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Emotion recognition based on electroencephalography (EEG) is becoming a growing concern of researchers due to its various applications and portable devices. Existing methods are mainly dedicated to EEG feature representation and have made impressive progress. However, the problem of scarce labels restricts their further promotion. In light of this, we propose a self-supervised framework with contrastive learning for robust EEG-based emotion recognition, which can effectively leverage both readily available unlabeled EEG signals and labeled ones to learn highly discriminative EEG features. Firstly, we construct a specific pretext task according to the sequential non-stationarity of emotional EEG signals for contrastive learning, which aims at extracting pseudo-label information from all EEG data. Meanwhile, we propose a novel negative segment selection algorithm to reduce the noise of unlabeled data during the contrastive learning process. Secondly, to mitigate the overfitting issue induced by a small number of labeled samples during learning, we originate a loss function with label smoothing regularization that can guide the model to learn generalizable features. Extensive experiments over three benchmark datasets demonstrate the effectiveness and superiority of our model on EEG-based emotion recognition task. Besides, the generalization and robustness of the model have also been proved through sufficient experiments. [ABSTRACT FROM AUTHOR]

The Overview Effect is a complex experience reported by astronauts after viewing Earth from space. Numerous accounts suggest that it leads to increased interconnectedness to other human beings and environmental awareness, comparable to self-transcendence. It can cause fundamental changes in mental models of the world, improved well-being, and stronger appreciation of, and responsibility for Earth. From a cognitive perspective, it is closely linked to the emotion of awe, possibly triggered by the overwhelming perceived vastness of the universe. Given that most research in the domain focuses on self-reports, little is known about potential neurophysiological markers of the Overview Effect. In the experiment reported here, participants viewed an immersive Virtual Reality simulation of a space journey while their brain activity was recorded using electroencephalography (EEG). Post-experimental self-reports confirmed they were able to experience the Overview Effect in the simulated environment. EEG recordings revealed lower spectral power in beta and gamma frequency bands during the defining moments of the Overview Effect. The decrease in spectral power can be associated with reduced mental processing, and a disruption of known mental structures in this context, thereby providing more evidence for the cognitive effects of the experience. [ABSTRACT FROM AUTHOR]

Epilepsy is a disorder that interferes with regular brain activity and can occasionally cause seizures, odd sensations, and momentary unconsciousness. Epilepsy is frequently diagnosed using electroencephalograph (EEG) records, although conventional analysis is subjective and prone to error. The dynamic and non‐stationary nature of EEG structure restricted the performance of Deep Learning (DL) approaches used in earlier work to improve EEG classification. Our multi‐channel EEG classification model, dubbed LConvNet in this paper, combines Convolutional Neural Networks (CNN) for extracting spatial features and Long Short‐Term Memory (LSTM) for identifying temporal dependencies. To discriminate between epileptic and healthy EEG signals, the model is trained using open‐source secondary EEG data from Temple University Hospital (TUH). Our model outperformed other EEG classification models employed in comparable tasks, such as EEGNet, DeepConvNet, and ShallowConvNet, which had accuracy rates of 86%, 96%, and 78%, respectively. Our model attained an amazing accuracy rate of 97%. During additional testing, our model also displayed excellent performance in trainability, scalability, and parameter efficiency. [ABSTRACT FROM AUTHOR]

Background: To manage pain and stiffness of the lumbar spine, thrust manipulation is commonly used. High-velocity, small-amplitude thrust manipulation often elicits audible sounds. What causes this audible sound remains unclear, and its clinical significance has not been shown. This study aimed to identify how audible sound affects brainwave activity following a side-lying right rotatory thrust manipulation in a group of healthy individuals. Methods: This was a quasi-experimental repeated measures study design in which 44 subjects completed the study protocol. A portable Bluetooth EEG device was used to capture brainwave activity. The environment was controlled during testing to minimize any factors influencing the acquisition of real-time EEG data. After a short accommodation period, initial brainwaves were measured. Following this, each subject underwent a lumbar 4–5 side-lying right rotatory thrust manipulation, immediately followed by a second brainwave measurement. A third measurement took place one minute later, followed by a fourth one at the three-minute mark. Results: 21 subjects did not experience audible sounds, while 23 subjects experienced audible sounds. Both groups had significant changes measured by the 14 electrodes (p < 0.05). The audible group had more significant changes, which lasted only two minutes. Conclusion: The lack of brainwave response differences between the audible and non-audible groups implies no direct, measurable placebo or beneficial effect from the audible sound. This study could not identify a benefit from the audible sound during an HVLA manipulation of the subjects. [ABSTRACT FROM AUTHOR]

In traditional cultural perceptions of gender, women are stereotyped as being more "emotional" than men. Although significant progress has been made in studying gender differences in emotional responses over the past few decades, there is still no consistent conclusion as to whether women are more emotional than men. In this study, we investigated gender differences in emotional responses between two groups of students (10 males and 10 females) in the same architectural environment, particularly in a digital cultural tourism scenario. Participants viewed the "Time Tunnel" of the ancient city of Qingzhou through VR simulation. Brainwave evoked potentials were recorded using wearable EEG devices. The results showed that females typically reported stronger emotional responses, as evidenced by higher arousal, lower potency, and stronger avoidance motivation. In contrast, males exhibited higher potency, lower arousal, and stronger comfort. The findings suggest that males have a more positive emotional response in virtual digital environments, whereas females are more sensitive and vulnerable to such environments, experiencing some discomfort. These findings can be used to guide the design and adaptation of virtual built environments. [ABSTRACT FROM AUTHOR]

Previous studies have shown that the attorney’s speeches can exert significant impacts on the cognition and judgment of the jury in court arguments. However, the psychological effects induced by these speeches are intricately tied to subconscious brain states, making them challenging to accurately and comprehensively describe through subjective self-reports. This study aims to explore a neural reaction observation method for psychological effect analysis of the attorney’s speeches in courtroom scenarios. We utilized a corpus of courtroom arguments from legal movies and television series as source material. Participants’ psychological responses to these speeches were monitored using wearable electroencephalography (EEG) devices. Building upon this data, we employed a deep learning model based on Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to compute attention intensity, cognitive load, and emotional changes. Our test results demonstrate that this approach enables continuous and dynamic computation within courtroom argument contexts, providing a more accurate assessment of attorneys’ language skills [ABSTRACT FROM AUTHOR]

This study describes electroencephalography (EEG) measurements during a simple finger movement in people with stroke to understand how temporal patterns of cortical activation and network connectivity align with prolonged muscle contraction at the end of a task. We investigated changes in the EEG temporal patterns in the beta band (13–26 Hz) of people with chronic stroke (N = 10, 7 F/3 M) and controls (N = 10, 7 F/3 M), during and after a cued movement of the index finger. We quantified the change in beta band EEG power relative to baseline as activation at each electrode and the change in task-based phase-locking value (tbPLV) and beta band task-based coherence (tbCoh) relative to baseline coherence as connectivity between EEG electrodes. Finger movements were associated with a decrease in beta power (event related desynchronization (ERD)) followed by an increase in beta power (event related resynchronization (ERS)). The ERS in the post task period was lower in the stroke group (7%), compared to controls (44%) (p < 0.001) and the transition from ERD to ERS was delayed in the stroke group (1.43 s) compared to controls (0.90 s) in the C3 electrode (p = 0.007). In the same post movement period, the stroke group maintained a heightened tbPLV (p = 0.030 for time to baseline of the C3:Fz electrode pair) and did not show the decrease in connectivity in electrode pair C3:Fz that was observed in controls (tbPLV: p = 0.006; tbCoh: p = 0.023). Our results suggest that delays in cortical deactivation patterns following movement coupled with changes in the time course of connectivity between the sensorimotor and frontal cortices in the stroke group might explain clinical observations of prolonged muscle activation in people with stroke. This prolonged activation might be attributed to the combination of cortical reorganization and changes to sensory feedback post-stroke. [ABSTRACT FROM AUTHOR]

