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

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.

Distinct Contributions of Alpha and Beta Oscillations to Context-Dependent Visual Size Perception

Previous studies have proposed two cognitive mechanisms responsible for the Ebbinghaus illusion effect, i.e., contour interaction and size contrast. However, the neural underpinnings of these two mechanisms are largely unexplored. The present study introduced binocular depth to the Ebbinghaus illusion configuration and made the central target appear either in front of or behind the surrounding inducers in order to disturb size contrast instead of contour interaction. The results showed that the illusion effect, though persisted, was significantly reduced under the binocular depth conditions. Notably, the target with a larger perceived size reduced early alpha-band power (8-13 Hz, 0-100 ms after stimulus onset) at centroparietal sites irrespective of the relative depth of the target and the inducers, with the parietal alpha power negatively correlated with the illusion effect. Moreover, the target with a larger perceived size increased the occipito-parietal beta-band power (14-25 Hz, 200-300 ms after stimulus onset) under the no-depth condition, and the beta power was positively correlated with the illusion effect when the depth conditions were subtracted from the no-depth condition. The findings provided neurophysiological evidence in favor of the two cognitive mechanisms of the Ebbinghaus illusion by revealing that early alpha power is associated with low-level contour interaction and late beta power is linked to high-level size contrast, supporting the claim that neural oscillations at distinct frequency bands dynamically support different aspects of visual processing.

The effect of performance contingent reward prospects flexibly adapts to more versus less specific task goals

In some situations, for example, when we expect to gain a reward in case of good performance, goal-driven top-down attention is particularly strong. Little is known about the task specificity of such increases of top-down attention due to environmental factors. To understand to what extent performance-contingent reward prospects can result in specific and unspecific changes in cognitive processing, we here investigate reward effects under different levels of task specification. Thirty-two participants performed a visual or an auditory discrimination task cued by two consecutive visual stimuli: First, a reward cue indicated if good performance was rewarded. Second, a task cue announced either which of the two tasks would follow (precise cue) or that both tasks would follow equally likely (imprecise cue). Reward and task cue preciseness both significantly improved performance. Moreover, the response time difference between precisely and imprecisely cued trials was significantly stronger in rewarded than in unrewarded trials. These effects were reflected in event-related potential (ERP) slow wave amplitudes: Reward and preciseness both significantly enhanced the contingent negative variation (CNV) prior to the task stimulus. In an early CNV time interval, both factors also showed an interaction. A negative slow wave prior to the task cue was also significantly enhanced for rewarded trials. This effect correlated with the reward difference in response times. These results indicate that reward prospects trigger task-specific changes in preparatory top-down attention which can flexibly adapt over time and across different task requirements. This highlights that a reward-induced increase of cognitive control can occur on different specificity levels.

Resting-state EEG microstate analysis of internet gaming disorder and alcohol use disorder

INTRODUCTION

To investigate the neurophysiological aspects of addiction, the microstate characteristics of internet gaming disorder (IGD), alcohol use disorder (AUD), and healthy control (HC) groups were compared using resting-state electroencephalography (EEG).

METHODS

In total, 199 young adults (75 patients with IGD, 57 patients with AUD, and 67 HCs) participated in this study. We conducted EEG microstate analysis among the groups and also compared the obtained parameters with the results of psychological assessments.

RESULTS

The global explained variance, occurrence, and coverage of microstate C were significantly lower in the AUD group than in the IGD group. Additionally, rates of transition from microstates A, B, and D to C were significantly lower in the AUD group than in the IGD group, whereas rates of transition from microstate A to B were lower in the IGD group compared to HCs. Furthermore, the occurrence of microstate C and transition from microstate B to C were negatively correlated with the Alcohol Use Disorder Identification and Behavioural Inhibition Scale score.

CONCLUSION

There were significant differences in microstate characteristics among the groups, which correlated with the psychological scores. These findings suggest that microstate features can be used as neuromarkers in clinical settings to differentiate between addictive disorders and evaluate the pathophysiology of AUD and IGD.

Are cleft sentence structures more difficult to process?

This study compares the processing of cleft structures against that of monoclausal sentences using event-related potential (ERP). We aim to understand how syntactic complexity is processed by comparing the neural response to cleft and single-clause sentences with identical verb phrases, controlling for verb bias frequency effects. Sixty participants were tested, and we presented 100 cleft and 100 monoclausal sentences, balanced for active and passive verb usage. We examined the P600 component, an ERP associated with syntactic complexity, to assess the processing of cleft structures. Results showed that cleft structures incur a greater processing load, as indicated by a larger P600, compared to monoclausal sentences. The P600 response indicates that processing cleft sentences requires additional syntactic operations, consistent with behavioral studies showing that clinical populations have difficulty comprehending complex sentences.

[Identification of Differences in Brain Functions at Varied Degrees of Acclimatization to High Altitudes Based on Resting-State Electroencephalogram Signals]

Objective

To explore the differences in brain functions between individuals with good acclimatization to high altitudes and those with poor acclimatization based on microstate and functional connectivity analysis of resting-state electroencephalogram (EEG) activities.

Methods

A total of 44 residents exposed to the high-altitude environment of 3650 m above sea level were enrolled. They were divided into two groups according to their findings for the high-altitude acclimatization index (AAI), with 22 in the good acclimatization group and 22 in the poor acclimatization group. The good acclimatization group had 10 males and 12 females of the mean age of (20.48±2.09) years. Among them, 10 were Tibetans, and 12 were Han Chinese. The poor acclimatization group consisted of 18 males and 4 females, with a mean age of (18.90±1.25) years. Among them, 7 were Tibetans, and 15 were Han Chinese. The K-means clustering algorithm was used to classify EEG microstates into four categories of A, B, C, and D. The average duration, coverage, occurrence frequency, and transition probabilities between microstates were compared. Coherence analysis (COH) was performed to assess the strength of brain network connectivity.

Results

Compared to the good acclimatization group, the poor acclimatization group exhibited a longer duration, higher occurrence frequency, and coverage of microstate C. The coverage of microstate D was also higher, while the occurrence frequency and coverage of microstate B were lower. The poor acclimatization group showed a decrease in transitions from microstate B to A or D, while transitions from C to B or D, and those from D to A or C increased, showing statistically significant differences between the groups (P<0.05). In the α, β, δ, and θ frequency bands, the poor acclimatization group had higher COH strength in regions such as the frontal and occipital lobes, showing symmetrical functional connectivity between the left and right hemispheres.

Conclusion

The good and poor high-altitude acclimatization groups exhibit different neural mechanisms in terms of microstates and functional connectivity. There are significant differences between individuals with good acclimatization and those with poor acclimatization regarding visual information processing, interference resistance, brain network stability, and coherence.

Patient-specific visual neglect severity estimation for stroke patients with neglect using EEG

Objective.We aim to assess the severity of spatial neglect (SN) through detailing patients' field of view (FOV) using EEG. Spatial neglect, a prevalent neurological syndrome in stroke patients, typically results from unilateral brain injuries, leading to inattention to the contralesional space. Commonly used Neglect detection methods like the Behavioral Inattention Test-conventional lack the capability to assess the full extent and severity of neglect. Although the Catherine Bergego Scale provides valuable clinical information, it does not detail the specific FOV affected in neglect patients.Approach.Building on our previously developed EEG-based brain-computer interface system, AR-guided EEG-based neglect detection, assessment, and rehabilitation system (AREEN), we aim to map neglect severity across a patient's FOV. We have demonstrated that AREEN can assess neglect severity in a patient-agnostic manner. However, its effectiveness in patient-specific scenarios, which is crucial for creating a generalizable plug-and-play system, remains unexplored. This paper introduces a novel EEG-based combined spatio-temporal network (ESTNet) that processes both time and frequency domain data to capture essential frequency band information associated with SN. We also propose a FOV correction system using Bayesian fusion, leveraging AREEN's recorded response times for enhanced accuracy by addressing noisy labels within the dataset.Main results.Extensive testing of ESTNet on our proprietary dataset has demonstrated its superiority over benchmark methods, achieving 79.62% accuracy, 76.71% sensitivity, and 86.36% specificity. Additionally, we provide saliency maps to enhance model explainability and establish clinical correlations.Significance.These findings underscore ESTNet's potential combined with Bayesian fusion-based FOV correction as an effective tool for generalized neglect assessment in clinical settings.

