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Introduction: Transcranial alternating current stimulation (tACS) is a promising tool for modulating brain oscillations. This study investigated whether 5 Hz tACS could modulate neural oscillations in the prefrontal cortex and how this modulation impacts performance in working memory (WM) tasks. Method: In two sessions, 28 healthy participants received 5 Hz tACS or sham stimulation over the left dorsolateral prefrontal cortex (DLPFC) while performing tasks with high and low WM loads. Resting-state EEG was recorded before and after stimulations for 5 minutes. EEG power was measured at electrodes surrounding the stimulation site. Results: The results showed that tACS significantly improved reaction time (RT) compared to sham stimulation. This effect was task-specific, as tACS improved RT for hit responses only in high WM load trials, with no impact on low-load trials. Moreover, tACS significantly increased EEG power at 5 Hz and in the theta band compared to pre-stimulation levels. Discussion: These findings demonstrate that tACS applied over left DLPFC modulates post-stimulation brain oscillations at the stimulation sites – known as tACS after-effects. Furthermore, the results suggest that 5 Hz tACS enhances response speed by elevating task-related activity in the prefrontal cortex to an optimal level for task performance. Conclusion: In summary, the findings highlight the potential of tACS as a technique for modulating specific brain oscillations, with implications for research and therapeutic interventions. [ABSTRACT FROM AUTHOR]

Introduction: Major Depressive Disorder (MDD) leads to dysfunction and impairment in neurological structures and cognitive functions. Despite extensive research, the pathophysiological mechanisms and effects of MDD on the brain remain unclear. This study aims to assess the impact of MDD on brain activity using EEG power spectral analysis and asymmetry metrics. Methods: EEG recordings were obtained from 48 patients with MDD and 78 healthy controls. The data were segmented into 2-second windows (1024 data points) and analyzed using the Welch method, an advanced variant of the Fast Fourier Transform (FFT). A Hanning time window with 50% overlap was applied to compute the modified periodogram. Absolute and relative power, along with asymmetry values in the theta, alpha, and beta frequency bands, were calculated. Results: Patients with MDD exhibited significantly higher absolute and relative power in the theta and beta bands and decreased power in the alpha band compared to healthy controls. Asymmetry analysis revealed significant differences between symmetric channels in the theta band (F7-F8, C3-C4, T3-T4, T5-T6), alpha band (F7-F8, C3-C4, T3-T4, T5-T6, O1-O2), and beta band (C3-C4, T3-T4, T5-T6, P3-P4). Discussion: The findings suggest that MDD affects brain mechanisms and cognitive functions, as evidenced by altered power values in the theta and alpha bands. Additionally, asymmetry values in theta, alpha, and beta bands may serve as potential biomarkers for MDD. This study highlights that beyond the commonly used alpha asymmetry, theta and beta asymmetry can also provide valuable insights into the neurophysiological effects of MDD, aligning with previous neuroimaging studies that indicate impairments in memory, attention, and neuroanatomical connectivity in MDD. [ABSTRACT FROM AUTHOR]

Introduction: The study of attention has been pivotal in advancing our comprehension of cognition. The goal of this study is to investigate which EEG data representations or features are most closely linked to attention, and to what extent they can handle the cross-subject variability. Methods: We explore the features obtained from the univariate time series from a single EEG channel, such as time domain features and recurrence plots, as well as representations obtained directly from the multivariate time series, such as global field power or functional brain networks. To address the cross-subject variability in EEG data, we also investigate persistent homology features that are robust to different types of noise. The performance of the different EEG representations is evaluated with the Support Vector Machine (SVM) accuracy on the WithMe data derived from a modified digit span experiment, and is benchmarked against baseline EEG-specific models, including a deep learning architecture known for effectively learning task-specific features. Results: The raw EEG time series outperform each of the considered data representations, but can fall short in comparison with the black-box deep learning approach that learns the best features. Discussion: The findings are limited to the WithMe experimental paradigm, highlighting the need for further studies on diverse tasks to provide a more comprehensive understanding of their utility in the analysis of EEG data. [ABSTRACT FROM AUTHOR]

Significant strides have been made in emotion recognition from Electroencephalography (EEG) signals. However, effectively modeling the diverse spatial, spectral, and temporal features of multi-channel brain signals remains a challenge. This paper proposes a novel framework, the Directional Spatial and Spectral Attention Network (DSSA Net), which enhances emotion recognition accuracy by capturing critical spatial-spectral-temporal features from EEG signals. The framework consists of three modules: Positional Attention (PA), Spectral Attention (SA), and Temporal Attention (TA). The PA module includes Vertical Attention (VA) and Horizontal Attention (HA) branches, designed to detect active brain regions from different orientations. Experimental results on three benchmark EEG datasets demonstrate that DSSA Net outperforms most competitive methods. On the SEED and SEED-IV datasets, it achieves accuracies of 96.61% and 85.07% for subject-dependent emotion recognition, respectively, and 87.03% and 75.86% for subject-independent recognition. On the DEAP dataset, it attains accuracies of 94.97% for valence and 94.73% for arousal. These results showcase the framework's ability to leverage both spatial and spectral differences across brain hemispheres and regions, enhancing classification accuracy for emotion recognition. [ABSTRACT FROM AUTHOR]

This study examines the impact of positive and negative feedback on recall of past decisions, focusing on behavioral performance and electrophysiological (EEG) responses. Participants completed a decision-making task involving 10 real-life scenarios, each followed by immediate positive or negative feedback. In a recall phase, participants' accuracy (ACC), errors (ERRs), and response times (RTs) were recorded alongside EEG data to analyze brain activity patterns related to recall. Results indicate that accurately recalled decisions with positive feedback had slower RTs, suggesting an attentional bias toward positive information that could increase cognitive load during memory retrieval. A lack of difference in recall accuracy implies that social stimuli and situational goals may influence the positivity bias. EEG data showed distinct patterns: lower alpha band activity in frontal regions (AF7, AF8) for both correct and incorrect decisions recall, reflecting focused attention and cognitive control. Correctly recalled decisions with negative feedback showed higher delta activity, often linked to aversive processing, while incorrect recalls with negative feedback showed higher beta and gamma activity. A theta band feedback-dependent modulation in electrode activity showed higher values for decisions with negative feedback, suggesting memory suppression. These findings suggest that recalling decisions linked to self-threatening feedback may require greater cognitive effort, as seen in increased beta and gamma activity, which may indicate motivational processing and selective memory suppression. This study provides insights into the neural mechanisms of feedback-based memory recall, showing how feedback valence affects not only behavioral outcomes but also the cognitive and emotional processes involved in decision recall. [ABSTRACT FROM AUTHOR]

Marketing plays a vital role in the success of a business, driving customer engagement, brand recognition, and revenue growth. Neuromarketing adds depth to this by employing insights into consumer behavior through brain activity and emotional responses to create more effective marketing strategies. Electroencephalogram (EEG) has typically been utilized by researchers for neuromarketing, whereas Eye Tracking (ET) has remained unexplored. To address this gap, we propose a novel multimodal approach to predict consumer choices by integrating EEG and ET data. Noise from EEG signals is mitigated using a bandpass filter, Artifact Subspace Reconstruction (ASR), and Fast Orthogonal Regression for Classification and Estimation (FORCE). Class imbalance is handled by employing the Synthetic Minority Over-sampling Technique (SMOTE). Handcrafted features, including statistical and wavelet features, and automated features from Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM), have been extracted and concatenated to generate a feature space representation. For ET data, preprocessing involved interpolation, gaze plots, and SMOTE, followed by feature extraction using LeNet-5 and handcrafted features like fixations and saccades. Multimodal feature space representation was generated by performing feature-level fusion for EEG and ET, which was later fed into a meta-learner-based ensemble classifier with three base classifiers, including Random Forest, Extended Gradient Boosting, and Gradient Boosting, and Random Forest as the meta-classifier, to perform classification between buy vs. not buy. The performance of the proposed approach is evaluated using a variety of performance metrics, including accuracy, precision, recall, and F1 score. Our model demonstrated superior performance compared to competitors by achieving 84.01% accuracy in predicting consumer choices and 83% precision in identifying positive consumer preferences. [ABSTRACT FROM AUTHOR]

