEG differences are not explained by delayed cognitive ability.

The promoting effect of the absence of second-party's punishment power on third-party punishment in maintaining social fairness norms: An EEG hyper-scanning study

Third-party punishment (TPP) plays an irreplaceable role in maintaining social fairness. Punishment power is a significant area of study within economic games. However, the impact of whether or not the second-party possesses punishment power on TPP remains unexplored. The present study utilizes the high temporal resolution of EEG and time-frequency analysis, intra-barin functional connectivity analysis, inter-brain synchronization (IBS) analysis, and granger causality analysis(GCA) to comprehensively explore the neural mechanism of TPP from the perspective of third-party individual's decision-making and IBS in the real-time social interaction. Time-frequency results found that, the absence of the punishment power activated more theta-band and alpha-band power compare to when second-party has punishment power. When second-party has no punishment power, functional connection results observed stronger functional connectivity in theta band for medium unfair offers between rTPJ and PFC. Dual-brain analysis revealed that when the second-party has no punishment power, there is a significantly higher IBS in the alpha band between the frontal and frontal-central lobes of the second-party and the parietal and parietal occipital lobes of the third-party. GCA results further showed that the direction of IBS from third-party to second-party was significantly stronger than from second-party to third-party. This study demonstrates that the absence of the second-party's punishment power promote TPP, and similar cognitive process of thinking on how to maintain social fairness enhances IBS. The current study emphasizes the influence of punishment power on TPP, broadens the research perspective and contributes crucial insights into maintain social fairness.

Implications of Aperiodic and Periodic EEG Components in Classification of Major Depressive Disorder from Source and Electrode Perspectives

Electroencephalography (EEG) is useful for studying brain activity in major depressive disorder (MDD), particularly focusing on theta and alpha frequency bands via power spectral density (PSD). However, PSD-based analysis has often produced inconsistent results due to difficulties in distinguishing between periodic and aperiodic components of EEG signals. We analyzed EEG data from 114 young adults, including 74 healthy controls (HCs) and 40 MDD patients, assessing periodic and aperiodic components alongside conventional PSD at both source and electrode levels. Machine learning algorithms classified MDD versus HC based on these features. Sensor-level analysis showed stronger Hedge's g effect sizes for parietal theta and frontal alpha activity than source-level analysis. MDD individuals exhibited reduced theta and alpha activity relative to HC. Logistic regression-based classifications showed that periodic components slightly outperformed PSD, with the best results achieved by combining periodic and aperiodic features (AUC = 0.82). Strong negative correlations were found between reduced periodic parietal theta and frontal alpha activities and higher scores on the Beck Depression Inventory, particularly for the anhedonia subscale. This study emphasizes the superiority of sensor-level over source-level analysis for detecting MDD-related changes and highlights the value of incorporating both periodic and aperiodic components for a more refined understanding of depressive disorders.

Color and brightness at work: Shedding some light on mind wandering

INTRODUCTION

Occupational hazards are partly caused by the physical factors of the work environment, among which are ambient color and brightness, which can interfere with cognitive performance. Especially in modern work environments, performance relies heavily on cognitive functions such as attention, and an important factor in disrupting sustained attention is mind wandering (MW). This study aimed to investigate the effects of white and blue colors with two brightness levels on sustained attention and brain electrophysiology.

METHODS

A total of 20 participants were exposed to 4 different conditions (white and blue as color and 300 and 800 lx as the brightness level) in separate blocks in a virtual reality environment in which a continuous performance test (CPT) was performed.

RESULTS

The high brightness blue condition induced significant changes in sustained attention. MW network analysis showed a significant decrease in delta frequency band in the blue color condition with high brightness and beta decrease in the blue color condition with low brightness, whereas the activity of MW network increased when exposed to the white color condition.

CONCLUSION

High-brightness blue light resulted in better sustained attention and decreased activity of MW-related neural regions. It is thus recommended that these results be taken into consideration in the interior design of educational settings and cars among other environments that require a high level and maintenance of cognitive functions, especially sustained attention.