Mindfulness meditation is a contemplative practice that is informed by Buddhism. It has been proven effective for improving mental and physical health in clinical and non-clinical contexts. To date, mainstream dialogue and scientific research on mindfulness has focused primarily on short-term mindfulness training and applications of mindfulness for reducing stress. Understanding advanced mindfulness practice has important implications for mental health and general wellbeing. According to Theravada Buddhist meditation, a "cessation" event is a dramatic experience of profound clarity and equanimity that involves a complete discontinuation in experience, and is evidence of mastery of mindfulness meditation. Thirty-seven cessation events were captured in a single intensively sampled advanced meditator (over 6,000 h of retreat mindfulness meditation training) while recording electroencephalography (EEG) in 29 sessions between November 12, 2019 and March 11, 2020. Functional connectivity and network integration were assessed from 40 s prior to cessations to 40 s after cessations. From 21 s prior to cessations there was a linear decrease in large-scale functional interactions at the whole-brain level in the alpha band. In the 40 s following cessations these interactions linearly returned to prior levels. No modulation of network integration was observed. The decrease in whole-brain functional connectivity was underlain by frontal to left temporal and to more posterior decreases in connectivity, while the increase was underlain by wide-spread increases in connectivity. These results provide neuroscientific evidence of large-scale modulation of brain activity related to cessation events that provides a foundation for future studies of advanced meditation. [ABSTRACT FROM AUTHOR]

Nostalgia, a self-related emotion characterized by its bittersweet yet predominantly positive nature, plays a vital role in shaping individual psychology and behavior. This includes impacts on mental and physical health, behavioral patterns, and cognitive functions. However, higher levels of trait nostalgia may be linked to potential adverse outcomes, such as increased loneliness, heightened neuroticism, and more intense experiences of grief. The specific electroencephalography (EEG) feature associated with individuals exhibiting trait nostalgia, and how it differs from others, remains an area of uncertainty. To address this, our study employs microstate analysis to investigate the differences in resting-state EEG between individuals with varying levels of trait nostalgia. We assessed trait nostalgia in 63 participants using the Personal Inventory of Nostalgia and collected their resting-state EEG signals with eyes closed. The results of the regression analysis indicate a significant correlation between trait nostalgia and the temporal characteristics of microstates A, B, and C. Further, the occurrence of microstate B was significantly more frequent in the high trait nostalgia group than in the low trait nostalgia group. Independent samples t-test results showed that the transition probability between microstates A and B was significantly higher in the high trait nostalgia group. These results support the hypothesis that trait nostalgia is reflected in the resting state brain activity. Furthermore, they reveal a deeper sensory immersion in nostalgia experiences among individuals with high levels of trait nostalgia, and highlight the critical role of self-referential and autobiographical memory processes in nostalgia. [ABSTRACT FROM AUTHOR]

The resting-state paradigm is frequently applied to study spontaneous activity of the brain in normal and clinical conditions. To assess the relationship between brain activity and subjective experiences, various questionnaires are used. Previous studies using Amsterdam Resting State Questionnaire were focusing on fMRI functional connectivity or EEG microstates and spectral aspect. Here, we utilized Global Field Synchronization as the parameter to estimate global functional connectivity. By re-analyzing the resting-state data from 226 young healthy participants we showed a strong evidence of relationship between ARSQ domain of Comfort and GFS values in the alpha range (r = 0.210, BF10 = 12.338) and substantial evidence for positive relationship between ARSQ domain of Comfort and GFS in the beta frequency range (r = 196, BF10 = 6.307). Our study indicates the relevance of assessments of spontaneous thought occurring during the resting-state for the understanding of the individual intrinsic electrical brain activity. [ABSTRACT FROM AUTHOR]

Social norms and altruistic punitive behaviours are both based on the integration of information from multiple contexts. Individual behavioural performance can be altered by loss and gain contexts, which produce different mental states and subjective perceptions. In this study, we used event-related potential and time-frequency techniques to examine performance on a third-party punishment task and to explore the neural mechanisms underlying context-dependent differences in punishment decisions. The results indicated that individuals were more likely to reject unfairness in the context of loss (vs. gain) and to increase punishment as unfairness increased. In contrast, fairness appeared to cause an early increase in cognitive control signal enhancement, as indicated by the P2 amplitude and theta oscillations, and a later increase in emotional and motivational salience during decision-making in gain vs. loss contexts, as indicated by the medial frontal negativity and beta oscillations. In summary, individuals were more willing to sanction violations of social norms in the loss context than in the gain context and rejecting unfair losses induced more equity-related cognitive conflict than accepting unfair gains, highlighting the importance of context (i.e., gain vs. loss) in equity-related social decision-making processes. [ABSTRACT FROM AUTHOR]

Purpose: Identify the presence of a dysfunctional electroencephalographic (EEG) pattern in individuals with sickle cell disease (SCD) and hip osteonecrosis, and assess its potential associations with depression, anxiety, pain severity, and serum levels of brain-derived neurotrophic factor (BDNF). Methods: In this cross-sectional investigation, 24 SCD patients with hip osteonecrosis and chronic pain were matched by age and sex with 19 healthy controls. Resting-state EEG data were recorded using 32 electrodes for both groups. Power spectral density (PSD) and peak alpha frequency (PAF) were computed for each electrode across Delta, Theta, Alpha, and Beta frequency bands. Current Source Density (CSD) measures were performed utilizing the built-in Statistical nonparametric Mapping Method of the LORETA-KEY software. Results: Our findings demonstrated that SCD individuals exhibited higher PSD in delta and theta frequency bands when compared to healthy controls. Moreover, SCD individuals displayed increased CSD in delta and theta frequencies, coupled with decreased CSD in the alpha frequency within brain regions linked to pain processing, motor function, emotion, and attention. In comparison to the control group, depression symptoms, and pain intensity during hip abduction were positively correlated with PSD and CSD in the delta frequency within the parietal region. Depression symptoms also exhibited a positive association with PSD and CSD in the theta frequency within the same region, while serum BDNF levels showed a negative correlation with CSD in the alpha frequency within the left insula. Conclusion: This study indicates that individuals with SCD experiencing hip osteonecrosis and chronic pain manifest a dysfunctional EEG pattern characterized by the persistence of low-frequency PSD during a resting state. This dysfunctional EEG pattern may be linked to clinical and biochemical outcomes, including depression symptoms, pain severity during movement, and serum BDNF levels. [ABSTRACT FROM AUTHOR]

Electroencephalography (EEG) is a medical engineering technique aimed at recording the electric activity of the human brain. Brain signals derived from an EEG device can be processed and analyzed through computers by using digital signal processing, computational statistics, and machine learning techniques, that can lead to scientifically-relevant results and outcomes about how the brain works. In the last decades, the spread of EEG devices and the higher availability of EEG data, of computational resources, and of software packages for electroencephalography analysis has made EEG signal processing easier and faster to perform for any researcher worldwide. This increased ease to carry out computational analyses of EEG data, however, has made it easier to make mistakes, as well. And these mistakes, if unnoticed or treated wrongly, can in turn lead to wrong results or misleading outcomes, with worrisome consequences for patients and for the advancements of the knowledge about human brain. To tackle this problem, we present here our ten quick tips to perform electroencephalography signal processing analyses avoiding common mistakes: a short list of guidelines designed for beginners on what to do, how to do it, and what not to do when analyzing EEG data with a computer. We believe that following our quick recommendations can lead to better, more reliable and more robust results and outcome in clinical neuroscientific research. [ABSTRACT FROM AUTHOR]

Electroencephalographic (EEG) microstates can provide a unique window into the temporal dynamics of large-scale brain networks across brief (millisecond) timescales. Here, we analysed fundamental temporal features of microstates extracted from the broadband EEG signal in a large (N = 139) cohort of children spanning early-to-middle childhood (4-12 years of age). Linear regression models were used to examine if participants age and biological sex could predict the temporal parameters GEV, duration, coverage, and occurrence, for five microstate classes (A-E) across both eyes-closed and eyes-open resting-state recordings. We further explored associations between these microstate parameters and posterior alpha power after removal of the 1/f-like aperiodic signal. The microstates obtained from our neurodevelopmental EEG recordings broadly replicated the four canonical microstate classes (A to D) frequently reported in adults, with the addition of the more recently established microstate class E. Biological sex served as a significant predictor in the regression models for four of the five microstate classes (A, C, D, and E). In addition, duration and occurrence for microstate E were both found to be positively associated with age for the eyes-open recordings, while the temporal parameters of microstates C and E both exhibited associations with alpha band spectral power. Together, these findings highlight the influence of age and sex on large-scale functional brain networks during early-to-middle childhood, extending understanding of neural dynamics across this important period for brain development.