Background: As a clinical precursor to Alzheimer's disease (AD), amnestic mild cognitive impairment (aMCI) bears a considerably heightened risk of transitioning to AD compared to cognitively normal elders. Early prediction of whether aMCI will progress to AD is of paramount importance, as it can provide pivotal guidance for subsequent clinical interventions in an early and effective manner. Methods: A total of 107 aMCI cases were enrolled and their electroencephalogram (EEG) data were collected at the time of the initial diagnosis. During 18-month follow-up period, 42 individuals progressed to AD (PMCI), while 65 remained in the aMCI stage (SMCI). Spectral, nonlinear, and functional connectivity features were extracted from the EEG data, subjected to feature selection and dimensionality reduction, and then fed into various machine learning classifiers for discrimination. The performance of each model was assessed using 10-fold cross-validation and evaluated in terms of accuracy (ACC), area under the curve (AUC), sensitivity (SEN), specificity (SPE), positive predictive value (PPV), and F1-score. Results: Compared to SMCI patients, PMCI patients exhibit a trend of "high to low" frequency shift, decreased complexity, and a disconnection phenomenon in EEG signals. An epoch-based classification procedure, utilizing the extracted EEG features and k -nearest neighbor (KNN) classifier, achieved the ACC of 99.96%, AUC of 99.97%, SEN of 99.98%, SPE of 99.95%, PPV of 99.93%, and F1-score of 99.96%. Meanwhile, the subject-based classification procedure also demonstrated commendable performance, achieving an ACC of 78.37%, an AUC of 83.89%, SEN of 77.68%, SPE of 76.24%, PPV of 82.55%, and F1-score of 78.47%. Conclusion: Aiming to explore the EEG biomarkers with predictive value for AD in the early stages of aMCI, the proposed discriminant framework provided robust longitudinal evidence for the trajectory of the aMCI cases, aiding in the achievement of early diagnosis and proactive intervention. [ABSTRACT FROM AUTHOR]

Background: F-8-coil repetitive transcranial magnetic stimulation (rTMS) and H-1-coil deep repetitive transcranial magnetic stimulation (dTMS) have been indicated for the treatment of major depressive disorder (MDD) in adult patients by applying different treatment protocols. Nevertheless, the evidence for long-term electrophysiological alterations in the cortex following prolonged TMS interventions, as assessed by quantitative electroencephalography (qEEG), remains insufficiently explored. This study aims to demonstrate the qEEG-based distinctions between rTMS and dTMS in the management of depression and to evaluate the potential correlation between the electrophysiological changes induced by these two distinct TMS interventions and the clinical improvement in depressive and anxiety symptoms. Methods: A total of 60 patients diagnosed with treatment resistant depression received rTMS (n = 30) or dTMS (n = 30) along with their usual treatments in Kemal Arıkan Psychiatry Clinic. All the participants underwent resting-state qEEG recording before and at the end of 30 sessions of TMS treatment. The significant qEEG changes were then tested for their correlation with the improvement in depression and anxiety. Results: After the course of rTMS and dTMS a considerable reduction is seen in the severity of depression and anxiety. Although improvements in depression and anxiety were observed in both TMS groups, specific neural activity patterns were associated with better outcomes in depression. Patients who exhibited lower alpha activity in the left fronto-central region and higher gamma activity in the right prefrontal region following rTMS showed more significant improvements in depression symptoms. Similarly, those whose beta activity increased in the left prefrontal region but decreased in the right prefrontal region after rTMS tended to have greater reductions in depression and anxiety severity. For patients in the dTMS group, those who demonstrated a decrease in left temporal theta activity after treatment were more likely to experience a substantial improvement in depression severity. Conclusion: Following 30 sessions of rTMS with a F8 coil and dTMS with an H1 coil, notable alterations in qEEG activity with clinical significance were discerned. The persistence of these changes should be investigated in the subsequent follow-up period. [ABSTRACT FROM AUTHOR]

Introduction: Mental health monitoring utilizing EEG analysis has garnered notable interest due to the non-invasive characteristics and rich temporal information encoded in EEG signals, which are indicative of cognitive and emotional conditions. Conventional methods for EEG-based mental health evaluation often depend on manually crafted features or basic machine learning approaches, like support vector classifiers or superficial neural networks. Despite the potential of these approaches, they often fall short in capturing the intricate spatiotemporal relationships within EEG data, leading to lower classification accuracy and poor adaptability across various populations and mental health scenarios. Methods: To overcome these limitations, we introduce the EEG Mind-Transformer, an innovative deep learning architecture composed of a Dynamic Temporal Graph Attention Mechanism (DT-GAM), a Hierarchical Graph Representation and Analysis (HGRA) module, and a Spatial-Temporal Fusion Module (STFM). The DT-GAM is designed to dynamically extract temporal dependencies within EEG data, while the HGRA models the brain's hierarchical structure to capture both localized and global interactions among different brain regions. The STFM synthesizes spatial and temporal elements, generating a comprehensive representation of EEG signals. Results and discussion: Our empirical results confirm that the EEG Mind-Transformer significantly surpasses conventional approaches, achieving an accuracy of 92.5%, a recall of 91.3%, an F1-score of 90.8%, and an AUC of 94.2% across several datasets. These findings underline the model's robustness and its generalizability to diverse mental health conditions. Moreover, the EEG Mind-Transformer not only pushes the boundaries of state-of-the-art EEG-based mental health monitoring but also offers meaningful insights into the underlying brain functions associated with mental disorders, solidifying its value for both research and clinical settings. [ABSTRACT FROM AUTHOR]

Introduction: This study aimed to investigate the psychological and physiological impacts of audio-visual (AV) and audio-visual-olfactory (AVO) stimuli within an immersive virtual nature environment. Methods: Twenty-two nurses from the mental health in-patient ward of a Canadian hospital participated in the study. Each participant chose one of the three available immersive scenarios (beach, lake, waterfall) to experiment with under the AV and AVO conditions. Psychological assessments were conducted via questionnaires to investigate relaxation levels and other measures of user experience. Although the AVO condition demonstrated the greatest improvement in relaxation relative to baseline, no significant differences were observed either between the conditions or across the scenarios. Physiological metrics were collected using an in-house instrumented Oculus Quest 2 virtual reality (VR) headset that allowed us to investigate brain activity via electroencephalography (EEG). Results: Results show a significant difference between the two conditions in certain brain regions. Significant differences in neural patterns were also seen for the participants who reported improvements in relaxation, relative to those who did not report any improvements. For these latter-referenced participants, decreased relaxation resulted from the non-congruence of the presented smells with participant expectations. Furthermore, neuromarkers measured from the EEG, such as frontal alpha asymmetry (a measure of approach/withdrawal), engagement score, as well as valence and arousal indices suggested increased relaxation levels in the AVO condition. Conclusion: Our results suggest that multisensory immersive experiences can impact both physiological and psychological outcomes, resulting in increased relaxation levels and enhanced sense engagement for certain scenes. The instrumented VR headset enabled the monitoring of user neural and behavioural patterns, thus allowing for new insights to be gained beyond those achievable with only questionnaires. [ABSTRACT FROM AUTHOR]

Introduction: Modeling multi-channel electroencephalographic (EEG) time-series is a challenging tasks, even for the most recent deep learning approaches. Particularly, in this work, we targeted our efforts to the high-fidelity reconstruction of this type of data, as this is of key relevance for several applications such as classification, anomaly detection, automatic labeling, and brain-computer interfaces. Methods: We analyzed the most recent works finding that high-fidelity reconstruction is seriously challenged by the complex dynamics of the EEG signals and the large inter-subject variability. So far, previous works provided good results in either high-fidelity reconstruction of single-channel signals, or poor-quality reconstruction of multi-channel datasets. Therefore, in this paper, we present a novel deep learning model, called hvEEGNet, designed as a hierarchical variational autoencoder and trained with a new loss function. We tested it on the benchmark Dataset 2a (including 22-channel EEG data from 9 subjects). Results: We show that it is able to reconstruct all EEG channels with high-fidelity, fastly (in a few tens of epochs), and with high consistency across different subjects. We also investigated the relationship between reconstruction fidelity and the training duration and, using hvEEGNet as an anomaly detector, we spotted some data in the benchmark dataset that are corrupted and never highlighted before. Discussion: Thus, hvEEGNet could be very useful in several applications where automatic labeling of large EEG dataset is needed and time-consuming. At the same time, this work opens new fundamental research questions about (1) the effectiveness of deep learning models training (for EEG data) and (2) the need for a systematic characterization of the input EEG data to ensure robust modeling. [ABSTRACT FROM AUTHOR]

