Search

Background: Gardening as a leisure activity has been shown to be potentially beneficial for older adults, this study aimed to assess the physiological and psychological impacts of a specific horticultural activity, watering indoor plants, compared with playing a computer game - the Klondike Solitaire Card Game. Methods: This crossover study included 100 older adults recruited from a nursing care facility for older people in Shanghai, China. They were allocated to either engage in watering indoor plants (horticultural group) or playing a computer game (control group). For the inclusion criteria, screening for cognitive function was conducted to exclude those with severe cognitive impairments, thereby focusing on those capable of participating fully in the study activities. Brainwave activities, blood pressure, and psychological states were measured through pre- and post-test questionnaires to evaluate the physiological and psychological effects. Results: The activity of watering indoor plants was associated with significant reductions in both systolic and diastolic blood pressure, in contrast to the computer game activity. Additionally, participants in the horticultural group showed increased brainwave mean values, indicating heightened relaxation and focus, while decreases were observed in the control group. The experimental group reported reduced anxiety scores, along with increased feelings of comfort and relaxation, and also exhibited significantly higher meditation levels, which indicates a profound mental relaxation effect. Conclusion: Our findings suggest that engaging in indoor horticultural activities, such as watering houseplants for 10 minutes, can reduce mental and physical stress levels in older adults. [ABSTRACT FROM AUTHOR]

In recent decades, entropy measures have gained prominence in neuroscience due to the nonlinear behaviour exhibited by neural systems. This rationale justifies the application of methods from the theory of nonlinear dynamics to cerebral activity, aiming to detect and quantify its variability more effectively. In the context of electroencephalogram (EEG) signals, entropy analysis offers valuable insights into the complexity and irregularity of electromagnetic brain activity. By moving beyond linear analyses, entropy measures provide a deeper understanding of neural dynamics, particularly pertinent in elucidating the mechanisms underlying brain aging and various acute/chronic-progressive neurological disorders. Indeed, various pathologies can disrupt nonlinear structuring in neural activity, which may remain undetected by linear methods such as power spectral analysis. Consequently, the utilization of nonlinear tools, including entropy analysis, becomes crucial for capturing these alterations. To establish the relevance of entropy analysis and its potential to discern between physiological and pathological conditions, this review discusses its diverse applications in studying healthy brain aging and neurodegenerative diseases, including Alzheimer's disease (AD) and Parkinson's disease (PD). Various entropy parameters, such as approximate entropy (ApEn), sample entropy (SampEn), multiscale entropy (MSE), and permutation entropy (PermEn), are analysed within this context. By quantifying the complexity and irregularity of EEG signals, entropy analysis may serve as a valuable biomarker for early diagnosis, treatment monitoring, and disease management. Such insights offer clinicians crucial information for devising personalized treatment and rehabilitation plans tailored to individual patients. [ABSTRACT FROM AUTHOR]

EEG-based brain–computer interface systems have demonstrated impressive capability in the context of the broad use of deep learning. However, serious security flaws have been shown by their vulnerability to adversarial sample attacks. It is not possible to directly apply traditional attack strategies from the image domain to EEG signals because of their unique visualization and dynamic features. A unique multi-target smooth universal adversarial perturbation (MS-UAP) approach designed specifically for EEG data is presented in this paper to address this difficulty. We define a loss function and optimize parameters based on UAP for both target and nontarget class samples, respectively, to address the temporal nature of the EEG signal and the attack’s flexibility. Additionally, this research generates a smoothed adversarial sample by convolution operations, yielding a more disorienting effect, to avoid the introduction of physiologically untrustworthy square wave distortions caused by typical visual attack approaches. Three public EEG datasets were used for extensive testing and assessments, and the results show that MS-UAP successfully targets convolutional neural network (CNN) classifiers and launches simultaneous attacks on several classes, exhibiting strong model transferability. To ensure accurate attacks that go undetected, MS-UAP creates a single disturbance that is targeted at target classes and has the least negative effect on other classes. In order to facilitate effective real-time attacks, MS-UAP, an offline-generated universal perturbation template, can be smoothly incorporated into EEG data. The study is significant since it emphasizes how important it is to improve BCI system security and promote the creation of stronger defenses. [ABSTRACT FROM AUTHOR]

“Trying too hard” can interfere with skilled movement, such as sports and music playing. Postural control can similarly suffer when conscious attention is directed toward it (“conscious movement processing”; CMP). However, the neural mechanisms through which CMP influences balance remain poorly understood. We explored the effects of CMP on electroencephalographic (EEG) perturbation-evoked cortical responses and subsequent balance performance. Twenty healthy young adults (age = 25.1 ± 5 years; 10 males and 10 females) stood on a force plate-embedded moveable platform while mobile EEG was recorded. Participants completed two blocks of 50 discrete perturbations, containing an even mix of slower (186 mm/s peak velocity) and faster (225 mm/s peak velocity) perturbations. One block was performed under conditions of CMP (i.e., instructions to consciously control balance), while the other was performed under “Control” conditions with no additional instructions. For both slow and fast perturbations, CMP resulted in significantly smaller cortical N1 signals (a perturbation-evoked potential localized to the supplementary motor area) and lower sensorimotor beta EEG activity 200–400 ms postperturbation. Significantly greater peak velocities of the center of pressure (i.e., greater postural instability) were also observed during the CMP condition. Our findings provide the first evidence that disruptions to postural control during CMP may be a consequence of insufficient cortical activation relevant for balance (i.e., insufficient cortical N1 responses followed by enhanced beta suppression). We propose that conscious attempts to minimize postural instability through CMP acts as a cognitive dual-task that dampens the sensitivity of the sensorimotor system for future losses of balance. [ABSTRACT FROM AUTHOR]