Abstract Cochlear implants (CI) are one of the most successful treatments available to enable individuals with severe to profound hearing loss to regain access to the world of sound. This is accomplished through the electrical stimulation of the auditory nerve using electrodes implanted inside the cochlea. The use of subjective user feedback makes the process of fitting these devices much more challenging in cases where users are not able to actively or accurately report their experience (e.g. pediatrics), making an objective measurement that reflects the accuracy or effectiveness of a program quite attractive. We recorded one objective measure, the electrically-stimulated cortical auditory evoked potential (eCAEP), non-invasively using the CI in response to a simulated speech sound in seven adult participants and compared it to their eCAEP recorded using a scalp EEG set-up. The eCAEPs recorded with CI electrodes were comparable to scalp recorded eCAEPs (grand mean cross-correlation of r = 0.83, individual mean cross-correlations ranged from 0.13 to 0.70). Evoked potential peaks P1, N1 and P2 showed no significant latency difference based on if the eCAEP was recorded on the scalp or using the CI. The eCAEP waveforms recorded via the CI appear to converge in a distinct P1-N1-P2 waveform by as early as 130 sweeps. In conclusion, in this study we show the feasibility of recording the eCAEP directly through the CI system, which could potentially be used to guide CI fitting and track auditory cortex development in response to CI use.

Abstract People are routinely forced to undertake cognitive challenges under the effect of sleep deprivation, due to professional and social obligations forcing them to ignore their circadian clock. However, low intra-individual and high inter-individual differences in behavioural outcomes are known to occur when people are sleep deprived, leading to the conclusion that trait-like differences to sleep deprivation could explain the differing levels of resilience. Within this study we consider if trait-like resilience to sleep deprivation, measured using psychomotor vigilance tests over a 40 h protocol, could be associated with graph metrics (mean node strength, clustering coefficient, characteristic path length and stability) calculated from EEG functional networks acquired when participants ( $$n=13$$ ) are well rested (baseline). Furthermore, we investigated how stability (the consistency of a participant’s functional network over time measured using 2-D correlation) changed over the constant routine. We showed evidence of strong significant correlations between high mean node strength, low characteristic path length and high stability at baseline with a general resilience to extended sleep deprivation, although the same features lead to vulnerability during the period of natural sleep onset, highlighting non-uniform correlations over time. We also show significant differences in the levels of stability between resilient and vulnerable groups.

This study investigates the classification of eye movements using EEG data recorded from a wearable device, with eye tracking data employed as ground truth for model training. We aim to classify various eye movements, including fixations, saccades, and directional movements, utilizing long short-term memory (LSTM) neural networks. Data were collected from 22 participants using the BrainBit headband, which recorded EEG signals at 250 Hz with four dry electrodes, and the Pupil Labs Invisible eye tracker, which recorded 2D gaze coordinates at 100 Hz during computer-based tasks. The EEG data underwent preprocessing and feature extraction to capture essential characteristics relevant to eye movement classification. Our LSTM model, trained and validated on this dataset, achieved a classification accuracy of 90% for the saccade detection task and 68% and 62% for up versus down and left versus right movement classification accordingly. These results demonstrate the potential of using EEG data alone for reliable eye movement classification, laying the groundwork for future research in neural signal processing and its applications in human-computer interaction and neurotechnological systems.

Abstract Alpha oscillations are associated with various psychiatric disorders, with many studies focusing on the prefrontal cortex, where transcranial alternating current stimulation (TACS) is applied in the alpha frequency band. This approach often involves selecting individualized alpha frequencies to resonate with their endogenous alpha oscillations. While strong alpha oscillations (8–13 Hz) are typically induced when the eyes are closed, they can also occur during the resting state with eyes open. However, it remains unclear whether these alpha oscillations share a common neural generation mechanism. Exploring which of these alpha oscillations is more suitable as a stable alpha peak frequency is a question of significant interest. Therefore, to systematically study this issue, we specifically collected resting-state electroencephalographic (EEG) data from the prefrontal region of 40 individuals, under both eyes-open and closed- eye conditions, with multiple follow-ups extending up to nine months. Through spectral analysis on each person’s entire dataset and averaging the results, we observed a significant positive correlation between the alpha-band power in the eyes-open and the eyes-closed states, in terms of both absolute power and relative power. Further analysis revealed that this correlation was primarily contributed by the periodic activity within the alpha band. Upon modelling the oscillatory components, we discovered distinct differences in the oscillatory characteristics-such as number of the alpha sub-oscillations between the eyes-open state and the eyes-closed state. Our study is the first to systematically explored the relationship between alpha oscillations in the prefrontal cortex in the eyes-open and eyes-closed states, identifying both shared part of the neural generation mechanism and some distinct neural mechanisms that are unique to each state.

Abstract Fragile X syndrome (FXS) is a rare neurodevelopmental disorder caused by a CGG repeat expansion ≥ 200 repeats in 5’ untranslated region of the FMR1 gene, leading to intellectual disability and cognitive difficulties, including in the domain of communication. A recent phase 2a clinical trial testing BPN14770, a phosphodiesterase 4D inhibitor, showed improved cognition in 30 adult males with FXS on drug relative to placebo. The initial study found significant improvements in clinical measures assessing cognition, language, and daily functioning in addition to marginal improvements in electroencephalography (EEG) results for the amplitude of the N1 event-related potential (ERP) component. These EEG results suggest BPN14770 improved neural hyperexcitability in FXS. The current study investigated the relationship between BPN14770 pharmacokinetics and the amplitude of the N1 ERP component from the initial data. Consistent with the original group-level finding post-period 1 of the study, participants who received BPN14770 in period 1 showed a significant correlation between N1 amplitude and serum concentration of BPN14770 measured at the end of period 1. These findings strengthen the validity of the original result, indicating that BPN14770 improves cognitive performance by modulating neural hyperexcitability. This study represents the first report of a significant correlation between a reliably abnormal EEG marker and serum concentration of a novel pharmaceutical in FXS.