Introduction: Time perception is a fundamental cognitive function, the brain mechanisms of which are not fully understood. Recent electroencephalography (EEG) studies have shown that neural oscillations in specific frequency bands may play a role in this process. In the current study, we sought to investigate how neurophysiological activity of cortical structures relates to subjective time estimations. Methods: The study sample included 41 healthy volunteers, who were to produce subjective minutes with eyes closed and open by pressing the response button marking the beginning and end of this time interval. High-density EEG was recorded in parallel and the activity of cortical sources within the theta, alpha, and beta frequency bands was analyzed with standardized low-resolution brain electromagnetic tomography. Results: The results revealed that activity of several cortical structures within the beta-band correlated with the duration of subjective minutes across participants, which highlights the role of the beta-rhythm in supra-second time perception. The sets of involved structures were different depending on eyes being open or closed, while the produced duration did not differ being around 58 s in both conditions. Individual minute correlated with beta power in the left precuneus, left superior parietal lobule, and right superior frontal gyrus (SFG) during eyes-closed sessions, and with that in the caudal anterior cingulate cortex, cuneus, posterior cingulate cortex, parahippocampal gyrus, and right lingual gyrus during the eyes-open condition. Noteworthy, some structures showed tendencies toward opposite correlations between conditions. Discussion: Taken together, our findings bridge the gap between functional magnetic resonance imaging and EEG time perception studies and suggest reliance on different aspects of subjective experience when judging about time with eyes open or closed. [ABSTRACT FROM AUTHOR]

Introduction: Cognitive deficits are common in psychiatric and mental health disorders, making the assessment of cognitive function in mental health treatment an important area of research. Cognitive Function Development Therapy (CFDT) is a novel therapeutic modality designed to enhance cognitive function and regulate the autonomic nervous system through targeted exercises and activities focused on attention networks and memory systems. The therapy is tracked and based on Primary Cognitive Function (PCF) scores. Methods: This retrospective, observational study analyzed real world data from 183 children and adults undergoing CFDT to evaluate changes in cognition over time, incorporating both cognitive performance measures and an exploratory analysis of neurophysiological function. Objective neurophysiological measures in the form of the brain vital signs framework, based in event-related potentials (ERPs), were measured in a small subset of clients to explore the frameworks use in CFDT. Results: Our findings indicate that CFDT holds promise for improving cognitive performance, as evidenced by increased PCF scores at the group level compared to pre-treatment levels [ F (5, 173) = 7.087, p < 0.001, ηp2 = 0.170]. Additionally, a weak effect of age [Spearman's Rho range: −0.301 to −0.340, p < 0.001] was found to influence the degree of cognitive improvement, suggesting the importance of early intervention for maximizing cognitive gains. The exploratory analysis suggested that CFDT may affect neurophysiological measures of information processing, particularly in basic attention, as reflected in increased amplitude in P300 measures. Discussion: While these initial findings are encouraging, caution is warranted due to the retrospective nature of the study, though overall, the results suggest a positive impact of CFDT on cognitive function. [ABSTRACT FROM AUTHOR]

Background: Mice play a crucial role in studying the mechanisms of general anesthesia. However, identifying reliable EEG markers for different depths of anesthesia induced by multifarious agents remains a significant challenge. Spindle activity, typically observed during NREM sleep, reflects synchronized thalamocortical activity and is characterized by a frequency range of 7–15 Hz and a duration of 0.5–3 s. Similar patterns, referred to as "anesthetic spindles," are also observed in the EEG during general anesthesia. However, the variability of anesthetic spindles across different anesthetic agents and depths is not yet fully understood. Method: Mice were anesthetized with dexmedetomidine, propofol, ketamine, etomidate, isoflurane, or sevoflurane, and cortical EEG recordings were obtained. EEG signals were bandpass filtered between 0.1 and 60 Hz and analyzed using a custom MATLAB script for spindle detection. Anesthesia depth was assessed based on Guedel's modified stages of anesthesia and the presence of burst suppression in the EEG. Results: Compared to sleep spindles, anesthetic spindles induced by the different agents exhibited higher amplitudes and longer durations. Isoflurane- and sevoflurane-induced spindles varied with the depth of anesthesia. Spindles associated with etomidate were prominent during induction and light anesthesia, whereas those induced by sevoflurane and isoflurane were more dominant during deep anesthesia and emergence. Post-anesthesia, spindles persisted but ceased more quickly following inhalational anesthesia. Conclusion: Anesthesia spindle waves reflect distinct changes in anesthesia depth and persist following emergence, serving as objective EEG markers for assessing both anesthesia depth and the recovery process. [ABSTRACT FROM AUTHOR]

Introduction: Emotion recognition using electroencephalography (EEG) is a key aspect of brain-computer interface research. Achieving precision requires effectively extracting and integrating both spatial and temporal features. However, many studies focus on a single dimension, neglecting the interplay and complementarity of multi-feature information, and the importance of fully integrating spatial and temporal dynamics to enhance performance. Methods: We propose the Spatiotemporal Adaptive Fusion Network (STAFNet), a novel framework combining adaptive graph convolution and temporal transformers to enhance the accuracy and robustness of EEG-based emotion recognition. The model includes an adaptive graph convolutional module to capture brain connectivity patterns through spatial dynamic evolution and a multi-structured transformer fusion module to integrate latent correlations between spatial and temporal features for emotion classification. Results: Extensive experiments were conducted on the SEED and SEED-IV datasets to evaluate the performance of STAFNet. The model achieved accuracies of 97.89% and 93.64%, respectively, outperforming state-of-the-art methods. Interpretability analyses, including confusion matrices and t-SNE visualizations, were employed to examine the influence of different emotions on the model's recognition performance. Furthermore, an investigation of varying GCN layer depths demonstrated that STAFNet effectively mitigates the over-smoothing issue in deeper GCN architectures. Discussion: In summary, the findings validate the effectiveness of STAFNet in EEG-based emotion recognition. The results emphasize the critical role of spatiotemporal feature extraction and introduce an innovative framework for feature fusion, advancing the state of the art in emotion recognition. [ABSTRACT FROM AUTHOR]

Decades of research on joint attention, coordinated joint engagement, and social contingency identify caregiver-child interaction in infancy as a foundation for language. These patterns of early behavioral synchrony contribute to the structure and connectivity of the brain in the temporoparietal regions typically associated with language skills. Thus, children attune to their communication partner and subsequently build cognitive skills directly relating to comprehension and production of language, literacy skills, and beyond. This has yielded marked interest in measuring this contingent, synchronous social behavior neurally. Neurological measures of early social interactions between caregiver and child have become a hotbed for research. In this paper, we review that research and suggest that these early neural couplings between adults and children lay the foundation for a broader cognitive system that includes attention, problem solving, and executive function skills. This review describes the role of behavioral synchrony in language development, asks what the relationship is between neural synchrony and language growth, and how neural synchrony may play a role in the development of a broader cognitive system founded in a socially-gated brain. We address the known neural correlates of these processes with an emphasis on work that examines the tight temporal contingency between communicative partners during these rich social interactions, with a focus on EEG and fNIRS and brief survey of MRI and MEG. [ABSTRACT FROM AUTHOR]

Introduction: Multisensory integration (MSI) enhances perception by combining information from different sensory modalities. In schizophrenia, individuals often exhibit impaired audiovisual processing, resulting in broader temporal binding windows (TBWs) which appear to be associated with symptom severity. Since the underlying mechanisms of these aberrations are not yet fully understood, the present study aims to investigate multisensory processing in schizophrenia in more detail. Methods: Individuals with schizophrenia (SZ) and healthy controls (HC) performed a simultaneity judgement task, a paradigm that is suitable for the examination of multisensory integration processes. The paradigm was also conducted to allow for the comparison of perceptions under ecologically valid and invalid conditions. Additionally, EEG recordings were made to explore underlying neural mechanisms. Results: In line with previous research, we replicated enlarged TBWs in SZ compared to HC, independent of ecological validity. Neurophysiological data further revealed reduced amplitudes in the early ERP complex N1/P2 in SZ compared to HC. Discussion: Since amplitude reduction in the N1/P2 complex is often associated with audiovisual integration processes, the results highlight perceptual dysfunction in SZ, particularly concerning the disengagement of auditory and visual stimuli. [ABSTRACT FROM AUTHOR]