Common neural mechanisms supporting time judgements in humans and monkeys

There has been an increasing interest in identifying the biological underpinnings of human time perception, for which purpose research in non-human primates (NHP) is common. Although previous work, based on behaviour, suggests that similar mechanisms support time perception across species, the neural correlates of time estimation in humans and NHP have not been directly compared. In this study, we assess whether brain evoked responses during a time categorization task are similar across species. Specifically, we assess putative differences in post-interval evoked potentials as a function of perceived duration in human EEG (N = 24) and local field potential (LFP) and spike recordings in pre-supplementary motor area (pre-SMA) of one monkey. Event-related potentials (ERPs) differed significantly after the presentation of the temporal interval between "short" and "long" perceived durations in both species, even when the objective duration of the stimuli was the same. Interestingly, the polarity of the reported ERPs was reversed for incorrect trials (i.e., the ERP of a "long" stimulus looked like the ERP of a "short" stimulus when a time categorization error was made). Hence, our results show that post-interval potentials reflect the perceived (rather than the objective) duration of the presented time interval in both NHP and humans. In addition, firing rates in monkey's pre-SMA also differed significantly between short and long perceived durations and were reversed in incorrect trials. Together, our results show that common neural mechanisms support time categorization in NHP and humans, thereby suggesting that NHP are a good model for investigating human time perception.

Correlates of implicit semantic processing as revealed by representational similarity analysis applied to EEG

Most researchers agree that some stages of object recognition can proceed implicitly. Implicit recognition occurs when an object is automatically and unintentionally encoded and represented in the brain even though the object is irrelevant to the current task. No consensus has been reached as to what level of semantic abstraction processing can go implicitly. An informative method to explore the level of abstraction and the time courses of informational content in neural representations is representational similarity analysis (RSA). Here, we apply RSA to EEG data recorded while participants processed semantics of visually presented objects. Explicit focus on semantics was given when participants classified images of objects as manmade or natural. For implicit processing of semantics, participants judged the location of images on the screen. The category animate/inanimate as well as more concrete categories (e.g., birds, fruit, musical instruments, etc.) are processed implicitly whereas the category manmade/natural is not processed implicitly.

The moderating role of the late positive potential in the link between attachment anxiety and emotion regulation difficulties

Introduction

Understanding how adults experience and regulate their emotions is strongly linked to attachment orientations. Numerous studies indicate emotional regulation difficulties in both attachment avoidance and anxiety. Additionally, emotional Event-Related Potentials (ERPs), such as the Late Positive Potential (LPP), reveal the process of emotional information at the cerebral level, and thus, LPP is commonly used in studies examining emotion regulation processes. For instance, when individuals are asked to use cognitive strategies to increase, maintain, or decrease their emotional responses to stimuli, changes in LPP amplitude can reflect the effectiveness of these regulation strategies. However, little is known about the potential moderating effect of the LPP during the implementation of emotional regulation strategies in the relationship between attachment dimensions and emotional dysregulation. To address this oversight, the purpose of the present study was to examine the association between both dimensions of attachment, anxiety and avoidance, and emotional dysregulation, as well as the moderating role of the LPP during the induced implementation of cognitive reappraisal.

Methods

Brain activity was recorded using EEG from n = 63 adults while they performed a task in which they were instructed to either reappraise or suppress emotions elicited by unpleasant images. To assess the associations between LPP, emotional dysregulation, and attachment orientations, the Difficulties in Emotion Regulation Scale Spanish version (DERS-E) and the Experiences in Close Relationships questionnaire (ECR-12) were used.

Results

Interestingly, we found that greater LPP amplitudes during reappraisal implementation intensified the association between attachment anxiety and emotional regulation difficulties. Conversely, this relationship was non-significant under lower levels of LPP amplitude-Providing supporting evidence for the moderating role of LPP.

Discussion

Our results highlight how attachment anxiety can influence the ability to regulate emotions. This study provides new insights into how variations in LPP contribute to the effectiveness of emotion regulation strategies.

Electroencephalography based delirium screening in acute supratentorial stroke

BACKGROUND

Up to 25% of patients suffering from an acute stroke are diagnosed with delirium during the hospital stay, with older age increasing the risk. Generalized slowing in the electroencephalogram (EEG) supports the diagnosis of delirium. We examined the potential of single-channel EEG (DeltaScan®) as an easy-to-use device on intensive care units for detecting delirium. Our aim was to investigate characteristics of bihemispheric EEG recordings and single-channel EEG in patients suffering from strokes with and without delirium and to analyze the diagnostic accuracy of EEG-based diagnoses.

METHODS

Within the first five days after stroke onset, patients received single-channel EEG DeltaScan® and a routine 21-channel EEG. The DeltaScan® analyzes right sided fronto-parietal EEG using a proprietary algorithm focusing on polymorphic delta activity (PDA). In routine EEG the power spectral density (PSD) in predefined frequency bands was analyzed based on 2-minute eyes-closed resting state segments. EEG-analyses were conducted in MNE (v1.3.1) in Python (3.10) and RStudio (v4.2.1).

RESULTS

In 9 of 53 patients (52-90 years) delirium was diagnosed according to DSM-V criteria. Sensitivity of DeltaScan® was 44% (95% CI = 15.3-77.3%), while specificity was 71% (95% CI = 57-83%). We found patients with right hemispheric stroke having a higher probability to be false positive in DeltaScan® (p = 0.01). The 21-channel EEG based power analysis revealed significant differences in frontal delta and theta power between patients with and without delirium (p < 0.05).

CONCLUSIONS

When EEG is used in clinical practice to support a delirium diagnosis in stroke patients, bihemispheric recordings are likely preferable over unilateral recordings. Slowing in the delta- or theta-frequency spectrum over the site of stroke may lead to false-positive results in single channel EEG based delirium scoring.

The utility of wearable electroencephalography combined with behavioral measures to establish a practical multi-domain model for facilitating the diagnosis of young children with attention-deficit/hyperactivity disorder

BACKGROUND

A multi-method, multi-informant approach is crucial for evaluating attention-deficit/hyperactivity disorders (ADHD) in preschool children due to the diagnostic complexities and challenges at this developmental stage. However, most artificial intelligence (AI) studies on the automated detection of ADHD have relied on using a single datatype. This study aims to develop a reliable multimodal AI-detection system to facilitate the diagnosis of ADHD in young children.

METHODS

78 young children were recruited, including 43 diagnosed with ADHD (mean age: 68.07 ± 6.19 months) and 35 with typical development (mean age: 67.40 ± 5.44 months). Machine learning and deep learning methods were adopted to develop three individual predictive models using electroencephalography (EEG) data recorded with a wearable wireless device, scores from the computerized attention assessment via Conners' Kiddie Continuous Performance Test Second Edition (K-CPT-2), and ratings from ADHD-related symptom scales. Finally, these models were combined to form a single ensemble model.

RESULTS

The ensemble model achieved an accuracy of 0.974. While individual modality provided the optimal classification with an accuracy rate of 0.909, 0.922, and 0.950 using the ADHD-related symptom rating scale, the K-CPT-2 score, and the EEG measure, respectively. Moreover, the findings suggest that teacher ratings, K-CPT-2 reaction time, and occipital high-frequency EEG band power values are significant features in identifying young children with ADHD.

CONCLUSIONS

This study addresses three common issues in ADHD-related AI research: the utility of wearable technologies, integrating databases from diverse ADHD diagnostic instruments, and appropriately interpreting the models. This established multimodal system is potentially reliable and practical for distinguishing ADHD from TD, thus further facilitating the clinical diagnosis of ADHD in preschool young children.