Background: Electromagnetic forces in transcranial magnetic stimulation (TMS) coils generate a loud clicking sound that produces confounding auditory activation and is potentially hazardous to hearing. To reduce this noise while maintaining stimulation efficiency similar to conventional TMS coils, we previously developed a quiet TMS double containment coil (qTMS-DCC). Objective: To compare the stimulation strength, perceived loudness, and EEG response between qTMS-DCC and a commercial TMS coil. Methods: Nine healthy volunteers participated in a within-subject study design. The resting motor thresholds (RMTs) for qTMS-DCC and MagVenture Cool-B65 were measured. Psychoacoustic titration matched the Cool-B65 loudness to qTMS-DCC pulsed at 80, 100, and 120 % RMT. Event-related potentials (ERPs) were recorded for both coils. The psychoacoustic titration and ERPs were acquired with the coils both on and 6 cm off the scalp, the latter isolating the effects of airborne auditory stimulation from body sound and electromagnetic stimulation. The ERP comparisons focused on a centro-frontal region that encompassed peak responses in the global signal while stimulating the primary motor cortex. Results: RMT did not differ significantly between the coils, with or without the EEG cap on the head. qTMS-DCC was perceived to be substantially quieter than Cool-B65. For example, qTMS-DCC at 100 % coil-specific RMT sounded like Cool-B65 at 34 % RMT. The general ERP waveform and topography were similar between the two coils, as were early-latency components, indicating comparable electromagnetic brain stimulation in the on-scalp condition. qTMS- DCC had a significantly smaller P180 component in both on-scalp and off-scalp conditions, supporting reduced auditory activation. Conclusions: The stimulation efficiency of qTMS-DCC matched Cool-B65 while having substantially lower perceived loudness and auditory-evoked potentials.

Abstract Background Post-stroke depression (PSD) is a prevalent psychiatric complication in stroke patients, severely reducing quality of life and delaying social recovery in stroke survivors. Clinical studies have shown that acupuncture can be used as an alternative approach for PSD. The aim of this study was to examine the safety, efficacy, and electroencephalogram (EEG) mechanism of acupuncture in treating PSD patients. Methods From October 28, 2022 to May 16, 2023, this single-center, single-blind, randomized clinical trial was conducted at the Second Affiliated Hospital of Nanchang University. A total of 56 eligible subjects were assigned in a random manner, with an equal distribution between two groups: the manual acupuncture (MA) group and the sham acupuncture (SA) group. The primary outcome was the Hamilton Depression Scale-24 (HAMD-24); the secondary outcomes included the Pittsburgh Sleep Quality Index (PSQI), the National Institutes of Health Stroke Scale (NIHSS), the Barthel index, EEG power spectrum, and EEG imaginary coherent (iCOH). Results Compared to the SA group, the MA group exhibited significant improvements in HAMD-24, NIHSS, PSIQ, and Barthel index at week 6. The total improvement rate was 85.71% in the MA group and 28.57% in the SA group. After 6 weeks of treatment, the alpha and beta bands power spectrum increased significantly, while the delta and theta bands power spectrum decreased significantly in the MA group compared to the SA group. The iCOH analysis showed that the MA group had significantly higher functional connectivity in the four bands than the SA group. Conclusions Acupuncture might be regarded as an adjunctive treatment for PSD patients with improvements in their neurological deficits, sleep quality, and depression. Meanwhile, the mechanism of acupuncture in treating PSD patients may be through decreasing the slow wave power spectrum and increasing the fast wave power spectrum, and enhancing brain functional connectivity. Trial registration Chinese Clinical Trial Registry (ChiCTR2200065112/2022-10-28).

Abstract Motor imagery electroencephalography (EEG) analysis is crucial for the development of effective brain-computer interfaces (BCIs), yet it presents considerable challenges due to the complexity of the data and inter-subject variability. This paper introduces EEGCCT, an application of compact convolutional transformers designed specifically to improve the analysis of motor imagery tasks in EEG. Unlike traditional approaches, EEGCCT model significantly enhances generalization from limited data, effectively addressing a common limitation in EEG datasets. We validate and test our models using the open-source BCI Competition IV datasets 2a and 2b, employing a Leave-One-Subject-Out (LOSO) strategy to ensure subject-independent performance. Our findings demonstrate that EEGCCT not only outperforms conventional models like EEGNet in standard evaluations but also achieves better performance compared to other advanced models such as Conformer, Hybrid s-CViT, and Hybrid t-CViT, while utilizing fewer parameters and achieving an accuracy of 70.12%. Additionally, the paper presents a comprehensive ablation study that includes targeted data augmentation, hyperparameter optimization, and architectural improvements.

Abstract Background To gain a comprehensive understanding of the altered sensory processing in patients with migraine, in this study, we developed an electroencephalography (EEG) protocol for examining brainstem and cortical responses to sensory stimulation. Furthermore, machine learning techniques were employed to identify neural signatures from evoked brainstem–cortex activation and their interactions, facilitating the identification of the presence and subtype of migraine. Methods This study analysed 1,000-epoch-averaged somatosensory evoked responses from 342 participants, comprising 113 healthy controls (HCs), 106 patients with chronic migraine (CM), and 123 patients with episodic migraine (EM). Activation amplitude and effective connectivity were obtained using weighted minimum norm estimates with spectral Granger causality analysis. This study used support vector machine algorithms to develop classification models; multimodal data (amplitude, connectivity, and scores of psychometric assessments) were applied to assess the reliability and generalisability of the identification results from the classification models. Results The findings revealed that patients with migraine exhibited reduced amplitudes for responses in both the brainstem and cortical regions and increased effective connectivity between these regions in the gamma and high-gamma frequency bands. The classification model with characteristic features performed well in distinguishing patients with CM from HCs, achieving an accuracy of 81.8% and an area under the curve (AUC) of 0.86 during training and an accuracy of 76.2% and an AUC of 0.89 during independent testing. Similarly, the model effectively identified patients with EM, with an accuracy of 77.5% and an AUC of 0.84 during training and an accuracy of 87% and an AUC of 0.88 during independent testing. Additionally, the model successfully differentiated patients with CM from patients with EM, with an accuracy of 70.5% and an AUC of 0.73 during training and an accuracy of 72.7% and an AUC of 0.74 during independent testing. Conclusion Altered brainstem-cortex activation and interaction are characteristic of the abnormal sensory processing in migraine. Combining evoked activity analysis with machine learning offers a reliable and generalisable tool for identifying patients with migraine and for assessing the severity of their condition. Thus, this approach is an effective and rapid diagnostic tool for clinicians.

Abstract Background Preterm birth is a principal reason for perinatal morbidity and mortality increasing the incidence of severe neurodevelopmental deficits. There is growing proof that early postnatal amplitude-integrated electroencephalography (aEEG) has a prognostic value for neurodevelopmental consequence in preterm born neonates. Furthermore, MRI has been widely utilized to enhance comprehension of the brain substrate responsible for neurodevelopmental abnormalities. Thus, this study aims at evaluating the role of early postnatal aEEG and brain MRI in forecasting neurodevelopmental consequence in preterm infants at the age of 1 year. Methods A cohort study performed in the neonatal intensive care unit of a tertiary hospital during the duration from October 2021 to June 2023 including 60 preterm neonates

Abstract A concern in the field of autism electroencephalography (EEG) biomarker discovery is their lack of reproducibility. In the present study, we considered the problem of learning reproducible associations between multiple features of resting state (RS) neural activity and autism, using EEG data collected during a RS paradigm from 36 to 96 month-old children diagnosed with autism (N = 224) and neurotypical children (N = 69). Specifically, EEG spectral power and functional connectivity features were used as inputs to a regularized generalized linear model trained to predict diagnostic group (autism versus neurotypical). To evaluate our model, we proposed a procedure that quantified both the predictive generalization and reproducibility of learned associations produced by the model. When prioritizing both model predictive performance and reproducibility of associations, a highly reproducible profile of associations emerged. This profile revealed a distinct pattern of increased gamma power and connectivity in occipital and posterior midline regions associated with an autism diagnosis. Conversely, model selection based on predictive performance alone resulted in non-robust associations. Finally, we built a custom machine learning model that further empirically improved robustness of learned associations. Our results highlight the need for model selection criteria that maximize the scientific utility provided by reproducibility instead of predictive performance.