HIV-associated neurocognitive disorders (HAND) are one of the common complications in people living with HIV (PLWH), which can affect their attention, working memory, and other related cognitive functions. With the widespread use of combination antiretroviral therapy (cART), the incidence of HAND has declined. However, HAND is still an important complication of HIV, which not only affects the quality of life of patients but also affects their adherence to HIV treatment. Its diagnosis mainly relies on neurocognitive tests, which have a certain degree of subjectivity, making it difficult to diagnose and classify HAND accurately, and there is an urgent need to explore more sensitive biomarkers. Multimodal brain imaging has seen a surge in recent years with simultaneous EEG-fMRI being at the forefront of cognitive multimodal neuroimaging. It is a complementary fusion technique that effectively combines the high spatial resolution of fMRI with the high temporal resolution of EEG, compensating for the shortcomings of a single technique and providing a new method for studying cognitive function. It is expected to reveal the underlying mechanisms of HAND and provide high spatiotemporal warning biomarkers of HAND, which will provide a new perspective for the early diagnosis and treatment of HAND and contribute to the improvement of patient prognosis. [ABSTRACT FROM AUTHOR]

Despite the long history of the horse-human bond, our understanding of the brain and mind of horses remains limited due to the lack of methods to investigate their brain functions. This study introduces a novel methodology for completely non-invasive, multi-channel recording of electroencephalography (EEG) and evoked potentials in awake horses to examine equine auditory cortical processing. The new approach utilizes specially designed brush-shaped active electrodes that facilitate stable signal acquisition through the hair coat by penetrating electrode pins and integrated pre-amplifiers. A 12-channel electrode array provided greater scalp coverage than prior work. As a proof of concept, clear cortical auditory evoked potentials (CAEPs) were recorded in response to sound onsets and offsets. The equine CAEP waveform morphology resembled the human P1-N1-P2-N2 complex, although the latencies were shorter than typical human values. The CAEP amplitudes were maximal at centroparietal electrodes, contrasting with the frontocentral distribution seen in humans, potentially explained by differences in auditory cortex orientation between species. This non-invasive multi-electrode method enables the evaluation of cognitive abilities, normal and abnormal brain functions, and advances scientific understanding of the equine mind. It offers potential widespread applications for recording EEGs and evoked potentials in awake horses and other medium-to-large mammalian species. [ABSTRACT FROM AUTHOR]

As robots become integral to various sectors, improving human-robot collaboration is crucial, particularly in anticipating human actions to enhance safety and efficiency. Electroencephalographic (EEG) signals offer a promising solution, as they can detect brain activity preceding movement by over a second, enabling predictive capabilities in robots. This study explores how EEG can be used for action anticipation in human-robot interaction (HRI), leveraging its high temporal resolution and modern deep learning techniques. We evaluated multiple Deep Learning classification models on a motor imagery (MI) dataset, achieving up to 80.90% accuracy. These results were further validated in a pilot experiment, where actions were accurately predicted several hundred milliseconds before execution. This research demonstrates the potential of combining EEG with deep learning to enhance real-time collaborative tasks, paving the way for safer and more efficient human-robot interactions. [ABSTRACT FROM AUTHOR]

Introduction: Intersections are particularly complex traffic situations and are often the scene of accidents. Driver behaviour and decision-making might be affected by specific factors such as the right of way, traffic volume, and the occurrence of a critical event directly before the intersection. Methods: We developed a new driving scenario in virtual reality (VR) to test the impact of these factors using a fully immersive head-mounted display. Participants had to navigate through a series of intersections to reach their target destination. We recorded their driving behaviour as well as their brain activity using electroencephalography (EEG). Results: Our results showed that participants engaged cognitive control processes when approaching an intersection with high traffic volume and when reacting to a critical event, as indexed by driving behaviour and proactively by increased theta power. We did not find differences for right of way in the EEG data, but driving behaviour was as expected, revealing a driving speed reduction when participants had to yield to traffic. Discussion: We discuss advantages and potential challenges of an immersive VR-based approach to driving simulations and the challenges encountered when recording and analysing EEG data. We conclude that despite movement and electronic artefacts, EEG data in the theta and alpha bands can be analysed robustly and allow for novel insights into control processes in realistic VR scenarios. [ABSTRACT FROM AUTHOR]

Objectives: Electroencephalograms (EEGs) or multi-unit activities (MUAs) of tonic–clonic seizures typically exhibit a distinct structure. After a preliminary phase (DC shift, spikes), the tonic phase is characterized by synchronized activity of numerous neurons, followed by the clonic phase, marked by a periodic sequence of spikes. However, the mechanisms underlying the transition from tonic to clonic phases remain poorly understood. Methods: We employ a simple two-dimensional cellular automaton model to simulate seizure activity, specifically focusing on replicating the tonic–clonic transition. This model effectively illustrates the physical processes during the ictal phase and, more importantly, differentiates the roles of neurons' activity, identifying their origin as either synaptic or ephaptic. Results: Our model reveals an intriguing interaction between the synaptic and ephaptic modes of action potential wave conduction. By replicating the EEG and multi-unit activity (MUA) structure of a tonic–clonic seizure and comparing it with real MUA data, we validate the model's underlying assumption: the transition from tonic to clonic phases is driven by a shift in dominance from synaptic to ephaptic conduction. During synaptic-mode control, neural conduction occurs through synaptic transmission involving chemical substances, while in the ephaptic mode, information transfer occurs through direct Ohmic conduction. Significance: Gaining a deeper understanding of the neuronal electrical conduction transitions during tonic–clonic seizures is crucial for improving the treatment of this debilitating condition. [ABSTRACT FROM AUTHOR]

This perspective considers the novel concept of olfactory neurofeedback (O-NFB) within the framework of brain-computer interfaces (BCIs), where olfactory stimuli are integrated in various BCI control loops. In particular, electroencephalography (EEG)-based O-NFB systems are capable of incorporating different components of complex olfactory processing – from simple discrimination tasks to using olfactory stimuli for rehabilitation of neurological disorders. In our own work, EEG theta and alpha rhythms were probed as control variables for O-NFB. Additionaly, we developed an olfactory-based instructed-delay task. We suggest that the unique functions of olfaction offer numerous medical and consumer applications where O-NFB is combined with sensory inputs of other modalities within a BCI framework to engage brain plasticity. We discuss the ways O-NFB could be implemented, including the integration of different types of olfactory displays in the experiment set-up and EEG features to be utilized. We emphasize the importance of synchronizing O-NFB with respiratory rhythms, which are known to influence EEG patterns and cognitive processing. Overall, we expect that O-NFB systems will contribute to both practical applications in the clinical world and the basic neuroscience of olfaction. [ABSTRACT FROM AUTHOR]

Brain imaging performed in natural settings is known as mobile brain and body imaging (MoBI). One of the features which distinguishes MoBI and laboratory-based experiments is the body posture. Previous studies pointed to mechanical, autonomic, cortical and cognitive differences between upright stance and sitting or reclining. The purpose of this study was to analyse effects of posture on eye-movement related potentials (EMRP) recorded during free viewing of human faces. A 64-channel wireless EEG was recorded from 14 participants in either standing or reclining postures while they freely viewed pictures of emotional faces displaying fear, anger, sadness, and a neutral emotional state. Eye tracking data was used to insert triggers corresponding to the instant at which the gaze first landed on a face. Spatial filtering of the EEG data was performed using a group independent component analysis (ICA). Grand average EMRPs displayed the post-saccadic lambda component and the face-sensitive N170/vertex positive potential (VPP) complex. The lambda component but not the N170 component was stronger during reclining than upright posture. Emotional expression of faces showed no effects on EMRP components or subjective ratings. Results suggest that posture primarily affects early components of EMRPs recorded using wireless EEG recordings during free viewing of faces. Thus, findings from evoked potential data obtained in seated individuals, e.g., in laboratory experiments, should be interpreted with caution in MoBI experiments with posture affecting primarily the early latency component. [ABSTRACT FROM AUTHOR]