Human brain imaging with high-density electroencephalography: Techniques and applications

Electroencephalography (EEG) is a technique for non-invasively measuring neuronal activity in the human brain using electrodes placed on the participant's scalp. With the advancement of digital technologies, EEG analysis has evolved over time from the qualitative analysis of amplitude and frequency modulations to a comprehensive analysis of the complex spatiotemporal characteristics of the recorded signals. EEG is now considered a powerful tool for measuring neural processes in the same time frame in which they happen (i.e. the subsecond range). However, it is commonly argued that EEG suffers from low spatial resolution, which makes it difficult to localize the generators of EEG activity accurately and reliably. Today, the availability of high-density EEG (hdEEG) systems, combined with methods for incorporating information on head anatomy and sophisticated source-localization algorithms, has transformed EEG into an important neuroimaging tool. hdEEG offers researchers and clinicians a rich and varied range of applications. It can be used not only for investigating neural correlates in motor and cognitive neuroscience experiments, but also for clinical diagnosis, particularly in the detection of epilepsy and the characterization of neural impairments in a wide range of neurological disorders. Notably, the integration of hdEEG systems with other physiological recordings, such as kinematic and/or electromyography data, might be especially beneficial to better understand the neuromuscular mechanisms associated with deconditioning in ageing and neuromotor disorders, by mapping the neurokinematic and neuromuscular connectivity patterns directly in the brain.

Maternal heart rate variability at 3-months postpartum is associated with maternal mental health and infant neurophysiology

Previous research has demonstrated a critical link between maternal mental health and infant development. However, there is limited understanding of the role of autonomic regulation in postpartum maternal mental health and infant outcomes. In the current study, we tested 76 mother-infant dyads from diverse socioeconomic backgrounds when infants were 3-months of age. We recorded simultaneous ECG from dyads while baseline EEG was collected from the infant; ECG heart rate variability (HRV) and EEG theta-beta ratio and alpha asymmetry were calculated. Dyadic physiological synchrony was also analyzed to better understand the role of autonomic co-regulation. Results demonstrated that lower maternal HRV was associated with higher self-reported maternal depression and anxiety. Additionally, mothers with lower HRV had infants with lower HRV. Maternal HRV was also associated with higher infant theta-beta ratios, but not alpha asymmetry. Exploratory analyses suggested that for mother-infant dyads with greater physiological synchrony, higher maternal HRV predicted increased infant theta-beta ratio via infant HRV. These findings support a model in which maternal mental health may influence infant neurophysiology via alterations in autonomic stress regulation and dyadic physiological co-regulation.

Inter-subject gamma oscillation synchronization as a biomarker of abnormal processing of social interaction in ADHD

Children with attention-deficit hyperactivity disorder (ADHD) have difficulties in social interactions. Studying brain activity during social interactions is difficult with conventional artificial stimuli. This pioneering study examined the neural correlates of social perception in children with ADHD and matched controls using naturalistic stimuli. We presented 20 children with ADHD and 20 age-and-sex-matched controls with tailored movies featuring high- or low-level social interactions while recording electroencephalographic signals. Both groups exhibited synchronized gamma-band oscillations, but controls demonstrated greater inter-subject correlations. Additionally, the difference in inter-subject correlations between high- and low-interaction movies was significantly larger in controls compared to ADHD patients. Between 55 and 75 Hz comparing viewing high interaction movies with low interaction moves, controls had a significantly larger weighting in the right parietal lobe, while ADHD patients had a significantly smaller weighting in the left occipital lobe. These findings reveal distinct spatiotemporal neural signatures in social interaction processing among children with ADHD and controls using naturalistic stimuli. These neural markers offer potential for group differentiation and assessing intervention efficacy, advancing our understanding ADHD-related social interaction mechanisms.

The metacontrol of event segmentation-A neurophysiological and behavioral perspective

During our everyday life, the constant flow of information is divided into discrete events, a process conceptualized in Event Segmentation Theory (EST). How people perform event segmentation and the resulting granularity of encapsulated segments likely depends on their metacontrol style. Yet, the underlying neural mechanisms remain undetermined. The current study examines how the metacontrol style affects event segmentation through the analysis of EEG data using multivariate pattern analysis (MVPA) and source localization analysis. We instructed two groups of healthy participants to either segment a movie as fine-grained as possible (fine-grain group) or provided no such instruction (free-segmentation group). The fine-grain group showed more segments and a higher likelihood to set event boundaries upon scene changes, which supports the notion that cognitive control influences segmentation granularity. On a neural level, representational dynamics were decodable 400 ms prior to the decision to close a segment and open a new one, and especially fronto-polar regions (BA10) were associated with this representational dynamic. Groups differed in their use of this representational dynamics to guide behavior and there was a higher sensitivity to incoming information in the Fine-grain group. Moreover, a higher likelihood to set event boundaries was reflected by activity increases in the insular cortex suggesting an increased monitoring of potentially relevant upcoming events. The study connects the EST with the metacontrol framework and relates these to overarching neural concepts of prefrontal cortex function.