Fatigue Characterization of EEG Brain Networks Under Mixed Reality Stereo Vision

Mixed Reality (MR) technology possesses profound and extensive potential across a multitude of domains, including, but not limited to industry, healthcare, and education. However, prolonged use of MR devices to watch stereoscopic content may lead to visual fatigue. Since visual fatigue involves multiple brain regions, our study aims to explore the topological characteristics of brain networks derived from electroencephalogram (EEG) data. Because the Phase-Locked Value (PLV) is capable of effectively measuring the phase synchronization relationship between brain regions, it was calculated between all pairs of channels in both comfort and fatigue states. Subsequently, a sparse brain network was constructed based on PLV by applying an appropriate threshold. The node properties (betweenness centrality, clustering coefficient, node efficiency) and edge properties (characteristic path length) were calculated based on the corresponding brain network within specific frequency bands for both comfort and fatigue states. In analyzing the PLV of brain connectivity in comfort and fatigue states, a notable enhancement in brain connectivity is observed within the alpha, theta, and delta frequency bands during fatigue status. By analyzing the node and edge properties of brain networks, it is evident that the mean values of these properties in the fatigue state were higher than those in the comfort state. By analyzing the node and edge properties at a local level, the average difference in betweenness centrality, clustering coefficients, and nodal efficiency across the three EEG frequency bands was computed to find significant brain regions. The main findings are as follows: Betweenness centrality primarily differs in frontal and parietal regions, with minor involvement in temporal and central regions. The clustering Coefficient mainly varies in the frontal region, with slight differences being seen in the temporal and occipital regions. Nodal efficiency primarily varies in the frontal, temporal, and central regions, with minor differences being seen in the parietal and occipital regions. Edge property analysis indicates that there is a higher occurrence of long-distance connections among brain regions during the fatigue state, which reflects a loss of synaptic transmission efficiency on a global level. Our study plays a crucial role in understanding the neural mechanisms underlying visual fatigue, potentially providing insights that could be applied to high-demand cognitive fields where prolonged use of MR devices leads to visual fatigue.

Synchronization-based fusion of EEG and eye blink signals for enhanced decoding accuracy

Decoding locomotor tasks is crucial in cognitive neuroscience for understanding brain responses to physical tasks. Traditional methods like EEG offer brain activity insights but may require additional modalities for enhanced interpretative precision and depth. The integration of EEG with ocular metrics, particularly eye blinks, presents a promising avenue for understanding cognitive processes by combining neural and ocular behaviors. However, synchronizing EEG and eye blink activities poses a significant challenge due to their frequently inconsistent alignment. Our study with 35 participants performing various locomotor tasks such as standing, walking, and transversing obstacles introduced a novel methodology, pcEEG+, which fuses EEG principal components (pcEEG) with aligned eye blink data (syncBlink). The results demonstrated that pcEEG+ significantly improved decoding accuracy in locomotor tasks, reaching 78% in some conditions, and surpassed standalone pcEEG and syncBlink methods by 7.6% and 22.7%, respectively. The temporal generalization matrix confirmed the consistency of pcEEG+ across tasks and times. The results were replicated using two driving simulator datasets, thereby confirming the validity of our method. This study demonstrates the efficacy of the pcEEG+ method in decoding locomotor tasks, underscoring the importance of temporal synchronization for accuracy and offering a deeper insight into brain activity during complex movements.

Task-oriented EEG denoising generative adversarial network for enhancing SSVEP-BCI performance

Objective.The quality of electroencephalogram (EEG) signals directly impacts the performance of brain-computer interface (BCI) tasks. Many methods have been proposed to eliminate noise from EEG signals, but most of these methods focus solely on signal denoising itself, disregarding the impact on subsequent tasks, which deviates from the original intention of EEG denoising. The main objective of this study is to optimize EEG denoising models with a purpose of improving the performance of BCI tasks.Approach.To this end, we proposed an innovative task-oriented EEG denoising generative adversarial network (TOED-GAN) method. This network utilizes the generator of GAN to decompose and reconstruct clean signals from the raw EEG signals, and the discriminator to learn to distinguish the generated signals from the true clean signals, resulting in a remarkable increase of the signal-to-noise ratio by simultaneously enhancing task-related components and removing task-irrelevant noise from the original contaminated signals.Main results.We evaluated the performance of the model on a public dataset and a self-collected dataset respectively, with canonical correlation analysis classification tasks of the steady-state visual evoked potential (SSVEP) based BCI. Experimental results demonstrate that TOED-GAN exhibits excellent performance in removing EEG noise and improving performance for SSVEP-BCI, with accuracy improvement rates reaching 18.47% and 21.33% in contrast to the baseline methods of convolutional neural networks, respectively.Significance.This work proves that the proposed TOED-GAN, as an EEG denoising method tailored for SSVEP tasks, contributes to enhancing the performance of BCIs in practical application scenarios.

Repetition of critical search features modulates EEG lateralized potentials in visual search

In visual search, the repetition of target and distractor colors enables both successful search and effective distractor handling. Nevertheless, the specific consequences of trial-to-trial feature repetition in different search contexts are poorly understood. Here, we investigated how feature repetition shapes the electrophysiological and behavioral correlates of target processing and distractor handling, testing theoretically informed predictions with single-trial mixed-effects modeling. In two experiments, the colors of a fixed-shape target and singleton distractor changed unpredictably across trials. Targets were color singletons in Experiment 1, allowing efficient search among pop-out items, but were not uniquely colored in Experiment 2, encouraging slower shape-feature search. Interference by the distractor occurred only in pop-out search but was reduced by repetition. This was paralleled by the contralateral electroencephalography (EEG) response: Following a search color change, the target-related N2pc was greatly reduced, and salient distractors elicited an N2pc followed by an enhanced PD. This biphasic response was absent in Experiment 2, where color was less useful to search. Overall, distractor positivities were not sensitive to feature repetition, suggesting that they are unrelated to preparatory suppression. Attention-related lateralization components are not universally elicited by target or distractor feature values but are driven specifically by expected features important to the search task.

Transcutaneous Auricular Vagus Nerve Stimulation for Visually Induced Motion Sickness: An eLORETA Study

Transcutaneous auricular vagus nerve stimulation (taVNS), a non-invasive form of electrical brain stimulation, has shown potent therapeutic potential for a wide spectrum of conditions. How taVNS influences the characterization of motion sickness - a long mysterious syndrome with a polysymptomatic onset - remains unclear. Here, to examine taVNS-induced effects on brain function in response to motion-induced nausea, 64-channel electroencephalography (EEG) recordings from 42 healthy participants were analyzed; collected during nauseogenic visual stimulation concurrent with taVNS administration, in a crossover randomized sham-controlled study. Cortical neuronal generators were estimated from the obtained EEG using exact low-resolution brain electromagnetic tomography (eLORETA). While both sham and taVNS increased insula activation during electrical stimulation, compared to baseline, taVNS additionally augmented middle frontal gyrus neuronal activity. Following taVNS, brain regions including the supramarginal, parahippocampal, and precentral gyri were activated. Contrasting sham, taVNS markedly increased activity in the middle occipital gyrus during stimulation. A repeated-measures ANOVA showed that taVNS reduced motion sickness symptoms. This reduction in symptoms correlated with taVNS-induced neural activation. Our findings provide new insights into taVNS-induced brain changes, during and after nauseogenic stimuli exposure, including accompanying behavioral response. Together, these findings suggest that taVNS has promise as an effective neurostimulation tool for motion sickness management.

Frontal midline theta power during the cue-target-interval reflects increased cognitive effort in rewarded task-switching

Cognitive performance largely depends on how much effort is invested during task-execution. This also means that we rarely perform as good as we could. Cognitive effort is adjusted to the expected outcome of performance, meaning that it is driven by motivation. The results from recent studies suggest that the expenditure of cognitive control is particularly prone to being affected by modulations of cognitive effort. Although recent EEG studies investigated the neural underpinnings of the interaction of effort and control, reports on how cognitive effort is reflected by oscillatory activity of the EEG are quite sparse. It is the goal of the present study to bridge this gap by performing an exploratory analysis of high-density EEG data from a switching-task using manipulations of monetary incentives. A beamformer approach is used to localize the sensor-level effects in source-space. The results indicate that the manipulation of cognitive effort was successful. The participants reported significantly higher motivation and cognitive effort in high versus low reward trials. Performance was also significantly increased. The analysis of the EEG data revealed that the increase of cognitive effort was reflected by an increased mid-frontal theta activity during the cue-target interval, suggesting an increased use of proactive control. This interpretation is supported by the result from a regression analysis performed on single-trial data, showing higher mid-frontal theta power prior to target-onset being associated with faster responses. Alpha-desynchronization throughout the trial was also more pronounced in high reward trials, signaling a bias of attention towards the processing of external stimuli. Source reconstruction suggests that these effects are located in areas related to cognitive control, and visual processing.