Background: Repetitive transcranial magnetic stimulation (rTMS) and median nerve electrical stimulation (MNES) are two non-invasive neuromodulation techniques that have demonstrated potential in facilitating the recovery of consciousness in patients with impaired consciousness. However, existing studies on awakening interventions for patients with prolonged disorders of consciousness (pDoC) following intracerebral hemorrhage remains limited. In particular, systematic comparisons of the efficacy of rTMS versus MNES in this specific patient population are lacking. Methods: This is a single-center randomized controlled trial in which 45 patients will be randomly assigned to the control group, rTMS group and MNES group. The intervention period will lasts 4 weeks. All patients underwent multimodal assessments before and at the end of treatment, which were used to comprehensively evaluate their recovery of consciousness and changes in brain function. The assessments includes the Coma Recovery Scale, electroencephalogram, event-related potentials (P300 and mismatched negative) and functional near-infrared spectroscopy. Discussion: This study represents the first systematic comparison of the efficacy between rTMS and MNES in patients with pDoC following intracerebral hemorrhage. The objective is to employ multimodal assessment techniques to provide clinical references into the individualized application of these neuromodulation therapies. Clinical trial registration: https://www.chictr.org.cn/ , identifier ChiCTR2400082022. [ABSTRACT FROM AUTHOR]

Objective: Although seizures are the cardinal feature, epilepsy is associated with other forms of brain dysfunction including impaired cognition, abnormal sleep, and increased risk of developing dementia. We hypothesized that, given the widespread neurologic dysfunction caused by epilepsy, accelerated brain aging would be seen. We measured the sleep-based brain age index (BAI) in a diverse group of patients with epilepsy. The BAI is a machine learning-based biomarker that measures how much the brain activity of a person during overnight sleep deviates from chronological age-based norms. Methods: This case–control study drew information of age-matched controls without epilepsy from home sleep monitoring volunteers and from non-epilepsy patients with Sleep Lab testing. Patients with epilepsy underwent in-patient monitoring and were classified by epilepsy type and seizure burden. The primary outcomes measured were BAI, processed from electroencephalograms, and epilepsy severity metrics (years with epilepsy, seizure frequency standardized by year, and seizure burden [number of seizures in life]). Subanalyses were conducted on a subset with NIH Toolbox cognitive testing for total, fluid, and crystallized composite cognition. Results: 138 patients with epilepsy (32 exclusively focal and 106 generalizable [focal seizures with secondary generalization]) underwent in-patient monitoring, and age-matched, non-epilepsy controls were analyzed. The mean BAI was higher in epilepsy patients vs controls and differed by epilepsy type: −0.05 years (controls) versus 5.02 years (all epilepsy, p < 0.001), 5.53 years (generalizable, p < 0.001), and 3.34 years (focal, p = 0.03). Sleep architecture was disrupted in epilepsy, especially in generalizable epilepsy. A higher BAI was positively associated with increased lifetime seizure burden in focal and generalizable epilepsies and associated with lower crystallized cognition. Lifetime seizure burden was inversely correlated with fluid, crystallized, and composite cognition. Significance: Epilepsy is associated with accelerated brain aging. Higher brain age indices are associated with poorer cognition and more severe epilepsy, specifically generalizability and higher seizure burden. These findings strengthen the use of the sleep-derived, electroencephalography-based BAI as a biomarker for cognitive dysfunction in epilepsy. [ABSTRACT FROM AUTHOR]

Limb amputation results in such devastating consequences as loss of motor and sensory functions and phantom limb pain (PLP). Neurostimulation-based approaches have been developed to treat this condition, which provide artificial somatosensory feedback such as peripheral nerve stimulation (PNS), spinal cord stimulation (SCS), and transcutaneous electrical nerve stimulation (TENS). Yet, the effectiveness of different neurostimulation methods has been rarely tested in the same participants. Meanwhile, such tests would help to select the most effective method or a combination of methods and could contribute to the development of multisensory limb prostheses. In this study, two transhumeral amputees were implanted with stimulating electrodes placed in the medial nerve and over the spinal cord epidurally. PNS and SCS were tested in each participant as approaches to enable tactile and proprioceptive sensations and suppress PLP. Both PNS and SCS induced sensation in different parts of the phantom hand, which correlated with cortical responses detected with electroencephalographic (EEG) recordings. The sensations produced by PNS more often felt natural compared to those produced by SCS. Еvoked response potentials (ERPs) were more lateralized and adapted faster for PNS compared to SCS. In the tasks performed with the bionic hand, neurostimulation-induced sensations enabled discrimination of object size. As the participants practiced with neurostimulation, they improved on the object-size discrimination task and their sensations became more natural. А combination of PNS and TENS enabled sensations that utilized both tactile and proprioceptive information. This combination was effective to convey the perception of object softness. In addition to enabling sensations, neurostimulation led to a decrease in PLP. Clinical trial registration: https://clinicaltrials.gov/ , identifier, #NCT05650931. [ABSTRACT FROM AUTHOR]

Introduction: Acupuncture is beneficial in improving visual function for myopi periocular acupoints Taiyang can improve contrast sensitivity (CS). In this study, we aim to further investigate the impact of acupuncture at the limbs acupoints-Guangming(GB37) acupoint on visual function, and the neural mechanism of acupuncture at the GB37 acupoint improving visual function through electroencephalography (EEG) microstate. Methods: A total of 22 myopia were recruited. Uncorrected distance visual acuity (UDVA) and CS of myopic patients were tested before and after acupuncture, and EEG data were recorded throughout the entire acupuncture procedure. Results: Our study found that compared with pre-acupuncture, the UDVA and CS of both eyes at each spatial frequency were improved; compared with the resting state of pre-acupuncture, the duration, occurrence and contribution of microstate A were significantly increased, while those of microstate D were decreased during the post-acupuncture state. The duration of microstate A was positively correlated with the CS. There was no correlation between UDVA and EEG microstates. Discussion: Acupuncture at GB37 can improve the UDVA and CS in myopic patients, which may be related to microstate A. [ABSTRACT FROM AUTHOR]

Introduction: In severe cases, an infection with the Borna Disease Virus 1 (BoDV-1), the causative agent of Borna disease in horses, sheep, and other domestic mammals, was reported to be accompanied by cognitive dysfunctions, seizures, deep coma, or severe to fatal encephalitis in humans. In addition, asymptomatic or mild courses of BoDV-1 infection are discussed to act as a co-factor in the etiology of Major Depressive Disorder (MDD). Previously, studies using electroencephalography (EEG) reported BoDV-1-dependent changes in event-related potentials (ERPs), thus indicating the use and added value of non-invasive studies in Borna research. Methods: Here, we examined possible connections between BoDV-1 status, EEG restingstate alpha activity, and serum levels of pro-inflammatory Interleukin 6 (IL-6) and Interleukin 8 (IL-8) in MDD patients and in a comparison group of adults without MDD diagnosis. Results: Interestingly, for both groups, we revealed a comparable high number of BoDV-1 positive and BoDV-1 negative participants. Compared to adults without MDD diagnosis, MDD patients showed a decrease in their relative EEG alpha power at posterio-central, but increased values at anterio-central electrode sites. Most important, no group-dependent effect of BoDV-1 status on EEG resting-state activity had been observed. Compared to BoDV-1 positive and negative adults without MDD diagnosis, as well as BoDV-1 positive MDD patients, BoDV-1 negative MDD patients revealed a comparatively weak significant negative correlation between relative fronto-central EEG alpha power and concentrations of pro-inflammatory IL-8. Discussion: Taken together, our data confirm MDD-dependent alterations in EEG resting-state alpha activity, which, however, were not accompanied by major BoDV-1 dependent neurophysiological or immunological effects. Future – probably more invasive – studies further have to clarify the significance of the observed negative correlation between relative fronto-central EEG alpha power and concentrations of pro-inflammatory IL-8. [ABSTRACT FROM AUTHOR]