Emotion-Induced Frontal Alpha Asymmetry as a Candidate Predictor of Relapse After Discontinuation of Antidepressant Medication

BACKGROUND

One in 3 patients relapse after antidepressant discontinuation. Thus, the prevention of relapse after achieving remission is an important component in the long-term management of major depressive disorder. However, no clinical or other predictors are established. Frontal reactivity to sad mood as measured by functional magnetic resonance imaging has been reported to relate to relapse independently of antidepressant discontinuation and is an interesting candidate predictor.

METHODS

Patients (n = 56) who had remitted from a depressive episode while taking antidepressants underwent electroencephalography (EEG) recording during a sad mood induction procedure prior to gradually discontinuing their medication. Relapse was assessed over a 6-month follow-up period. Thirty five healthy control participants were also tested. Current source density of the EEG power in the alpha band (8-13 Hz) was extracted and alpha asymmetry was computed by comparing the power across 2 hemispheres at frontal electrodes (F5 and F6).

RESULTS

Sad mood induction was robust across all groups. Reactivity of alpha asymmetry to sad mood did not distinguish healthy control participants from patients with remitted major depressive disorder on medication. However, the 14 (25%) patients who relapsed during the follow-up period after discontinuing medication showed significantly reduced reactivity in alpha asymmetry compared with patients who remained well. This EEG signal provided predictive power (69% out-of-sample balanced accuracy and a positive predictive value of 0.75).

CONCLUSIONS

A simple EEG-based measure of emotional reactivity may have potential to contribute to clinical prediction models of antidepressant discontinuation. Given the very small sample size, this finding must be interpreted with caution and requires replication in a larger study.

An end-to-end multi-task motor imagery EEG classification neural network based on dynamic fusion of spectral-temporal features

Electroencephalograph (EEG) brain-computer interfaces (BCI) have potential to provide new paradigms for controlling computers and devices. The accuracy of brain pattern classification in EEG BCI is directly affected by the quality of features extracted from EEG signals. Currently, feature extraction heavily relies on prior knowledge to engineer features (for example from specific frequency bands); therefore, better extraction of EEG features is an important research direction. In this work, we propose an end-to-end deep neural network that automatically finds and combines features for motor imagery (MI) based EEG BCI with 4 or more imagery classes (multi-task). First, spectral domain features of EEG signals are learned by compact convolutional neural network (CCNN) layers. Then, gated recurrent unit (GRU) neural network layers automatically learn temporal patterns. Lastly, an attention mechanism dynamically combines (across EEG channels) the extracted spectral-temporal features, reducing redundancy. We test our method using BCI Competition IV-2a and a data set we collected. The average classification accuracy on 4-class BCI Competition IV-2a was 85.1 % ± 6.19 %, comparable to recent work in the field and showing low variability among participants; average classification accuracy on our 6-class data was 64.4 % ± 8.35 %. Our dynamic fusion of spectral-temporal features is end-to-end and has relatively few network parameters, and the experimental results show its effectiveness and potential.

A machine learning artefact detection method for single-channel infant event-related potential studies

Objective. Automated detection of artefact in stimulus-evoked electroencephalographic (EEG) data recorded in neonates will improve the reproducibility and speed of analysis in clinical research compared with manual identification of artefact. Some studies use very short, single-channel epochs of EEG data with little recorded EEG per infant-for example because the clinical vulnerability of the infants limits access for recording. Current artefact-detection methods that perform well on adult data and resting-state and multi-channel data in infants are not suitable for this application. The aim of this study was to create and test an automated method of detecting artefact in single-channel 1500 ms epochs of infant EEG.Approach. A total of 410 epochs of EEG were used, collected from 160 infants of 28-43 weeks postmenstrual age. This dataset-which was balanced to include epochs of background activity and responses to visual, auditory, tactile and noxious stimuli-was presented to seven independent raters, who independently labelled the epochs according to whether or not they were able to visually identify artefacts. The data was split into a training set (340 epochs) and an independent test set (70 epochs). A random forest model was trained to identify epochs as either artefact or not artefact.Main results. This model performs well, achieving a balanced accuracy of 0.81, which is as good as manual review of data. Accuracy was not significantly related to the infant age or type of stimulus.Significance. This method provides an objective tool for automated artefact rejection for short epoch, single-channel EEG in neonates and could increase the utility of EEG in neonates in both the clinical and research setting.