Socioeconomic status moderates the relationship between self-esteem and attitudes towards conspicuous consumption: An electrophysiological study

Research on the impact of self-esteem on conspicuous consumption has shown inconsistent results. This study addresses this variability by examining how socioeconomic status (SES) moderates the relationship between self-esteem and conspicuous consumption. We hypothesized that the relationship varies with SES, predicting a positive relationship at high SES levels and a negative relationship at low SES levels. Our findings confirm this hypothesis. Using a Go/NoGo association task (GNAT) combined with event-related potentials (ERP), we investigated the cognitive processing speeds for congruent and incongruent pairings. This method provided insights into the implicit attitudes towards conspicuous products and revealed the complex dynamics between self-esteem and SES in influencing consumer behavior tendencies. This research advances our understanding of the interactions between self-esteem and SES in conspicuous consumption from an implicit cognitive perspective. It also offers significant practical implications, guiding the development of consumer marketing strategies that can be tailored to different socioeconomic contexts.

Event-related potentials of familiar monosyllabic words with unexpected lexical tones: A picture-word study of Mandarin-speaking preschoolers with and without a history of late talking

This study examined how Mandarin-speaking preschoolers with and without a history of late talking (LT) process familiar monosyllabic words with unexpected lexical tones, focusing on both phonological and semantic violations. This study initially enrolled 64 Mandarin-speaking toddlers: 31 with a history of LT (mean age: 27.67 months) and 33 without a history of LT (non-LT) (mean age: 27.85 months). Event-related potentials were recorded at the age of 4 years during a picture-word mismatch task (LT mean age: 51.36 months; non-LT mean age: 51.20 months); in this task, the participants were presented with auditory words either matching (Tone 3) or mismatching with images in terms of their lexical tones; the mismatches encompassed acoustically dissimilar (Tone 1) and similar (Tone 2) mismatches. A significant difference in the phonological mapping negativity (PMN) responses to Tones 1 and 3 was observed only in the non-LT group. However, differences in the N400 responses to Tones 1 and 3 remained consistent across both groups. In addition, greater differences in the PMN responses between Tones 1 and 3 were associated with higher language proficiency during the preschool period. The PMN response serves as an indicator of neural correlates in lexical tone processing, reflecting challenges encountered by preschoolers with a history of LT when processing the lexical tones of familiar words. Furthermore, the PMN response was correlated with concurrent language abilities. These findings indicate the importance of early tonal perception development for Mandarin speakers with a history of LT. RESEARCH HIGHLIGHTS: Preschoolers with a history of late talking (LT), similar to preschoolers without such a history, can establish word expectations and detect the lexical tone violation in real time. However, those with a history of LT require additional time to process acoustic cues and differentiate between word semantics based on lexical tone information. The phonological mapping negativity response serves as an indicator of neural correlates in lexical tone processing, reflecting challenges encountered by preschoolers with a history of LT when processing the lexical tones of familiar words. The present findings indicate the importance of early intervention for Mandarin speakers with a history of LT, with an emphasis on lexical tone processing from toddlerhood.

Can personality traits be predicted from resting-state EEG oscillations? A replication study

Personality neuroscience seeks to uncover the neurobiological underpinnings of personality. Identifying links between measures of brain activity and personality traits is important in this respect. Using an entirely inductive approach, Jach et al. (2020) attempted to predict personality trait scores from resting-state spectral electroencephalography (EEG) using multivariate pattern analysis (MVPA) and found meaningful results for Agreeableness. The exploratory nature of this work and concerns about replicability in general require a rigorous replication, which was the aim of the current study. We applied the same analytic approach to a large data set (N = 772) to evaluate the robustness of the previous results. Similar to Jach et al. (2020), 8 min of resting-state EEG before and after unrelated tasks with both eyes open and closed were analyzed using support vector regressions (SVR). A 10-fold cross-validation was used to evaluate the prediction accuracy between the spectral power of 59 EEG electrodes within 30 frequency bins ranging from 1 to 30 Hz and Big Five personality trait scores. We were not able to replicate the findings for Agreeableness. We extended the analysis by parameterizing the total EEG signal into its periodic and aperiodic signal components. However, neither component was meaningfully associated with the Big Five personality traits. Our results do not support the initial results and indicate that personality traits may at least not be substantially predictable from resting-state spectral power. Future identification of robust and replicable brain-personality associations will likely require alternative analysis methods and rigorous preregistration of all analysis steps.

The influence of contextual uncertainty on facial expression processing: Evidence from behavior and ERPs

The brain helps individuals build expectations based on emotional prediction, facilitating the processing of faces in social interactions. Due to the intricacy of the environment, accurately predicting emotions remains a formidable task. Contextual uncertainty represents a state characterized by the inability to predict when, how, and why events occur. It leads to intensified sentiments and triggers adverse emotions like anxiety. Therefore, comprehending the influences of contextual uncertainty carries importance. The present study utilized event-related potentials (ERPs) technology to investigate contextual uncertainty's influence on facial expression processing. We employed a novel S1-S2 paradigm, using scene images as S1 and faces as S2. From the learning phase into the testing phase, the certain to uncertain group (CER to UNC) experienced more unpredictability (increased uncertainty), whereas the uncertain to certain group (UNC to CER) experienced more predictability (decreased uncertainty). This allowed for manipulating dynamic alterations in predictive relationships. The behavioral results showed that the valence ratings of neutral facial expressions were more negative in the CER to UNC group with increased contextual uncertainty. The ERP results showed that the more negative SPN (stimulus preceding negativity) amplitudes and positive LPP (late positive potential) amplitudes were observed in the UNC to CER group with decreased contextual uncertainty, compared to the CER to UNC group with increased contextual uncertainty. These findings have indicated that contextual uncertainty affects facial expression processing. In summary, these results contributed to comprehending the contextual uncertainty.

Effects of unconscious tactile stimuli on autonomic nervous activity and afferent signal processing

Autonomic nervous system (ANS) is a mechanism that regulates our internal environment. In recent years, the interest in how tactile stimuli presented directly to the body affect ANS function and cortical processing in humans has been renewed. However, it is not yet clear how subtle tactile stimuli below the level of consciousness affect human heart rate and cortical processing. To examine this, subthreshold electrical stimuli were presented to the left forearm of 43 participants during an image-viewing task, and electrocardiogram (ECG) and electroencephalogram (EEG) data were collected. The changes in the R-wave interval of the ECG immediately after the subthreshold electrical presentation and heartbeat-evoked potential (HEP), the afferent signal processing of cardiac activity, were measured. The results showed that heart rate decelerated immediately after the presentation of subthreshold electrical stimuli. The HEP during stimulus presentation was amplified for participants with greater heart rate acceleration immediately after this deceleration. The magnitude of these effects depended on the type of the subthreshold tactile stimuli. The results suggest that even with subthreshold stimulation, the changes in autonomic activity associated with orienting response and related afferent signal processing differ depending on the clarity of the tactile stimuli.

Machine-Learning-Based Depression Detection Model from Electroencephalograph (EEG) Data Obtained by Consumer-Grade EEG Device

Background/Objectives: There have been attempts to detect depression using medical-grade electroencephalograph (EEG) data based on a machine learning approach. EEG has garnered interest as a method for assessing brainwaves by attaching electrodes to the scalp to obtain electrical activity in the brain. Recently, machine learning has been applied to the EEG data to detect depression, with encouraging results. Specifically, studies using medical-grade EEG data have shown that depression can be accurately detected. However, there is a need to expand the range of applications by achieving a score with machine learning using simpler consumer-grade brain wave sensors. At present, a sufficient score has not been achieved.; Methods: To improve the score of depression detection, we quantified various EEG indices to train models such as power spectrum, asymmetry, complexity, and functional connectivity. In addition, feature selection was performed to ensure that the model learns only promising EEG indices for depression detection. The feature selection methods were Light Gradient Boosting Machine (LightGBM) feature importance, mutual information, ReliefF and ElasticNet coefficients. The selected EEG indices were learned by the LightGBM model, which is reported to be as accurate as the latest deep learning models. In cross-validation, the independence of test and training data was ensured to avoid excessively calculated score; Results: The results showed that the Macro F1 score was 91.59%, suggesting that a consumer-grade EEG can detect depression. In addition, analysis of the EEG indices selected by feature selection indicated that the Macro F1 score was about 80% for single EEG indices such as differential entropy in the frequency band β and functional connectivity in the left frontal region in the frequency band 1-128 Hz; Conclusions: Although the data were obtained from a consumer-grade EEG, the results suggest that these EEG indices are promising for detection depression.