Objective: Seizure prediction could improve quality of life for patients through removing uncertainty and providing an opportunity for acute treatments. Most seizure prediction models use feature engineering to process the EEG recordings. Long-Short Term Memory (LSTM) neural networks are a recurrent neural network architecture that can display temporal dynamics and, therefore, potentially analyze EEG signals without performing feature engineering. In this study, we tested if LSTMs could classify unprocessed EEG recordings to make seizure predictions. Methods: Long-term intracranial EEG data was used from 10 patients. 10-s segments of EEG were input to LSTM models that were trained to classify the EEG signal. The final seizure prediction was generated from 5 outputs of the LSTM model over 50 s and combined with time information to account for seizure cycles. Results: The LSTM models could make predictions significantly better than a random predictor. When compared to other publications using the same dataset, our model performed better than several others and was comparable to the best models published to date. Furthermore, this framework could still produce predictions significantly better than chance when the experimental paradigm design was altered, without the need to reperform feature engineering. Significance: Removing the need to perform feature engineering is an advancement on previously published models. This framework can be applied to many different patients' needs and a variety of acute interventions. Also, it opens the possibility of personalized seizure predictions that can be altered to meet daily needs. [ABSTRACT FROM AUTHOR]

Background: Applying convolutional neural networks to a large number of EEG signal samples is computationally expensive because the computational complexity is linearly proportional to the number of dimensions of the EEG signal. We propose a new Gated Recurrent Unit (GRU) network model based on reinforcement learning, which considers the implementation of attention mechanisms in Electroencephalogram (EEG) signal processing scenarios as a reinforcement learning problem. Methods: The model can adaptively select target regions or position sequences from inputs and effectively extract information from EEG signals of different resolutions at multiple scales. Just as convolutional neural networks benefit from translation invariance, our proposed network also has a certain degree of translation invariance, making its computational complexity independent of the EEG signal dimension, thus maintaining a lower learning cost. Although the introduction of reinforcement learning makes the model non differentiable, we use policy gradient methods to achieve end-to-end learning of the model. Results: We evaluated our proposed model on publicly available EEG dataset (BCI Competition IV-2a). The proposed model outperforms the current state-of-the-art techniques in the BCI Competition IV- 2a dataset with an accuracy of 86.78 and 71.54% for the subject-dependent and subject-independent modes, respectively. Conclusion: In the field of EEG signal processing, attention models that combine reinforcement learning principles can focus on key features, automatically filter out noise and redundant data, and improve the accuracy of signal decoding. [ABSTRACT FROM AUTHOR]

In order to accurately measure the level of competency frustration and what measures to take to alleviate the negative effects of competency frustration, 35 graduate students were selected to verify the effect of the frontal lobe α asymmetry (FAA) as a judgement of the competence frustration level using EEG experimental method. On this basis, through two stopwatch stopping experiments, 108 fire rescue personnel were selected to conduct the experiments to investigate the intervention effects of developmental feedback and compassion-focused therapy in turn. The results showed that frontal α asymmetry could be used as an EEG indicator for judging competency frustration, and the intervention method of compassion-focused therapy reduced the level of competency frustration of the subjects, while developmental feedback interventions did the opposite. The difference in the effects of the two intervention methods indicates that when intervening in competence frustration, it is easier to reduce the competency frustration by focusing on the subjects themselves than focusing on the completion of the task. [ABSTRACT FROM AUTHOR]

The Break4Brain project aims to elucidate the effects of both acute and chronic physical activity (PA) on educational achievement in children with and without Attention Deficit Hyperactivity Disorder (ADHD). This study will be conducted in two phases: a cross-over design followed by a hybrid type 1 implementation-effectiveness trial, which includes both a cluster randomized controlled trial (RCT) and a qualitative study. In phase I, 60 children aged 10–12, with 30 each from ADHD and non-ADHD groups, will participate in a laboratory-based study over 4 days within 1 month. They will participate in three counterbalanced experimental conditions: (i) PA with cognitive engagement, (ii) PA without cognitive engagement, and (iii) a cognitively engaging control. This phase will assess acute changes in brain function, academic performance, working memory, inhibitory control, and sustained attention. Phase II will involve 600 children aged 10–12, randomly assigned to either a video-based PA program or a control group (300 children per group) in an 8-week cluster RCT. This phase will also incorporate a qualitative approach to explore the implementation context through pre- and post-intervention semi-structured interviews with teachers and school staff, and questionnaires for students. The outcomes of interest in this phase will include working memory, cognitive flexibility, selective attention, and academic performance. For the cross-over study, we hypothesize that PA conditions will enhance the studied outcomes compared to the control condition. In the RCT, we anticipate that the 8-week active breaks program will result in significant improvements in the selected outcomes compared to the control group. This study is expected to make pioneering contributions by including novel variables and focusing on the ADHD population. Furthermore, if the cluster RCT proves effective, it could offer a practical and cost-effective resource for integrating active breaks into daily school routines. [ABSTRACT FROM AUTHOR]

In the literature, auditory attention is explored through neural speech tracking, primarily entailing modeling and analyzing electroencephalography (EEG) responses to natural speech via linear filtering. Our study takes a novel approach, introducing an enhanced coherence estimation technique to assess the strength of neural speech tracking. This enables effective discrimination between attended and ignored speech. To mitigate the impact of colored noise in EEG, we address two biases–overall coherence-level bias and spectral peak-shifting bias. In a listening study involving 32 participants with hearing impairment, tasked with attending to competing talkers in background noise, our coherence-based method effectively discerns EEG representations of attended and ignored speech. We comprehensively analyze frequency bands, individual frequencies, and EEG channels. Frequency bands of importance are shown to be delta, theta and alpha, and the important EEG channels are the central. Lastly, we showcase coherence differences across different noise reduction settings implemented in hearing aids (HAs), underscoring our method's potential to objectively assess auditory attention and enhance HA efficacy. [ABSTRACT FROM AUTHOR]

The most common manifestation of neurological disorders in children is the occurrence of epileptic seizures. In this study, we propose a multi-branch graph convolutional network (MGCNA) framework with a multi-head attention mechanism for detecting seizures in children. The MGCNA framework extracts effective and reliable features from high-dimensional data, particularly by exploring the relationships between EEG features and electrodes and considering the spatial and temporal dependencies in epileptic brains. This method incorporates three graph learning approaches to systematically assess the connectivity and synchronization of multi-channel EEG signals. The multi-branch graph convolutional network is employed to dynamically learn temporal correlations and spatial topological structures. Utilizing the multi-head attention mechanism to process multi-branch graph features further enhances the capability to handle local features. Experimental results demonstrate that the MGCNA exhibits superior performance on patient-specific and patient-independent experiments. Our end-to-end model for automatic detection of epileptic seizures could be employed to assist in clinical decision-making. [ABSTRACT FROM AUTHOR]

Introduction: According to reinforcement learning, humans adjust their behavior based on the difference between actual and anticipated outcomes (i.e., prediction error) with the main goal of maximizing rewards through their actions. Despite offering a strong theoretical framework to understand how we acquire motor skills, very few studies have investigated reinforcement learning predictions and its underlying mechanisms in motor skill acquisition. Methods: In the present study, we explored a 134-person dataset consisting of learners' feedback-evoked brain activity (reward positivity; RewP) and motor accuracy during the practice phase and delayed retention test to investigate whether these variables interacted according to reinforcement learning predictions. Results: Results showed a non-linear relationship between RewP and trial accuracy, which was moderated by the learners' performance level. Specifically, high-performing learners were more sensitive to violations in reward expectations compared to low-performing learners, likely because they developed a stronger representation of the skill and were able to rely on more stable outcome predictions. Furthermore, contrary to our prediction, the average RewP during acquisition did not predict performance on the delayed retention test. Discussion: Together, these findings support the use of reinforcement learning models to understand short-term behavior adaptation and highlight the complexity of the motor skill consolidation process, which would benefit from a multi-mechanistic approach to further our understanding of this phenomenon. [ABSTRACT FROM AUTHOR]