Abstract During the last four decades there has been a significant growth of interest in mindfulness-based practices and their potential to foster improvements in health, wellbeing and human functioning in a variety of clinical and nonclinical populations. With this growth has come a renewed interest in understanding the psychological processes involved as well as the neuropsychological mechanisms by which such practices operate and effect transformative personal experiences and positive change. The current perspective paper (i) presents a basic taxonomy of meditation types and the structure and function of the processes believed to be involved, (ii) describes these components in terms of key neuroanatomical regions of interest, and (iii) critically appraises current findings regarding EEG measures as they relate to different aspects of meditation, functional activity and connectivity across regions of interest. The correlates between mindfulness and EEG are well described in terms of attentional and interoceptive processes and neuroanatomical regions of interest. To a lesser extent, there is also a growing understanding of such correlates for meditation techniques centred on compassion and loving-kindness meditation. However, the same does not apply to wisdom-based and null-state meditation practices where consistent associations between neuropsychological processes and EEG characteristics have proven elusive. These latter practices are viewed by many as key to fostering the deeper transformative experiences underlying psychological and spiritual development, and although studies of null-state meditation have yielded promising theoretical developments, more research is required. Future research could also benefit from better standardisation of EEG measures and analytic techniques to allow more robust metanalyses, and greater consistency of terminology regarding the fundamental components of meditation practice.

Abstract Deep Learning (DL) has the potential to enhance patient outcomes in healthcare by implementing proficient systems for disease detection and diagnosis. However, the complexity and lack of interpretability impede their widespread adoption in critical high-stakes predictions in healthcare. Incorporating uncertainty estimations in DL systems can increase trustworthiness, providing valuable insights into the model’s confidence and improving the explanation of predictions. Additionally, introducing explainability measures, recognized and embraced by healthcare experts, can help address this challenge. In this study, we investigate DL models’ ability to predict sex directly from electroencephalography (EEG) data. While sex prediction have limited direct clinical application, its binary nature makes it a valuable benchmark for optimizing deep learning techniques in EEG data analysis. Furthermore, we explore the use of DL ensembles to improve performance over single models and as an approach to increase interpretability and performance through uncertainty estimation. Lastly, we use a data-driven approach to evaluate the relationship between frequency bands and sex prediction, offering insights into their relative importance. InceptionNetwork, a single DL model, achieved 90.7% accuracy and an AUC of 0.947, and the best-performing ensemble, combining variations of InceptionNetwork and EEGNet, achieved 91.1% accuracy in predicting sex from EEG data using five-fold cross-validation. Uncertainty estimation through deep ensembles led to increased prediction performance, and the models were able to classify sex in all frequency bands, indicating sex-specific features across all bands.

Abstract In neuroscience and clinical diagnostics, electroencephalography (EEG) is a crucial instrument for capturing neural activity. However, this signal is polluted by different artifacts like muscle activity, eye blinks, environmental interference, etc., which makes it more difficult to retrieve important information from the signal. Deep learning methods have demonstrated the potential to lower these artifacts and enhance the EEG’s quality in recent years. In this work, a novel deep learning method,“AnEEG” is presented for eliminating artifacts from EEG signal. The quantitative matrices NMSE, RMSE, CC, SNR and SAR are calculated to confirm the effectiveness of the proposed model. Through this process, it was found that the suggested model outperformed wavelet decomposition techniques. The model achieves lower NMSE and RMSE values, which indicates better agreement with the original signal. Achieving higher CC values means stronger linear agreement with the ground truth signals. Additionally, the model shows improvements in both SNR and SAR values. Overall, this suggested approach showcases promising results in improving the quality of EEG data by utilizing deep learning.

Abstract “Metacontrol” refers to the ability to find the right balance between more persistent and more flexible cognitive control styles, depending on task demands. Recent research on tasks involving response conflict regulation indicates a consistent link between aperiodic EEG activity and task conditions that demand a more or less persistent control style. In this study, we explored whether this connection between metacontrol and aperiodic activity also applies to cognitive flexibility. We examined EEG and behavioral data from two separate samples engaged in a task-switching paradigm, allowing for an internal replication of our findings. Both studies revealed that aperiodic activity significantly decreased during task switching compared to task repetition. Our results support the predictions of metacontrol theory but contradict those of traditional control theories which would have predicted the opposite pattern of results. We propose that aperiodic activity observed in EEG signals serves as a valid indicator of dynamic neuroplasticity in metacontrol, suggesting that truly adaptive metacontrol does not necessarily bias processing towards persistence in response to every control challenge, but chooses between persistence and flexibility biases depending on the nature of the challenge.

Abstract With mounting evidence for the benefits of meditation, there has been a growing interest in measuring and quantifying meditative states. This study introduces the Inner Dereification Index (IDI), a class of personalized models designed to quantify the distance from non-meditative states such as mind wandering based on a single individual’s neural activity. In addition to demonstrating high classification accuracy (median AUC: 0.996) at distinguishing meditation from thinking states moment by moment, IDI can accurately stratify meditator cohorts by experience, and correctly identify the practices most effective at training the dereification aspect of meditation (decentering from immersion with thoughts and perceptions and recognizing them as mental constructs). These results suggest that IDI models may be a useful real-time proxy for dereification and meditation progress, requiring only 1 min of mind wandering data (and no meditation data) during model training. Thus, they show promise for applications such as real-time meditation feedback, progress tracking, personalization of practices, and potential therapeutic applications of neurofeedback-assisted generation of positive states of consciousness.

Abstract Background Prolonged electroencephalographic depression during surgery is associated with poor outcomes for patients. However, the published literature on electroencephalographic depression caused by a sudden increase in the partial pressure of end-tidal carbon dioxide (PETCO2) is lacking. Case presentation We report four patients who were scheduled for laparoscopic liver surgery under general anesthesia. During the process of EEG monitoring with Sedline, four patients experienced electroencephalographic depression closely after a sudden increase in PETCO2. The four patients showed that electroencephalographic depression mainly manifested as a slow in EEG frequency, a reduction in the amplitude and power of EEG, and a decrease in spectral edge frequency. Patient state index was elevated in three cases. Conclusions To summarize, our patients showed EEG depression when PETCO2 suddenly increased, which suggests that clinical doctors should be alert to electroencephalographic depression when the PETCO2 abruptly increases. EEG monitoring devices should be applied in patients with possible hypercapnia. Anesthesiologists must comprehensively interpret the raw EEG, spectral edge frequency, and density spectral array data, in addition to patient sedation index values.

Abstract Alzheimer’s disease (AD) is a progressive neurodegenerative condition marked by memory impairments and distinct histopathological features such as amyloid-beta (Aβ) accumulations. Alzheimer’s patients experience sleep disturbances at early stages of the disease. APPswe/PS1dE9 (APP) mice exhibit sleep disruptions, including reductions in non-rapid eye movement (NREM) sleep, that contribute to their disease progression. In addition, astrocytic calcium transients associated with a sleep-dependent brain rhythm, slow oscillations prevalent during NREM sleep, are disrupted in APP mice. However, at present it is unclear whether restoration of circuit function by targeting astrocytic activity could improve sleep in APP mice. To that end, APP mice expressing channelrhodopsin-2 (ChR2) targeted to astrocytes underwent optogenetic stimulation at the slow oscillation frequency. Optogenetic stimulation of astrocytes significantly increased NREM sleep duration but not duration of rapid eye movement (REM) sleep. Optogenetic treatment increased delta power and reduced sleep fragmentation in APP mice. Thus, optogenetic activation of astrocytes increased sleep quantity and improved sleep quality in an AD mouse model. Astrocytic activity provides a novel therapeutic avenue to pursue for enhancing sleep and slowing AD progression.