Identification of perceived sentences using deep neural networks in EEG

Objetive. Decoding speech from brain activity can enable communication for individuals with speech disorders. Deep neural networks (DNNs) have shown great potential for speech decoding applications. However, the limited availability of large datasets containing neural recordings from speech-impaired subjects poses a challenge. Leveraging data from healthy participants can mitigate this limitation and expedite the development of speech neuroprostheses while minimizing the need for patient-specific training data.Approach. In this study, we collected a substantial dataset consisting of recordings from 56 healthy participants using 64 EEG channels. Multiple neural networks were trained to classify perceived sentences in the Spanish language using subject-independent, mixed-subjects, and fine-tuning approaches. The dataset has been made publicly available to foster further research in this area.Main results. Our results demonstrate a remarkable level of accuracy in distinguishing sentence identity across 30 classes, showcasing the feasibility of training DNNs to decode sentence identity from perceived speech using EEG. Notably, the subject-independent approach rendered accuracy comparable to the mixed-subjects approach, although with higher variability among subjects. Additionally, our fine-tuning approach yielded even higher accuracy, indicating an improved capability to adapt to individual subject characteristics, which enhances performance. This suggests that DNNs have effectively learned to decode universal features of brain activity across individuals while also being adaptable to specific participant data. Furthermore, our analyses indicate that EEGNet and DeepConvNet exhibit comparable performance, outperforming ShallowConvNet for sentence identity decoding. Finally, our Grad-CAM visualization analysis identifies key areas influencing the network's predictions, offering valuable insights into the neural processes underlying language perception and comprehension.Significance. These findings advance our understanding of EEG-based speech perception decoding and hold promise for the development of speech neuroprostheses, particularly in scenarios where subjects cannot provide their own training data.

Differential modulations of theta and beta oscillations by audiovisual congruency in letter-speech sound integration

The integration of visual letters and speech sounds is a crucial part of learning to read. Previous studies investigating this integration have revealed a modulation by audiovisual (AV) congruency, commonly known as the congruency effect. To investigate the cortical oscillations of the congruency effects across different oscillatory frequency bands, we conducted a Japanese priming task in which a visual letter was followed by a speech sound. We analyzed the power and phase properties of oscillatory activities in the theta and beta bands between congruent and incongruent letter-speech sound (L-SS) pairs. Our results revealed stronger theta-band (5-7 Hz) power in the congruent condition and cross-modal phase resetting within the auditory cortex, accompanied by enhanced inter-trial phase coherence (ITPC) in the auditory-related areas in response to the congruent condition. The observed congruency effect of theta-band power may reflect increased neural activities in the left auditory region during L-SS integration. Additionally, theta ITPC findings suggest that visual letters amplify neuronal responses to the following corresponding auditory stimulus, which may reflect the differential cross-modal influences in the primary auditory cortex. In contrast, decreased beta-band (20-35 Hz) oscillatory power was observed in the right centroparietal regions for the congruent condition. The reduced beta power seems to be unrelated to the processing of AV integration, but may be interpreted as the brain response to predicting auditory sounds during language processing. Our data provide valuable insights by indicating that oscillations in different frequency bands contribute to the disparate aspects of L-SS integration.

Investigating the synergistic neuromodulation effect of bilateral rTMS and VR brain-computer interfaces training in chronic stroke patients

Objective.Stroke is a major cause of adult disability worldwide, resulting in motor impairments. To regain motor function, patients undergo rehabilitation, typically involving repetitive movement training. For those who lack volitional movement, novel technology-based approaches have emerged that directly involve the central nervous system, through neuromodulation techniques such as transcranial magnetic stimulation (TMS), and closed-loop neurofeedback like brain-computer interfaces (BCIs). This, can be augmented through proprioceptive feedback delivered many times by embodied virtual reality (VR). Nonetheless, despite a growing body of research demonstrating the individual efficacy of each technique, there is limited information on their combined effects.Approach.In this study, we analyzed the Electroencephalographic (EEG) signals acquired from 10 patients with more than 4 months since stroke during a longitudinal intervention with repetitive TMS followed by VR-BCI training. From the EEG, the event related desynchronization (ERD) and individual alpha frequency (IAF) were extracted, evaluated over time and correlated with clinical outcome.Main results.Every patient's clinical outcome improved after treatment, and ERD magnitude increased during simultaneous rTMS and VR-BCI. Additionally, IAF values showed a significant correlation with clinical outcome, nonetheless, no relationship was found between differences in ERD pre- post- intervention with the clinical improvement.Significance.This study furnishes empirical evidence supporting the efficacy of the joint action of rTMS and VR-BCI in enhancing patient recovery. It also suggests a relationship between IAF and rehabilitation outcomes, that could potentially serve as a retrievable biomarker for stroke recovery.

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.

Exploring Electrophysiological Responses to Hypnosis in Patients with Fibromyalgia

Background/Objectives: Hypnosis shows great potential for managing patients suffering from fibromyalgia and chronic pain. Several studies have highlighted its efficacy in improving pain, quality of life, and reducing psychological distress. Despite its known feasibility and efficacy, the mechanisms of action remain poorly understood. Building on these insights, this innovative study aims to assess neural activity during hypnosis in fibromyalgia patients using high-density electroencephalography (EEG) and self-reported measures. Methods: Thirteen participants with fibromyalgia were included in this study. EEG recordings were done during resting state and hypnosis conditions. After both conditions, levels of pain, comfort, absorption, and dissociation were assessed using a numerical rating scale. Time perception was collected via an open-ended question. The study was prospectively registered in the ClinicalTrials.gov public registry (NCT04263324). Results: Neural oscillations showed increased theta power during hypnosis in the left parietal and occipital electrodes, increased beta power in the frontal and left temporal electrodes, and increased slow-gamma power in the frontal and left parietal electrodes. Functional connectivity using pairwise-phase consistency measures showed decreased connectivity in the frontal electrodes during hypnosis. Graph-based measures, the node strength, and the cluster coefficient were lower in frontal electrodes in the slow-gamma bands during hypnosis compared to resting state. Key findings indicate significant changes in neural oscillations and brain functional connectivity, suggesting potential electrophysiological markers of hypnosis in this patient population.

Social conformity updates the neural representation of facial attractiveness

People readily change their behavior to comply with others. However, to which extent they will internalize the social influence remains elusive. In this preregistered electroencephalogram (EEG) study, we investigated how learning from one's in-group or out-group members about facial attractiveness would change explicit attractiveness ratings and spontaneous neural representations of facial attractiveness. Specifically, we quantified the neural representational similarities of learned faces with prototypical attractive faces during a face perception task without overt social influence and intentional evaluation. We found that participants changed their explicit attractiveness ratings to both in-group and out-group influences. Moreover, social conformity updated spontaneous neural representation of facial attractiveness, an effect particularly evident when participants learned from their in-group members and among those who perceived tighter social norms. These findings offer insights into how group affiliations and individual differences in perceived social norms modulate the internalization of social influence.

Neurocognitive dynamics of preparatory and adaptive cognitive control: Insights from mass-univariate and multivariate pattern analysis of EEG data

Cognitive control refers to humans' ability to willingly align thoughts and actions with internally represented goals. Research indicates that cognitive control is not one-dimensional but rather integrates multiple sub-processes to cope with task demands successfully. In particular, the dynamic interplay between preparatory (i.e., prior to goal-relevant events) and adaptive (i.e., in response to unexpected demands) recruitment of neural resources is believed to facilitate successful behavioural performance. However, whether preparatory and adaptive processes draw from independent or shared neural resources, and how these align in the information processing stream, remains unclear. To address these issues, we recorded electroencephalographic data from 52 subjects while they performed a computerised task. Using a combination of mass-univariate and multivariate pattern analysis procedures, we found that different types of control triggered distinct sequences of brain activation patterns, and that the order and temporal extent of these patterns were dictated by the type of control used by the participants. Stimuli that fostered preparatory recruitment of control evoked a sequence of transient occipital-parietal, sustained central-parietal, and sustained fronto-central responses. In contrast, stimuli that indicated the need for quick behavioural adjustments triggered a sequence of transient occipital-parietal, fronto-central, and central parietal responses. There was also a considerable degree of overlap in the temporal evolution of these brain activation patterns, with behavioural performance being mainly related to the magnitude of the central-parietal and fronto-central responses. Our results demonstrate how different neurocognitive mechanisms, such as early attentional allocation and subsequent behavioural selection processes, are likely to contribute to cognitive control. Moreover, our findings extend prior work by showing that these mechanisms are engaged (at least partly) in parallel, rather than independently of each other.