Background: Post-stroke depression (PSD) is a prevalent psychiatric complication among stroke survivors. The PSD researches focus on pathogenesis, new treatment methods and efficacy prediction. This study explored the electroencephalography (EEG) microstates in PSD and assessed their changes after acupuncture treatment, aiming to find the biological characteristics and the predictors of treatment efficacy of PSD. Methods: A 64-channel resting EEG data was collected from 70 PSD patients (PSD group) and 40 healthy controls (HC group) to explore the neuro-electrophysiological mechanism of PSD. The PSD patients received 6 weeks of acupuncture treatment. EEG data was collected from 60 PSD patients after acupuncture treatment (MA group) to verify whether acupuncture had a modulating effect on abnormal EEG microstates. Finally, the MA group was divided into two groups: the remission prediction group (RP group) and the non-remission prediction group (NRP group) according to the 24-Item Hamilton Depression Scale (HAMD-24) reduction rate. A prediction model for acupuncture treatment was established by baseline EEG microstates. Results: The duration of microstate D along with the occurrence and contribution of microstate C were reduced in PSD patients. Acupuncture treatment partially normalized abnormal EEG microstates in PSD patients. Baseline EEG microstates predicted the efficacy of acupuncture treatment with an area under the curve (AUC) of 0.964. Conclusion: This study provides a novel viewpoint on the neurophysiological mechanisms of PSD and emphasizes the potential of EEG microstates as a functional biomarker. Additionally, we anticipated the therapeutic outcomes of acupuncture by analyzing the baseline microstates, which holds significant practical implication for the PSD treatment. [ABSTRACT FROM AUTHOR]

Introduction: The normal transition to sleep is characterized by a reduction in higher frequency activity and an increase in lower frequency activity in frontal brain regions. In sleep onset insomnia these changes in activity are weaker and may prolong the transition to sleep. Methods: Using a wearable device, we compared 30min of short duration repetitive transcranial electric stimulation (SDR-tES) at 0.75Hz, prior to going to bed, with an active control at 25Hz in the same individuals. Results: Treatment with 0.75Hz significantly reduced sleep onset latency (SOL) by 53% when compared with pre-treatment baselines and was also significantly more effective than stimulation with 25Hz which reduced SOL by 30%. Reductions in SOL with 25Hz stimulation displayed order effects suggesting the possibility of placebo. No order effects were observed with 0.75Hz stimulation. The decrease in SOL with 0.75Hz treatment was proportional to an individual's baseline wherein those suffering from the longest pre-treated SOLs realized the greatest benefits. Changes in SOL were correlated with left/right frontal EEG signal coherence around the stimulation frequency, providing a possible mechanism and target for more focused treatment. Stimulation at both frequencies also decreased perceptions of insomnia symptoms measured with the Insomnia Severity Index, and comorbid anxiety measured with the State Trait Anxiety Index. Discussion: Our study identifies a new potential treatment for sleep onset insomnia that is comparably effective to current state-of-practice options including pharmacotherapy and cognitive behavioral therapy and is safe, effective, and can be delivered in the home. [ABSTRACT FROM AUTHOR]

Introduction: This study is the first to assess whether infants' developing social brains may be susceptible to the vicarious social experience of interparental positivity. Specifically, we explored whether infants' exposure to interparental positivity may vicariously shape their neural substrates of social development. Methods: In a sample of 45 infants (M AgeMonths = 11.01; 48.9% girls), infant left-frontal resting alpha electroencephalogram (EEG) asymmetry was derived as a reliable indicator of neural substrates linked to adaptive social development. Moreover, positive characteristics of the mother–father couple relationship were assessed both by means of observation and self-report by mother and father. Importantly, various relevant covariates were considered, including interparental negativity (observed and self-reported), as well as infants' direct caregiving experiences and duration of infant exposure to mother–father relationship-dynamics (parent-report). Results: Results indicated that higher levels of observed interparental positivity were associated with greater infant left-frontal alpha EEG asymmetry, even after accounting for covariates (β's > 0.422). Discussion: The current study's results are first to suggest that positive vicarious social experiences in infants' day-to-day lives play a significant role for early neural development. [ABSTRACT FROM AUTHOR]

Nowadays studies using Virtual Reality (VR) are gaining high popularity due to VR being a better approximation of the ecological environment for visual experiments than standard 2D display settings. VR technology has been already applied in medicine in the therapy of mental disorders, neurorehabilitation, and neurofeedback. However, its effectiveness compared to the standard 2D procedure is still not fully documented and limited information about the neurophysiological underpinnings of VR is provided. In this study, we tested participants' performance during several sessions of the computer game in two different environments, VR vs. 2D monitor display. Participants performed three 25 min gaming sessions of adapted Delay Match-To-Sample task during EEG recording. The results showed that the VR group outperformed the 2D display group in the first session and then maintained its performance level throughout the remaining two sessions while the 2D group increased performance in each session eventually leveling up in the last one. Also group differences in the EEG activity were most profound only in the first session. In this session, the VR group was characterized by stronger and more synchronized neuronal activity, especially in delta, theta, and gamma bands. The VR group was less impacted by visual arousals as indicated by the theta/beta2 ratio in parietal electrodes. [ABSTRACT FROM AUTHOR]

Introduction: Social anxiety disorder (SAD) is a prevalent psychiatric condition characterized by an intense fear of and avoidance of social situations. Traditional assessment methods for SAD primarily rely on subjective self-report questionnaires and clinical interviews, which can be prone to biases and inaccuracies. This study aims to explore the functional excitation-inhibition (fEI) ratio derived from EEG data as a potential objective biomarker for assessing SAD severity. Methods: Resting-state EEG data were collected from 20 control subjects and 60 individuals with varying degrees of SAD severity (mild, moderate, and severe). The fEI ratio was estimated across different EEG bands and analyzed, focusing on differences between control subjects and SAD groups. Results: Significantly higher fEI ratios were observed in the alpha and low beta EEG bands in individuals with SAD compared to controls, especially within the prefrontal cortex. Additionally, a positive correlation was found between the fEI ratio and the severity of social anxiety symptoms across SAD severity levels. Discussion: The findings indicate that the fEI ratio in the alpha and low beta bands may serve as a promising biomarker for assessing SAD severity. These results contribute to a deeper understanding of the neural mechanisms underlying social anxiety, offering a potentially more objective approach to SAD assessment compared to traditional methods. [ABSTRACT FROM AUTHOR]

The alpha rhythm in human electroencephalography (EEG) is known to decrease in frequency with age. Previous study has shown that elderly individuals with dementia exhibit higher S values (spatial variability) and SD values (temporal variability) in the triple correlation of the occipital region (P3, P4, Oz) compared to healthy elderly individuals. The objective of this research is to examine changes in S and SD values of the alpha band with aging in healthy individuals using triple correlation values from the frontal region. The subjects were 50 healthy elderly subjects (mean age 73.0 ± 5.1 years), 34 healthy younger subjects (mean age 28.1 ± 4.6 years), and 21 dementia patients (mean age 70.1 ± 9.1 years). The methodology involved recording EEG for 5 min during rest with closed eyes, and then calculating S and SD values of the alpha band (8-13 Hz) using three electrodes in the frontal region (F3, F4, Fpz). The findings indicated that the S values of young individuals were significantly higher than those of elderly individuals (p < 0.01), whereas the SD values of young individuals tended to be lower than those of elderly individuals. The elevated S values in young individuals imply greater spatial variability akin to individuals with dementia, whereas the reduced SD values in young individuals suggest lower temporal variability unlike individuals with dementia. The discrepancy between the S value and SD value in healthy young individuals suggests that the normal cortical dipole in the frontal regions might be more abundant in them compared to healthy elderly individuals. [ABSTRACT FROM AUTHOR]

Introduction: Human color vision exhibits significant diversity that cannot be fully explained by categorical classifications. Understanding how individuals with different color vision phenotypes perceive, recognize, and react to the same physical stimuli provides valuable insights into sensory characteristics. This study aimed to identify behavioral and neural differences between different color visions, primarily classified as typical trichromats and anomalous trichromats, in response to two chromatic stimuli, blue-green and red, during an attention demanding oddball task. Methods: We analyzed the P3 component of event-related potentials (ERPs), associated with attention, and conducted a broad spatiotemporal exploration of neural differences. Behavioral responses were also analyzed to complement neural data. Participants included typical trichromats (n = 13) and anomalous trichromats (n = 5), and the chromatic stimuli were presented in an oddball paradigm. Results: Typical trichromats exhibited faster potentiation from the occipital to parietal regions in response to the more salient red stimulus, particularly in the area overlapping with the P3 component. In contrast, anomalous trichromats revealed faster potentiation to the expected more salient blue-green stimulus in the occipital to parietal regions, with no other significant neural differences between stimuli. Comparisons between the color vision types showed no significant overall neural differences. Discussion: The large variability in red-green sensitivity among anomalous trichromats, along with neural variability not fully explained by this sensitivity, likely contributed to the absence of clear neural distinctions based on color saliency. While reaction times were influenced by red-green sensitivity, neural signals showed ambiguity regarding saliency differences. These findings suggest that factors beyond red-green sensitivity influenced neural activity related to color perception and cognition in minority color vision phenotypes. Further research with larger sample sizes is needed to more comprehensively explore these neural dynamics and their broader implications. [ABSTRACT FROM AUTHOR]