Abstract Background Disorders of Consciousness (DoC) caused by severe brain injuries represent a challenging clinical entity, which is easy to misdiagnosis and lacks effective treatment options. Repetitive Transcranial Magnetic Stimulation (rTMS) is a non-invasive neuroelectric stimulation method that shows promise in improving consciousness for DoC, especially in minimally conscious state (MCS). However, there is little evidence of its effectiveness, especially in RCT studies. Methods Twenty MCS patients participated in a double-blind, randomized, crossover, sham-controlled clinical study to evaluate the safety and efficacy of rTMS for MCS. Subjects were randomized into two groups: one group received rTMS-active for 10 consecutive days (n = 10), and the other group received rTMS-sham for 10 consecutive days (n = 10). After a 10-day washout period, the two groups were crossed over and received the opposite treatment. the rTMS protocol consisted of 2,000 pulses per day in the left dorsolateral prefrontal cortex (L-DLPFC), sent at 10 Hz. The stimulation intensity was 90% of the resting motor threshold. Coma Recovery Scale Revised (CRS-R), the main evaluation index, was evaluated before and after each phase in a double-blind manner. Meanwhile RS-EEG and TMS-EEG data were acquired and relative alpha power (RAP), and perturbational complexity index based on state transitions (PCIst) were caculated. Results One-way ANOVA revealed significantly higher scores in rTMS-active treatment compared to rTMS-sham across various measures, including CRS-R total score, RAP, PCIst (all P

Abstract In Virtual Reality (VR), a higher level of presence positively influences the experience and engagement of a user. There are several parameters that are responsible for generating different levels of presence in VR, including but not limited to, graphical fidelity, multi-sensory stimuli, and embodiment. However, standard methods of measuring presence, including self-reported questionnaires, are biased. This research focuses on developing a robust model, via machine learning, to detect different levels of presence in VR using multimodal neurological and physiological signals, including electroencephalography and electrodermal activity. An experiment has been undertaken whereby participants (N = 22) were each exposed to three different levels of presence (high, medium, and low) in a random order in VR. Four parameters within each level, including graphics fidelity, audio cues, latency, and embodiment with haptic feedback, were systematically manipulated to differentiate the levels. A number of multi-class classifiers were evaluated within a three-class classification problem, using a One-vs-Rest approach, including Support Vector Machine, k-Nearest Neighbour, Extra Gradient Boosting, Random Forest, Logistic Regression, and Multiple Layer Perceptron. Results demonstrated that the Multiple Layer Perceptron model obtained the highest macro average accuracy of $$93\pm 0.03\%$$ 93 ± 0.03 % . Posthoc analysis revealed that relative band power, which is expressed as the ratio of power in a specific frequency band to the total baseline power, in both the frontal and parietal regions, including beta over theta and alpha ratio, and differential entropy were most significant in detecting different levels of presence.

Abstract Caregiving plays a critical role in children’s cognitive, emotional, and psychological well-being. In the current longitudinal study, we investigated the enduring effects of early maternal behavior on processes of interbrain synchrony in adolescence. Mother-infant naturalistic interactions were filmed when infants were 3–4 months old and interactions were coded for maternal sensitivity and intrusiveness with the Coding Interactive Behavior Manual. In early adolescence (Mean = 12.30, SD = 1.25), mother-adolescent interbrain synchrony was measured using hyperscanning EEG during a naturalistic interaction of positive valence. Guided by previous hyperscanning studies, we focused on interbrain connections within the right frontotemporal interbrain network. Results indicate that maternal sensitivity in early infancy was longitudinally associated with neural synchrony in the right interbrain frontotemporal network. Post-hoc comparisons highlighted enhancement of mother-adolescent frontal-frontal connectivity, a connection that has been implicated in parent-child social communication. In contrast, maternal intrusiveness in infancy was linked with attenuation of interbrain synchrony in the right interbrain frontotemporal network. Sensitivity and intrusiveness are key maternal social orientations that have shown to be individually stable in the mother-child relationship from infancy to adulthood and foreshadow children’s positive and negative social-emotional outcomes, respectively. Our findings are the first to demonstrate that these two maternal orientations play a role in enhancing or attenuating the child’s interbrain frontotemporal network, which sustains social communication and affiliation. Results suggest that the reported long-term impact of maternal sensitivity and intrusiveness may relate, in part, to its effects on tuning the child’s brain to sociality.

EEG-based biometrics represent a relatively recent research field that aims to recognize individuals based on their recorded brain activity using electroencephalography (EEG). Among the numerous features that have been proposed, connectivity-based approaches represent one of the more promising methods tested so far. In this paper, using the phase lag index (PLI) and the phase locking value (PLV) methods, we investigate how the performance of a connectivity-based EEG biometric system varies with respect to different time windows (using epochs of different lengths ranging from 0.5 s to 12 s with a step of 0.5 s) to understand if it is possible to define the optimal duration of the EEG signal required to extract those distinctive features. All the analyses were performed on two freely available EEG datasets, including 109 and 23 subjects, respectively. Overall, as expected, the results have shown a pronounced effect of the time window length on the biometric performance measured in terms of EER (equal error rate) and AUC (area under the curve), with an evident increase in the biometric performance as the time window increases. Furthermore, our initial findings strongly suggest that enlarging the window size beyond a specific maximum threshold fails to enhance the performance of biometric systems. In conclusions, we want to highlight that EEG connectivity has the potential to represent an optimal candidate as an EEG fingerprint and that, in this context, it is essential to establish an adequate time window capable of capturing subject-specific features. Furthermore, we speculate that the poor performance obtained with short time windows mainly depends on the difficulty of correctly estimating the connectivity metrics from very small EEG epochs (shorter than 8 s).

Background. The problem of concept formation in the visual modality remains largely unexplored. There are many studies on the formation of verbal concepts in the learning process. However, it has not yet been fully investigated how concept formation occurs in conditions when both the signified and the signifier (sign) are represented only in the visual modality and what brain mechanisms are involved in this process. Objectives. The aim of the present work was to identify the brain mechanisms of visual concepts formation, on the basis of EEG registration with subsequent localization of the sources of electrical activity. Another task is to evaluate the possibility of actualizing the process of indirect learning in the formation of visual concepts. Study Participants. 26 Russian-speaking subjects without neurological disorders: 10 males and 16 females (aged 18 to 40 years, mean age 22.92 years, SD = 6.38) participated in the study. Methods. Chinese hieroglyphs, unfamiliar to the subjects, were taken as signs. The designated ones were emoticons (schematic faces expressing various emotions). A total of 10 pairs of stimuli sign-designated were presented. A 19-channel electroencephalogram (EEG) was recorded in three successive series: 1) the initial perception of hieroglyphic signs without assigning them a category; 2) categorization of these signs in the process of associative learning, with only 8 designating stimuli-signs directly associated with the designated stimulus, while 2 stimuli-signs were never associated with the stimulus-designator, their meaning was established indirectly; 3) subsequent perception of signs with the meaning already assigned to them. All stimuli were presented to the subjects visually. A new method of localization of brain activity, “Virtually implanted electrode”, developed by A.V. Vartanov (patent RU No. 2 785 268) was applied. Results. The analysis of the subjects' responses showed that during the learning process all signs (including those formed indirectly) were assigned a certain meaning (designated). Differences in event-related potentials (ERP) were found in leads C3 and CZ. Significant differences in ERP as a result of learning were revealed in a number of brain structures. It was found that a number of functional connections between the left area of the secondary visual cortex and the right part of the cerebellum changed significantly as a result of learning. Conclusions. The development of visual categories is ensured by the coordinated work of the right part of the cerebellum, parahippocampal gyrus and primary visual cortex, which is confirmed by the discovered differences in the corresponding ERPs.