Causal role of frontocentral beta oscillation in comprehending linguistic communicative functions

Linguistic communication is often considered as an action serving the function of conveying the speaker's goal to the addressee. Although neuroimaging studies have suggested a role of the motor system in comprehending communicative functions, the underlying mechanism is yet to be specified. Here, by two EEG experiments and a tACS experiment, we demonstrate that the frontocentral beta oscillation, which represents action states, plays a crucial part in linguistic communication understanding. Participants read scripts involving two interlocutors and rated the interlocutors' attitudes. Each script included a critical sentence said by the speaker expressing a context-dependent function of either promise, request, or reply to the addressee's query. These functions were behaviorally discriminated, with higher addressee's will rating for the promise than for the reply and higher speaker's will rating for the request than for the reply. EEG multivariate analyses showed that different communicative functions were represented by different patterns of the frontocentral beta activity but not by patterns of alpha activity. Further tACS results showed that, relative to alpha tACS and sham stimulation, beta tACS improved the predictability of communicative functions of request or reply, as measured by the speaker's will rating. These results convergently suggest a causal role of the frontocentral beta activities in comprehending linguistic communications.

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.

The Selective Impairments of Total Sleep Deprivation on Alerting, Orienting, and Executive Control: Evidence from Event-Related Potentials

BACKGROUND

Many studies have shown that total sleep deprivation (TSD) impairs the attention network, which includes three subcomponents as follows: alerting, orienting, and executive control. However, the specific attention network(s) damaged by TSD remains unclear.

METHODS

Twenty two participants were enrolled to complete the attention network test (ANT) before and after 36 h of TSD with simultaneous electroencephalography recordings.

RESULTS

The repeated-measures analysis of variance of the response time (RT) suggested that the interaction effect between sleep conditions (before versus after TSD) and target congruence (incongruent versus congruent target) was significant; that is, the RT of the incongruent target was longer than that of the congruent target, whereas this difference disappeared after TSD. Furthermore, the interaction effect of sustained potential (SP) amplitude between the sleep conditions and target congruence was significant; that is, the incongruent target invoked a less positive sustained potential amplitude after than before TSD; whereas that invoked by the congruent target was not.

CONCLUSION

TSD selectively impairs attention networks. TSD affects the executive control network the most, which is followed by the alerting network rather than the orienting network. This provides a new perspective for understanding how shortened sleep affects attention.

CLINICAL TRIAL REGISTRATION

No: ChiCTR2400088448. Registered 19 August 2024, https://www.chictr.org.cn.

Using mobile EEG to study auditory work strain during simulated surgical procedures

Surgical personnel face various stressors in the workplace, including environmental sounds. Mobile electroencephalography (EEG) offers a promising approach for objectively measuring how individuals perceive sounds. Because surgical performance does not necessarily decrease with higher levels of distraction, EEG could help guide noise reduction strategies that are independent of performance measures. In this study, we utilized mobile EEG to explore how a realistic soundscape is perceived during simulated laparoscopic surgery. To examine the varying demands placed on personnel in different situations, we manipulated the cognitive demand during the surgical task, using a memory task. To assess responses to the soundscape, we calculated event-related potentials for distinct sound events and temporal response functions for the ongoing soundscape. Although participants reported varying degrees of demand under different conditions, no significant effects were observed on surgical task performance or EEG parameters. However, changes in surgical task performance and EEG parameters over time were noted, while subjective results remained consistent over time. These findings highlight the importance of using multiple measures to fully understand the complex relationship between sound processing and cognitive demand. Furthermore, in the context of combined EEG and audio recordings in real-life scenarios, a sparse representation of the soundscape has the advantage that it can be recorded in a data-protected way compared to more detailed representations. However, it is unclear whether information get lost with sparse representations. Our results indicate that sparse and detailed representations are equally effective in eliciting neural responses. Overall, this study marks a significant step towards objectively investigating sound processing in applied settings.

DERCo: A Dataset for Human Behaviour in Reading Comprehension Using EEG

This paper introduces the DERCo (Dublin EEG-based Reading Experiment Corpus), a language resource combining electroencephalography (EEG) and next-word prediction data obtained from participants reading narrative texts. The dataset comprises behavioral data collected from 500 participants recruited through the Amazon Mechanical Turk online crowd-sourcing platform, along with EEG recordings from 22 healthy adult native English speakers. The online experiment was designed to examine the context-based word prediction by a large sample of participants, while the EEG-based experiment was developed to extend the validation of behavioral next-word predictability. Online participants were instructed to predict upcoming words and complete entire stories. Cloze probabilities were then calculated for each word so that this predictability measure could be used to support various analyses pertaining to semantic context effects in the EEG recordings. EEG-based analyses revealed significant differences between high and low predictable words, demonstrating one important type of potential analysis that necessitates close integration of these two datasets. This material is a valuable resource for researchers in neurolinguistics due to the word-level EEG recordings in context.

Cardiac-Sympathetic Contractility and Neural Alpha-Band Power: Cross-Modal Collaboration during Approach-Avoidance Conflict

As evidence mounts that the cardiac-sympathetic nervous system reacts to challenging cognitive settings, we ask if these responses are epiphenomenal companions or if there is evidence suggesting a more intertwined role of this system with cognitive function. Healthy male and female human participants performed an approach-avoidance paradigm, trading off monetary reward for painful electric shock, while we recorded simultaneous electroencephalographic and cardiac-sympathetic signals. Participants were reward sensitive but also experienced approach-avoidance "conflict" when the subjective appeal of the reward was near equivalent to the revulsion of the cost. Drift-diffusion model parameters suggested that participants managed conflict in part by integrating larger volumes of evidence into choices (wider decision boundaries). Late alpha-band (neural) dynamics were consistent with widening decision boundaries serving to combat reward sensitivity and spread attention more fairly to all dimensions of available information. Independently, wider boundaries were also associated with cardiac "contractility" (an index of sympathetically mediated positive inotropy). We also saw evidence of conflict-specific "collaboration" between the neural and cardiac-sympathetic signals. In states of high conflict, the alignment (i.e., product) of alpha dynamics and contractility were associated with a further widening of the boundary, independent of either signal's singular association. Cross-trial coherence analyses provided additional evidence that the autonomic systems controlling cardiac-sympathetics might influence the assessment of information streams during conflict by disrupting or overriding reward processing. We conclude that cardiac-sympathetic control might play a critical role, in collaboration with cognitive processes, during the approach-avoidance conflict in humans.

Sensorimotor brain-computer interface performance depends on signal-to-noise ratio but not connectivity of the mu rhythm in a multiverse analysis of longitudinal data

Objective.Serving as a channel for communication with locked-in patients or control of prostheses, sensorimotor brain-computer interfaces (BCIs) decode imaginary movements from the recorded activity of the user's brain. However, many individuals remain unable to control the BCI, and the underlying mechanisms are unclear. The user's BCI performance was previously shown to correlate with the resting-state signal-to-noise ratio (SNR) of the mu rhythm and the phase synchronization (PS) of the mu rhythm between sensorimotor areas. Yet, these predictors of performance were primarily evaluated in a single BCI session, while the longitudinal aspect remains rather uninvestigated. In addition, different analysis pipelines were used to estimate PS in source space, potentially hindering the reproducibility of the results.Approach.To systematically address these issues, we performed an extensive validation of the relationship between pre-stimulus SNR, PS, and session-wise BCI performance using a publicly available dataset of 62 human participants performing up to 11 sessions of BCI training. We performed the analysis in sensor space using the surface Laplacian and in source space by combining 24 processing pipelines in a multiverse analysis. This way, we could investigate how robust the observed effects were to the selection of the pipeline.Main results.Our results show that SNR had both between- and within-subject effects on BCI performance for the majority of the pipelines. In contrast, the effect of PS on BCI performance was less robust to the selection of the pipeline and became non-significant after controlling for SNR.Significance.Taken together, our results demonstrate that changes in neuronal connectivity within the sensorimotor system are not critical for learning to control a BCI, and interventions that increase the SNR of the mu rhythm might lead to improvements in the user's BCI performance.