Introduction: We discuss event-related power differences (ERPDs) in low- and broadband-γ oscillations as the embedded-clause edge is processed in wh-dependencies such as Which decision regarding/about him/her did Paul say that Lydie rejected without hesitation? in first (L1) and second language (L2) French speakers. Methods: The experimental conditions manipulated whether pronouns appeared in modifiers (Mods; regarding him/her) or in noun complements (Comps; about him/ her) and whether they matched or mismatched a matrix-clause subject in gender. Results: Across L1 and L2 speakers, we found that anaphora-linked ERPDs for Mods vs. Comps in evoked power first arose in low γ and then in broadband γ. Referential elements first seem to be retrieved from working memory by narrowband processes in low γ and then referential identification seems to be computed in broadband-γ output. Interactions between discourse- and syntax-based referential processes for the Mods vs. Comps in these ERPDs furthermore suggest that multidomain γ-band processing enables a range of elementary operations for discourse and semantic interpretation. Discussion: We argue that a multidomain mechanism enabling operations conditioned by the syntactic and semantic nature of the elements processed interacts with local brain microcircuits representing features and feature sets that have been established in L1 or L2 acquisition, accounting for a single language epistemology across learning contexts. [ABSTRACT FROM AUTHOR]

Introduction: EEG-based emotion recognition has gradually become a new research direction, known as affective Brain-Computer Interface (aBCI), which has huge application potential in human-computer interaction and neuroscience. However, how to extract spatio-temporal fusion features from complex EEG signals and build learning method with high recognition accuracy and strong interpretability is still challenging. Methods: In this paper, we propose a hybrid attention spatio-temporal feature fusion network for EEG-based emotion recognition. First, we designed a spatial attention feature extractor capable of merging shallow and deep features to extract spatial information and adaptively select crucial features under different emotional states. Then, the temporal feature extractor based on the multi-head attention mechanism is integrated to perform spatio-temporal feature fusion to achieve emotion recognition. Finally, we visualize the extracted spatial attention features using feature maps, further analyzing key channels corresponding to different emotions and subjects. Results: Our method outperforms the current state-of-the-art methods on two public datasets, SEED and DEAP. The recognition accuracy are 99.12% ± 1.25% (SEED), 98.93% ± 1.45% (DEAP-arousal), and 98.57% ± 2.60% (DEAP-valence). We also conduct ablation experiments, using statistical methods to analyze the impact of each module on the final result. The spatial attention features reveal that emotion-related neural patterns indeed exist, which is consistent with conclusions in the field of neurology. Discussion: The experimental results show that our method can effectively extract and fuse spatial and temporal information. It has excellent recognition performance, and also possesses strong robustness, performing stably across different datasets and experimental environments for emotion recognition. [ABSTRACT FROM AUTHOR]

Introduction: The aim of the study was to assess the efficacy of In-Dex sedation in comparison to oral melatonin and hydroxyzine in individuals with Autism Spectrum Disorder (ASD) undergoing EEG recording and 15 determine which categories of patients exhibit the most favorable response to In-Dex sedation. Methods: This retrospective observational study involved pediatric patients with ASD who underwent sleep-EEG recording across two periods, before (biennium 2018-19) and after (biennium 2021-22) the routine implementation of In-Dex sedation. Clinical, EEG, and sedation data were stored in a database. A logistic multiple regression model was employed, with the failure of EEG serving as the dependent variable. Results: In the first period 203 EEGs were performed with a rate of failure of 10.8%, while in the second one 177 EEGs were recorded with a percentage of failure of 7.3% (8.3% with MH 23 sedation and 5.8% with In-Dex sedation). No significant adverse events were reported in either period. Multivariate logistic analysis demonstrated that In-Dex decreased the probability of failure (OR=0.25, 25 (0.61-0.88)), while the presence of behavioral disturbances (OR=3.65((1.54-8.85)) and the use of antipsychotic drugs (OR=2.76, (1.09-6.95)) increased it. Discussion: In the light of these results, we can state that In-Dex sedation is safe and reduce EEG failure rate compared to the use of melatonin and hydroxyzine alone, particularly in patients with severe behavioral issues. [ABSTRACT FROM AUTHOR]

Introduction: Event-Related Potentials (ERPs) are valuable for studying brain activity withmillisecond-level temporal resolution. While the temporal resolution of this technique is excellent, the spatial resolution is limited. Source localization aims to identify the brain regions generating the EEG data, thus increasing the spatial resolution, but its accuracy depends heavily on the headmodel used. This study compares the performance of subject-specific and template-based head models in both simulated and real-world ERP localization tasks. Methods: Simulated data mimicking realistic ERPs was created to evaluate the impact of head model choice systematically, after which subject-specific and template-based head models were used for the reconstruction of the data. The different modeling approaches were also applied to a face recognition dataset. Results: The results indicate that the template models capture the simulated activity less accurately, producing more spurious sources and identifying less true sources correctly. Furthermore, the results show that while creating more accurate and detailed head models is beneficial for the localization accuracy when using subject-specific head models, this is less the case for template head models. The main N170 source of the face recognition dataset was correctly localized to the fusiformgyrus, a known face processing area, using the subject-specificmodels. Apart fromthe fusiformgyrus, the templatemodels also reconstructed several other sources, illustrating the localization inaccuracies. Discussion: While template models allow researchers to investigate the neural generators of ERP components when no subject-specific MRIs are available, it could lead to misinterpretations. Therefore, it is important to consider a priori knowledge and hypotheses when interpreting results obtained with template head models, acknowledging potential localization errors. [ABSTRACT FROM AUTHOR]

This brief report revisits the earliest known hyperscanning study published in 1965, which examined simultaneous EEG recordings of identical twins separated by six meters. The original study's findings suggested that eye closure in one twin elicited alpha activity in the other, despite physical separation. Leveraging contemporary signal processing techniques, we reanalyzed the digitized data to validate their findings. Spectral analysis confirmed alpha activity in the twins' EEG signals, aligning with the original observations. Multitapering along with background noise subtraction also revealed the alpha activity in the unrelated subject, which could not be observed by visual inspection alone. Coherence analysis revealed significant alpha band synchrony between a twin and an unrelated subject, differing from the initial study's conclusions. Our findings indicate that even historical data can yield new insights when revisited with contemporary data analysis tools and highlight the potential for future large-scale studies using advanced techniques to explore nonlocal brain interactions. [ABSTRACT FROM AUTHOR]

Computer-based learning has gained popularity in recent years, providing learners greater flexibility and freedom. However, these learning environments do not consider the learner's mental state in real-time, resulting in less optimized learning experiences. This research aimed to explore the effect on the learning experience of a novel EEG-based Brain-Computer Interface (BCI) that adjusts the speed of information presentation in real-time during a learning task according to the learner's cognitive load. We also explored how motivation moderated these effects. In accordance with three experimental groups (non-adaptive, adaptive, and adaptive with motivation), participants performed a calibration task (n-back), followed by a memory-based learning task concerning astrological constellations. Learning gains were assessed based on performance on the learning task. Self-perceived mental workload, cognitive absorption and satisfaction were assessed using a post-test questionnaire. Between-group analyses using Mann-Whitney tests suggested that combining BCI and motivational factors led to more significant learning gains and an improved learning experience. No significant difference existed between the BCI without motivational factor and regular non-adaptive interface for overall learning gains, self-perceived mental workload, and cognitive absorption. However, participants who undertook the experiment with an imposed learning pace reported higher overall satisfaction with their learning experience and a higher level of temporal stress. Our findings suggest BCI's potential applicability and feasibility in improving memorization-based learning experiences. Further work should seek to optimize the BCI adaptive index and explore generalizability to other learning contexts. [ABSTRACT FROM AUTHOR]