Abstract Partially autonomous vehicles can help minimize human errors. However, being free from some driving subtasks can result in a low vigilance state, which can affect the driver’s attention towards the road. The present study first tested whether drivers of partially autonomous vehicles would benefit from the addition of auditory versions of the messages presented in variable message signs (VMS), particularly, when they find themselves in a monotonous driving situation. A second aim was to test whether the addition of auditory messages would also produce an indirect effect on the driver’s vigilance, improving performance on other driving subtasks not related to the message processing. Forty-three volunteers participated in a driving simulator study. They completed two tasks: (a) a VMS task, where they had to regain manual control of the car if the VMS message was critical, and (b) a car-following task, where they had to pay attention to the preceding car to respond to occasional brake events. Behavioral and EEG data were registered. Overall, results indicated that the addition of audio messages helped drivers process VMS information more effectively and maintain a higher level of vigilance throughout the driving time. These findings would provide useful information for the development of partially automated vehicles, as their design must guarantee that the driver remains attentive enough to assume control when necessary.

Abstract Introduction The use of visual and proprioceptive feedback is a key property of motor rehabilitation techniques. This feedback can be used alone, for example, for vision in mirror or video therapy, for proprioception in focal tendon vibration therapy, or in combination, for example, in robot-assisted training. This Electroencephalographic (EEG) study in healthy subjects explored the distinct neurophysiological impact of adding visual (video therapy), proprioceptive (focal tendinous vibration), or combined feedback (video therapy and focal tendinous vibration) to a motor imagery task. Methods Sixteen healthy volunteers performed 20 mental imagery (MI) tasks involving right wrist extension and flexion under four conditions: MI alone (IA), MI + video feedback observation (IO), MI + vibratory feedback (IV), and MI + observation + vibratory feedback (IOV). Brain activity was monitored with EEG, and time-frequency neurophysiological markers of movement were computed. The emotions of the patients were also measured during the task. Results In the alpha band, we observed bilateral ERD in the visual feedback conditions (IO, IOV). In the beta band, the ERD was bilateral in the IA, IV and IOV but more lateralized in the IV and IOV. After movement, we observed strong ERS in the IO and IOV but not in the IA or IV. Embodiment was stronger in conditions with vibratory feedback (IOV > IV > IA and IO) Conclusion Conditions with visual feedback (IO, IOV) recruit the mirror neurons system (alpha ERD) and provide more accurate feedback of the task than IA and IV, which triggers motor validation pathways (beta rebound analysis). Vibratory feedback enhances the recruitment of the left sensorimotor areas, with a synergistic effect in the IOV (beta ERD analysis), thus maximizing embodiment. Visual and vibratory feedback recruits the sensorimotor cortex during motor imagery in different ways and can be combined to maximize the benefits of both techniques Trial registration https://clinicaltrials.gov/study/NCT04449328 .

Abstract Higher cardiorespiratory fitness has been associated with improved cognitive control in preadolescent children, with various studies highlighting related brain health benefits. This cross-sectional study aimed to provide novel insights into the fitness-cognition relationship by investigating task-related changes in effective connectivity within two brain networks involved in cognitive control: the cingulo-opercular and fronto-parietal networks. Twenty-four higher-fit and twenty-four lower-fit preadolescent children completed a modified flanker task that modulated inhibitory control demand while their EEG and task performance were concurrently recorded. Effective connectivity for correct trials in the theta band was estimated using directed transfer function. The results indicate that children with higher fitness levels demonstrated greater connectivity in specific directions within the cingulo-opercular network (average effect size, d = 0.72). Brain-behavior correlations demonstrated a positive association between the majority of these connections and general task accuracy, which was also higher in higher fit children (average correlation coefficient, ρ = 0.34). The findings further support a positive relationship between fitness and cognitive performance in children. EEG findings offer novel insights into the potential brain mechanisms underlying the fitness-cognition relationship. The study suggests that increased task-related connectivity within the cingulo-opercular network may mediate the cognitive benefits associated with higher fitness levels in preadolescent children.

Abstract The world has a higher count of death rates as a result of Alcohol consumption. Identification is possible because Alcoholic EEG waves have a certain behavior that is totally different compared to the non-alcoholic individual. The available approaches take longer to provide the feedback because they analyze the data manually. For this reason, in the present paper we propose a novel approach applied to detect alcoholic EEG signals automatically by using deep learning methods. Our strategy has advantages as far as fast detection is concerned; hence people can help immediately when there is a need. The potential for a significant decrease in deaths from alcohol poisoning and improvement to public health is presented by this advancement. In order to create clusters and classify the alcoholic EEG signals, this research uses a cascaded process. To begin with, an initial clustering and feature extraction is done by LASSO regression. After that, a variety of meta-heuristics algorithms like Particle Swarm Optimization (PSO), Binary Coding Harmony Search (BCHS) as well as Binary Dragonfly Algorithm (BDA) are employed for feature minimization. When this method is used, normal and alcoholic EEG signals may be differentiated using non-linear features. PSO, BCHS, and BDA features allow for estimation of statistical parameters through t-test, Friedman statistic test, Mann-Whitney U test, and Z-Score with corresponding p-values for alcoholic EEG signals. Lastly, classification is done by the use of support vector machines (SVM) (including linear, polynomial, and Gaussian kernels), random forests, artificial neural networks (ANN), enhanced artificial neural networks (EANN), and LSTM models. Results showed that LASSO regression with BDA-based EANN proposed classifier have a classification accuracy of 99.59%, indicating that our method is highly accurate at classifying alcoholic EEG signals.

Abstract Goal-directed memory reactivation involves retrieving the most relevant information for the current behavioral goal. Previous research has linked this process to activations in the fronto-parietal network, but the underlying neurocognitive mechanism remains poorly understood. The current electroencephalogram (EEG) study explores attentional selection as a possible mechanism supporting goal-directed retrieval. We designed a long-term memory experiment containing three phases. First, participants learned associations between objects and two screen locations. In a following phase, we changed the relevance of some locations (selective cue condition) to simulate goal-directed retrieval. We also introduced a control condition, in which the original associations remained unchanged (neutral cue condition). Behavior performance measured during the final retrieval phase revealed faster and more confident responses in the selective vs. neutral condition. At the EEG level, we found significant differences in decoding accuracy, with above-chance effects in the selective cue condition but not in the neutral cue condition. Additionally, we observed a stronger posterior contralateral negativity and lateralized alpha power in the selective cue condition. Overall, these results suggest that attentional selection enhances task-relevant information accessibility, emphasizing its role in goal-directed memory retrieval.

Abstract Background Childhood adversity has been associated with alterations in threat-related information processing, including heightened perceptual sensitivity and attention bias towards threatening facial expressions, as well as hostile attributions of neutral faces, although there is a large degree of variability and inconsistency in reported findings. Methods Here, we aimed to implicitly measure neural facial expression processing in 120 adolescents between 12 and 16 years old with and without exposure to childhood adversity. Participants were excluded if they had any major medical or neurological disorder or intellectual disability, were pregnant, used psychotropic medication or reported acute suicidality or an ongoing abusive situation. We combined fast periodic visual stimulation with electroencephalography in two separate paradigms to assess the neural sensitivity and responsivity towards neutral and expressive, i.e. happy and angry, faces. Linear mixed effects models were used to assess the impact of childhood adversity on facial expression processing. Results Sixty-six girls, 53 boys and one adolescent who identified as ‘other’, between 12 and 16 years old (M = 13.93), participated in the current study. Of those, 64 participants were exposed to childhood adversity. In contrast to our hypotheses, adolescents exposed to adversity show lower expression-discrimination responses for angry faces presented in between neutral faces and higher expression-discrimination responses for happy faces presented in between neutral faces than unexposed controls. Moreover, adolescents exposed to adversity, but not unexposed controls, showed lower neural responsivity to both angry and neutral faces that were simultaneously presented. Conclusions We therefore conclude that childhood adversity is associated with a hostile attribution of neutral faces, thereby reducing the dissimilarity between neutral and angry faces. This reduced threat-safety discrimination may increase risk for psychopathology in individuals exposed to childhood adversity.