Immediate and Transient Perturbances in EEG Within Seconds Following Controlled Soccer Head Impact

Athletes in contact and collision sports can sustain frequent subconcussive head impacts. Although most impacts exhibit low kinematics around or below 10 g of head linear acceleration, there is growing concern regarding the cumulative effects of repetitive sports head impacts. Even mild impacts can lead to brain deformations as shown through neuroimaging and finite element modeling, and thus may result in mild and transient effects on the brain, prompting further investigations of the biomechanical dose-brain response relationship. Here we report findings from a novel laboratory study with continuous monitoring of brain activity through electroencephalography (EEG) during controlled soccer head impacts. Eight healthy participants performed simulated soccer headers at 2 mild levels (6 g, 4 rad/s and 10 g, 8 rad/s) and three directions (frontal, oblique left, oblique right). Participants were instrumented with an inertial measurement unit (IMU) bite bar and EEG electrodes for synchronized head kinematics and brain activity measurements throughout the experiment. After an impact, EEG exhibited statistically significant elevation of relative and absolute delta power that recovered within two seconds from the impact moment. These changes were statistically significantly higher for 10 g impacts compared with 6 g impacts in some topographical regions, and oblique impacts resulted in contralateral delta power increases. Post-session resting state measurements did not indicate any cumulative effects. Our findings suggest that even mild soccer head impacts could lead to immediate, transient neurophysiological changes. This study paves the way for further dose-response studies to investigate the cumulative effects of mild sports head impacts, with implications for long-term athlete brain health.

The association between social rewards and anxiety: Links from neurophysiological analysis in virtual reality and social interaction game

Individuals' affective experience can be intricate, influenced by various factors including monetary rewards and social factors during social interaction. However, within this array of factors, divergent evidence has been considered as potential contributors to social anxiety. To gain a better understanding of the specific factors associated with anxiety during social interaction, we combined a social interaction task with neurophysiological recordings obtained through an anxiety-elicitation task conducted in a Virtual Reality (VR) environment. Employing inter-subject representational similarity analysis (ISRSA), we explored the potential linkage between individuals' anxiety neural patterns and their affective experiences during social interaction. Our findings suggest that, after controlling for other factors, the influence of the partner's emotional cues on individuals' affective experiences is specifically linked to their neural pattern of anxiety. This indicates that the emergence of anxiety during social interaction may be particularly associated with the emotional cues provided by the social partner, rather than individuals' own reward or prediction errors during social interaction. These results provide further support for the cognitive theory of social anxiety and extend the application of VR in future cognitive and affective studies.

Structural inequality and temporal brain dynamics across diverse samples

BACKGROUND

Structural income inequality - the uneven income distribution across regions or countries - could affect brain structure and function, beyond individual differences. However, the impact of structural income inequality on the brain dynamics and the roles of demographics and cognition in these associations remains unexplored.

METHODS

Here, we assessed the impact of structural income inequality, as measured by the Gini coefficient on multiple EEG metrics, while considering the subject-level effects of demographic (age, sex, education) and cognitive factors. Resting-state EEG signals were collected from a diverse sample (countries = 10; healthy individuals = 1394 from Argentina, Brazil, Colombia, Chile, Cuba, Greece, Ireland, Italy, Turkey and United Kingdom). Complexity (fractal dimension, permutation entropy, Wiener entropy, spectral structure variability), power spectral and aperiodic components (1/f slope, knee, offset), as well as graph-theoretic measures were analysed.

FINDINGS

Despite variability in samples, data collection methods, and EEG acquisition parameters, structural inequality systematically predicted electrophysiological brain dynamics, proving to be a more crucial determinant of brain dynamics than individual-level factors. Complexity and aperiodic activity metrics captured better the effects of structural inequality on brain function. Following inequality, age and cognition emerged as the most influential predictors. The overall results provided convergent multimodal metrics of biologic embedding of structural income inequality characterised by less complex signals, increased random asynchronous neural activity, and reduced alpha and beta power, particularly over temporoposterior regions.

CONCLUSION

These findings might challenge conventional neuroscience approaches that tend to overemphasise the influence of individual-level factors, while neglecting structural factors. Results pave the way for neuroscience-informed public policies aimed at tackling structural inequalities in diverse populations.

Electrophysiological functional connectivity and complexity reflecting cognitive processing speed heterogeneity in young children with ADHD

Early intervention is imperative for young children with attention-deficit/hyperactivity disorder (ADHD) who manifest heterogeneous neurocognitive deficits. The study investigated the functional connectivity and complexity of brain activity among young children with ADHD exhibiting a fast cognitive processing speed (ADHD-F, n = 26), with ADHD exhibiting a slow cognitive processing speed (ADHD-S, n = 17), and typically developing children (n = 35) using wireless electroencephalography (EEG) during rest and task conditions. During rest, compared with the typically developing group, the ADHD-F group displayed lower long-range intra-hemispheric connectivity, while the ADHD-S group had lower frontal beta inter-hemispheric connectivity. During task performance, the ADHD-S group displayed lower frontal beta inter-hemispheric connectivity than the typically developing group. The ADHD-S group had lower frontal inter-hemispheric connectivity in broader frequency bands than the ADHD-F group, indicating ADHD heterogeneity in mental processing speed. Regarding complexity, the ADHD-S group tended to show lower frontal entropy estimators than the typically developing group during the task condition. These findings suggest that the EEG profile of brain connectivity and complexity can aid the early clinical diagnosis of ADHD, support subgrouping young children with ADHD based on cognitive processing speed heterogeneity, and may contain specific novel neural biomarkers for early intervention planning.

A single session of sensorimotor rhythm neurofeedback enhances long-game performance in professional golfers

Enhanced Sensorimotor Rhythm activity has been linked to increased automation in motor execution. Although existing research demonstrates the positive effects of SMR neurofeedback training on improving golf putting performance, its influence on golf long-game performance remains unexplored. This study sought to address this gap by involving seventeen professional female golfers (Age =24.63 ± 3.24 years, Handicap＝2.06 ± 1.18) in a crossover-designed experiment incorporating both NFT and a no-training control condition. During the study, participants executed 40 150-yard swings while receiving continuous SMR neurofeedback. Pre- and post-testing included visual analog scales to assess psychological processes associated with SMR activities, including attention engagement, conscious motor control, and physical relaxation levels. The results revealed that a single session of NFT effectively heightened SMR power irrespective of T1 (p = .02) or T2 (p = .03), which was observed with improved swing accuracy compared to the control conditions, particularly in "To Pin" (p = .04, the absolute distance to the hole after the ball comes to a stop). Subjective assessments further indicated that SMR NFT contributed to a sense of ease and tranquility during motor preparation for the golf swing (attention engagement: p = .01, conscious motor control: p = .033, physical relaxation: p = .013), and which offered valuable insights into the potential mechanisms underlying the impact of SMR NFT on long-game performance. Additionally, in such practical applications professional athletes can utilize our single-session neurofeedback protocol to train efficiently and cost-effectively before competitions, thereby enhancing their opportunity to achieve a higher rank.

Heartbeat-related spectral perturbation of electroencephalogram reflects dynamic interoceptive attention states in the trial-by-trial classification analysis

Attending to heartbeats for interoceptive awareness initiates distinct electrophysiological responses synchronized with the R-peaks of an electrocardiogram (ECG), such as the heartbeat-evoked potential (HEP). Beyond HEP, this study proposes heartbeat-related spectral perturbation (HRSP), a time-frequency map of the R-peak locked electroencephalogram (EEG), and explores its characteristics in identifying interoceptive attention states using a classification approach. HRSPs of EEG brain components specified by independent component analysis (ICA) were used for the offline and online classification of interoceptive states. A convolutional neural network (CNN) designed specifically for HRSP was applied to publicly available data from a binary-state experiment (attending to self-heartbeats and white noise) and data from our four-state classification experiment (attending to self-heartbeats, white noise, time passage, and toe) with diverse input feature conditions of HRSP. From the dynamic state perspective, we evaluated the primary frequency bands of HRSP and the minimal number of averaging epochs required to reflect changing interoceptive attention states without compromising accuracy. We also assessed the utility of group ICA and models for classifying HRSP in new participants. The CNN for trial-by-trial HRSP with actual R-peaks demonstrated significantly higher classification accuracy than HRSP with sham, i.e., randomly positioned, R-peaks. Gradient-weighted class activation mapping highlighted the prominent role of theta and alpha bands between 200-600 ms post-R-peak-features absent in classifications using sham HRSPs. Online classification benefits from employing a group ICA and classification model, ensuring reliable accuracy without individual EEG precollection. These results suggest HRSP's potential to reflect interoceptive attention states, proposing transformative implications for clinical applications.