Abstract Background Electroencephalography (EEG) is a promising tool for identifying the physiological biomarkers of fibromyalgia (FM). Evidence suggests differences in power band and density between individuals with FM and healthy controls. EEG changes appear to be associated with pain intensity; however, their relationship with the quality of pain has not been examined. We aimed to investigate whether abnormal EEG in the frontal and central points of the 10–20 EEG system in individuals with FM is associated with pain’s sensory-discriminative and affective-motivational dimensions. The association between EEG and the two dimensions of emotional disorders (depression and anxiety) was also investigated. Methods In this cross-sectional pilot study, pain experience (pain rating index [PRI]) and two dimensions of emotional disorders (depression and anxiety) were assessed using the McGill Pain Questionnaire (PRI-sensory and PRI-affective) and Hospital Anxiety and Depression Scale (HADS), respectively. In quantitative EEG analysis, the relative spectral power of each frequency band (delta, theta, alpha, and beta) was identified in the frontal and central points during rest. Results A negative correlation was found between the relative spectral power for the delta bands in the frontal (r= -0.656; p = 0.028) and central points (r= -0.624; p = 0.040) and the PRI-affective scores. A positive correlation was found between the alpha bands in the frontal (r = 0.642; p = 0.033) and central points (r = 0.642; p = 0.033) and the PRI-affective scores. A negative correlation between the delta bands in the central points and the anxiety subscale of the HADS (r = -0.648; p = 0.031) was detected. Conclusion The affective-motivational dimension of pain and mood disorders may be related to abnormal patterns of electrical activity in patients with FM. Trial registration Retrospectively registered on ClinicalTrials.gov (NCT05962658).

The R47H variant of triggering receptor expressed on myeloid cells 2 (TREM2) increases the risk of Alzheimers disease (AD). To investigate potential mechanisms, we analyzed knockin mice expressing human TREM2-R47H from one mutant mouse Trem2 allele. TREM2-R47H mice showed increased seizure activity in response to an acute excitotoxin challenge, compared to wildtype controls or knockin mice expressing the common variant of human TREM2. TREM2-R47H also increased spontaneous thalamocortical epileptiform activity in App knockin mice expressing amyloid precursor proteins bearing autosomal dominant AD mutations and a humanized amyloid-β sequence. In mice with or without such App modifications, TREM2-R47H increased the density of putative synapses in cortical regions without amyloid plaques. TREM2-R47H did not affect synaptic density in hippocampal regions with or without plaques. We conclude that TREM2-R47H increases AD-related network hyperexcitability and that it may do so, at least in part, by causing an imbalance in synaptic densities across brain regions.

Alcohol use disorders (AUD) are commonly occurring, heritable and polygenic disorders with etiological origins in the brain and the environment. To outline the causes and consequences of alcohol-related milestones, including AUD, and their related psychiatric comorbidities, the Collaborative Study on the Genetics of Alcoholism (COGA) was launched in 1989 with a gene-brain-behavior framework. COGA is a family based, diverse (~25% self-identified African American, ~52% female) sample, including data on 17,878 individuals, ages 7-97 years, in 2246 families of which a proportion are densely affected for AUD. All participants responded to questionnaires (e.g., personality) and the Semi-Structured Assessment for the Genetics of Alcoholism (SSAGA) which gathers information on psychiatric diagnoses, conditions and related behaviors (e.g., parental monitoring). In addition, 9871 individuals have brain function data from electroencephalogram (EEG) recordings while 12,009 individuals have been genotyped on genome-wide association study (GWAS) arrays. A series of functional genomics studies examine the specific cellular and molecular mechanisms underlying AUD. This overview provides the framework for the development of COGA as a scientific resource in the past three decades, with individual reviews providing in-depth descriptions of data on and discoveries from behavioral and clinical, brain function, genetic and functional genomics data. The value of COGA also resides in its data sharing policies, its efforts to communicate scientific findings to the broader community via a project website and its potential to nurture early career investigators and to generate independent research that has broadened the impact of gene-brain-behavior research into AUD.

A prominent theoretical framework spanning philosophy, psychology, and neuroscience holds that selective attention penetrates early stages of perceptual processing to alter the subjective visual experience of behaviorally relevant stimuli. For example, searching for a red apple at the grocery store might make the relevant color appear brighter and more saturated compared with seeing the exact same red apple while searching for a yellow banana. In contrast, recent proposals argue that data supporting attention-related changes in appearance reflect decision- and motor-level response biases without concurrent changes in perceptual experience. Here, we tested these accounts by evaluating attentional modulations of EEG responses recorded from male and female human subjects while they compared the perceived contrast of attended and unattended visual stimuli rendered at different levels of physical contrast. We found that attention enhanced the amplitude of the P1 component, an early evoked potential measured over visual cortex. A linking model based on signal detection theory suggests that response gain modulations of the P1 component track attention-induced changes in perceived contrast as measured with behavior. In contrast, attentional cues induced changes in the baseline amplitude of posterior alpha band oscillations (∼9-12 Hz), an effect that best accounts for cue-induced response biases, particularly when no stimuli are presented or when competing stimuli are similar and decisional uncertainty is high. The observation of dissociable neural markers that are linked to changes in subjective appearance and response bias supports a more unified theoretical account and demonstrates an approach to isolate subjective aspects of selective information processing.SIGNIFICANCE STATEMENT Does attention alter visual appearance, or does it simply induce response bias? In the present study, we examined these competing accounts using EEG and linking models based on signal detection theory. We found that response gain modulations of the visually evoked P1 component best accounted for attention-induced changes in visual appearance. In contrast, cue-induced baseline shifts in alpha band activity better explained response biases. Together, these results suggest that attention concurrently impacts visual appearance and response bias, and that these processes can be experimentally isolated.

The processing and recognition of electroencephalogram (EEG) signal is the most important part of brain-computer interface (BCI) system, and the quality of signal processing and recognition is directly related to the effectiveness of BCI system. Aiming at the problems of incomplete removal of artifacts and inadequate retention of active components in EEG signal, a fusion method of wavelet transform (WT) and Fast Independent Component Analysis (FastICA) is utilized to preprocess the raw EEG signals. The fusion method can remove noise artifacts while preserving effective information. Aiming at the problems of poor time-frequency resolution and low classification accuracy of the traditional feature extraction method, a feature extraction algorithm on the basis of hybrid Discrete Wavelet Transform (DWT) and Empirical Mode Decomposition (EMD) is proposed. Firstly, DWT is used to analyze the pre-processed EEG signal to obtain a series of sub-band signals. Then, EMD decomposition is applied to subband signal and eigenmode function is extracted to complete feature integration. Finally, the feature extraction results are input into the Support Vector Machine (SVM) for classification. Comparative experiments show that the classification accuracy of the proposed method reaches 91.32 %, which is significantly higher than other algorithms.

Abstract This letter to the Editor discusses the case of a 73-year-old male with mild SARS-CoV-2 infection who one month later developed rapidly progressive cognitive decline, and imaging findings suggestive of Creutzfeldt–Jakob disease (CJD). The diagnosis of sporadic CJD was made on the basis of clinical presentation (rapidly progressive decline, depression, gait disturbance, incontinence, mutism), cerebral MRI (small infarcts, atrophy), hybrid FDG-PET (putaminal and thalamic diffusion restriction, bifrontal hypometabolism), and elevated 14-3-3 in the cerebrospinal fluid (CSF). Despite administration of glucocorticoids, the patient died three months after the onset of symptoms. No autopsy was performed. This case raises the question of a possible link between SARS-CoV-2 infection and the subsequent development of CJD-like syndromes, which warrants further investigation.