Improved ADHD Diagnosis Using EEG Connectivity and Deep Learning through Combining Pearson Correlation Coefficient and Phase-Locking Value

Attention Deficit Hyperactivity Disorder (ADHD) is a widespread neurobehavioral disorder affecting children and adolescents, requiring early detection for effective treatment. EEG connectivity measures can reveal the interdependencies between EEG recordings, highlighting brain network patterns and functional behavior that improve diagnostic accuracy. This study introduces a novel ADHD diagnostic method by combining linear and nonlinear brain connectivity maps with an attention-based convolutional neural network (Att-CNN). Pearson Correlation Coefficient (PCC) and Phase-Locking Value (PLV) are used to create fused connectivity maps (FCMs) from various EEG frequency subbands, which are then inputted into the Att-CNN. The attention module is strategically placed after the latest convolutional layer in the CNN. The performance of different optimizers (Adam and SGD) and learning rates are assessed. The suggested model obtained 98.88%, 98.41%, 98.19%, and 98.30% for accuracy, precision, recall, and F1 Score, respectively, using the SGD optimizer in the FCM of the theta band with a learning rate of 1e-1. With the use of FCM, Att-CNN, and advanced optimizers, the proposed technique has the potential to produce trustworthy instruments for the early diagnosis of ADHD, greatly enhancing both patient outcomes and diagnostic accuracy.

Decoded EEG neurofeedback-guided cognitive reappraisal training for emotion regulation

Neurofeedback, when combined with cognitive reappraisal, offers promising potential for emotion regulation training. However, prior studies have predominantly relied on functional magnetic resonance imaging, which could impede its clinical feasibility. Furthermore, these studies have primarily focused on reducing negative emotions while overlooking the importance of enhancing positive emotions. In our current study, we developed a novel electroencephalogram (EEG) neurofeedback-guided cognitive reappraisal training protocol for emotion regulation. We recruited forty-two healthy subjects (20 females; 22.4 ± 2.2 years old) who were randomly assigned to either the neurofeedback group or the control group. We evaluated the efficacy of this newly proposed neurofeedback training approach in regulating emotions evoked by pictures with different valence levels (low positive and high negative). Initially, we trained an EEG-based emotion decoding model for each individual using offline data. During the training process, we calculated the subjects' real-time self-regulation performance based on the decoded emotional states and fed it back to the subjects as feedback signals. Our results indicate that the proposed decoded EEG neurofeedback-guided cognitive reappraisal training protocol significantly enhanced emotion regulation performance for stimuli with low positive valence. Additionally, wavelet energy and differential entropy features in the high-frequency band played a crucial role in emotion classification and were associated with neural plasticity changes induced by emotion regulation. These findings validate the beneficial effects of the proposed EEG neurofeedback protocol and offer insights into the neural mechanisms underlying its training effects. This novel decoded neurofeedback training protocol presents a promising cost-effective and non-invasive treatment technique for emotion-related mental disorders.

Electrophysiological (EEG) microstates during dream-like bizarre experiences in a naturalistic scenario using immersive virtual reality

Monitoring the reality status of conscious experience is essential for a human being to interact successfully with the external world. Despite its importance for everyday functioning, reality monitoring can systematically become erroneous, for example, while dreaming or during hallucinatory experiences. To investigate brain processes associated with reality monitoring occurring online during an experience, i.e., perceptual reality monitoring, we assessed EEG microstates in healthy, young participants. In a within-subjects design, we compared the experience of reality when being confronted with dream-like bizarre elements versus realistic elements in an otherwise highly naturalistic real-world scenario in immersive virtual reality. Dream-like bizarreness induced changes in the subjective experience of reality and bizarreness, and led to an increase in the contribution of a specific microstate labelled C'. Microstate C' was related to the suspension of disbelief, i.e. the suppression of bizarre mismatches. Together with the functional interpretation of microstate C' as reported by previous studies, the findings of this study point to the importance of prefrontal meta-conscious control processes in perceptual reality monitoring.

Effect of music therapy on short-term psychological and physiological outcomes in mechanically ventilated patients: A randomized clinical pilot study

Background

Elevated anxiety levels are common in patients on mechanical ventilation (MV) and may challenge recovery. Research suggests music-based interventions may reduce anxiety during MV. However, studies investigating specific music therapy techniques, addressing psychological and physiological well-being in patients on MV, are scarce.

Methods

This three-arm randomized clinical pilot study was conducted with MV patients admitted to the intensive care unit (ICU) of Hospital San José in Bogotá, Colombia between March 7, 2022, and July 11, 2022. Patients were divided into three groups: intervention group 1 (IG1), music-assisted relaxation; intervention group 2 (IG2), patient-preferred therapeutic music listening; and control group (CG), standard care. The main outcome measure was the 6-item State-Anxiety Inventory. Secondary outcomes were: pain (measured with a visual analog scale), resilience (measured with the Brief Resilience Scale), agitation/sedation (measured with the Richmond Agitation-Sedation Scale), vital signs (including heart rate, blood pressure, oxygen saturation, and respiratory rate), days of MV, extubation success, and days in the ICU. Additionally, three patients underwent electroencephalography during the interventions.

Results

Data from 23 patients were analyzed in this study. The age range of the patients was 24.0-84.0 years, with a median age of 66.0 years (interquartile range: 57.0-74.0). Of the 23 patients, 19 were female (82.6%). No statistically significant differences between the groups were observed for anxiety (P=0.330), pain (P=0.624), resilience (P=0.916), agitation/sedation (P=0.273), length of ICU stay (P=0.785), or vital signs. A statistically significant difference between the groups was found for days of MV (P=0.019). Electroencephalography measurements showed a trend toward delta and theta band power decrease for two patients and a power increase on both beta frequencies (slow and fast) in the frontal areas of the brain for one patient.

Conclusions

In this pilot study, music therapy did not significantly affect the anxiety levels in patients on MV. However, the interventions were widely accepted by the staff, patients, and caregivers and were safe, considering the critical medical status of the participants. Further large-scale randomized controlled trials are needed to investigate the potential benefits of music therapeutic interventions in this population.Trial Registration ISRCTN trial registry identifier: ISRCTN16964680.

The Neural Correlates of Spontaneous Beat Processing and Its Relationship with Music-Related Characteristics of the Individual

In the presence of temporally organized stimuli, there is a tendency to entrain to the beat, even at the neurological level. Previous research has shown that when adults listen to rhythmic stimuli and are asked to imagine the beat, their neural responses are the same as when the beat is physically accented. The current study explores the neural processing of simple beat structures where the beat is physically accented or inferred from a previously presented physically accented beat structure in a passive listening context. We further explore the associations of these neural correlates with behavioral and self-reported measures of musicality. Fifty-seven participants completed a passive listening EEG paradigm, a behavioral rhythm discrimination task, and a self-reported musicality questionnaire. Our findings suggest that when the beat is physically accented, individuals demonstrate distinct neural responses to the beat in the beta (13-23 Hz) and gamma (24-50 Hz) frequency bands. We further find that the neural marker in the beta band is associated with individuals' self-reported musical perceptual abilities. Overall, this study provides insights into the neural correlates of spontaneous beat processing and its connections with musicality.

The time course of Cantonese and Hong Kong Sign Language phonological activation: An ERP study of deaf bimodal bilingual readers of Chinese

This study investigated Cantonese and Hong Kong Sign Language (HKSL) phonological activation patterns in Hong Kong deaf readers using the ERP technique. Two experiments employing the error disruption paradigm were conducted while recording participants' EEGs. Experiment 1 focused on orthographic and speech-based phonological processing, while Experiment 2 examined sign-phonological processing. ERP analyses focused on the P200 (180-220 ms) and N400 (300-500 ms) components. The results of Experiment 1 showed that hearing readers exhibited both orthographic and phonological effects in the P200 and N400 windows, consistent with previous studies on Chinese reading. In deaf readers, significant speech-based phonological effects were observed in the P200 window, and orthographic effects spanned both the P200 and N400 windows. Comparative analysis between the two groups revealed distinct spatial distributions for orthographic and speech-based phonological ERP effects, which may indicate the engagement of different neural networks during early processing stages. Experiment 2 found evidence of sign-phonological activation in both the P200 and N400 windows among deaf readers, which may reflect the involvement of sign-phonological representations in early lexical access and later semantic integration. Furthermore, exploratory analysis revealed that higher reading fluency in deaf readers correlated with stronger orthographic effects in the P200 window and diminished effects in the N400 window, indicating that efficient orthographic processing during early lexical access is a distinguishing feature of proficient deaf readers.