Developmental, resting electroencephalography (EEG) is gaining rapid popularity with implementation in large‐scale studies as well as a recent WHO report naming resting EEG as a gold standard measure of brain health. With an increased interest in resting EEG as a potential biomarker for neurocognition, it is paramount that resting EEG findings are reliable and reproducible. One of the major threats to replicability and reproducibility stems from variations in preprocessing and analysis. One of the primary challenges facing the field of developmental EEG is that it can be challenging to acquire data from infants and children, which commonly makes data cleaning and analysis difficult and unstandardized. The goal of the present manuscript is to take a state of the field of the methods experts in resting EEG report they would use to clean and analyze a hypothetical data set. Here we report on the responses of 66 self‐identified experts in developmental psychophysiology, none of which submitted identical preregistrations. As expected, there were areas of more and less consensus, but ultimately, we believe our findings highlight opportunities for core methodological work and field‐level efforts to establish consensus. [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]

AI is anticipated to improve human decision-making across various domains, often in high-stakes, difficult tasks. However, human reliance on AI recommendations is often inappropriate. A common approach to address this is to provide explanations about the AI output to decision makers, but results have been mixed so far. It often remains unclear when people can rely appropriately on AI and when explanations can help. In this work, we conducted a lab experiment (N = 34) to investigate how the appropriateness of human reliance on (explainable) AI depends on the mental workload induced by different decision difficulties. Instead of self-assessments, we used EEG (Emotiv Epoc Flex head cap, 32 wet electrodes) to more directly measure participants' mental workload. We found that the difficulty of a decision, indicated by the induced mental workload, strongly influences participants' ability to rely appropriately on AI, as assessed through relative self-reliance, relative AI reliance, and decision accuracy with and without AI. While reliance was appropriate for low mental workload decisions, participants were prone to overreliance in high mental workload decisions. Explanations had no significant effect in either case. Our results imply that alternatives to the common ‘recommend-and-explain’ approach should be explored to assist human decision-making in challenging tasks. [ABSTRACT FROM AUTHOR]

Patients who develop chimeric antigen receptor (CAR) T-cell-related immune effector cell-associated neurotoxicity syndrome (ICANS) frequently undergo evaluation with electroencephalography (EEG). We hypothesize that EEG features and Synek scale score, a measure of degree of EEG abnormality, are associated with ICANS severity. Here, we performed a retrospective review of 125 adult patients at Memorial Sloan Kettering Cancer Center (MSKCC) who received CAR-T cell therapy from 2010 to 2019, including 53 patients with B-acute lymphoblastic leukemia treated with 1928z CAR T cells (NCT01044069) and 72 patients with large B-cell lymphoma (LBCL) treated with the commercial CAR T products axicabtagene ciloleucel or tisagenlecleucel. We collected video EEG monitoring (27 with B-ALL and 20 with LBCL) and recorded daily EEG features, Synek scores, and ICANS grade for 47 eligible patients. Synek scale and ICANS grade were positively correlated (correlation coefficient 0.47, 95% CI: 0.31–0.60). This was further corroborated in the univariable model associating high Synek scale (3 or 4) with high ICANS grade (OR = 15.2; 95%CI:7.8–29.7, p < 0.0001). EEG features such as discontinuity, absence of posterior dominant rhythm, and presence of generalized sharp waves were statistically significantly associated with higher ICANS grade in univariable models. In the multivariable model, discontinuity (OR = 4.2 (95%CI:1.3–13.8, p = 0.02) and absence of posterior dominant rhythm (OR = 10.5 (95%CI:4.6–23.9, p < 0.0001) were statistically associated with higher ICANS grade. Overall, EEG discontinuity and absence of posterior dominant rhythm were independently associated with higher severity of neurotoxicity. Further, our data suggest that Synek Scale, may be a severity marker for neurotoxicity. [ABSTRACT FROM AUTHOR]

Identifying relationships between structural and functional networks is crucial for understanding the large-scale organization of the human brain. The potential contribution of emerging techniques like functional near-infrared spectroscopy to investigate the structure–functional relationship has yet to be explored. In our study, using simultaneous Electroencephalography (EEG) and Functional near-infrared spectroscopy (fNIRS) recordings from 18 subjects, we characterize global and local structure–function coupling using source-reconstructed EEG and fNIRS signals in both resting state and motor imagery tasks, as this relationship during task periods remains underexplored. Employing the mathematical framework of graph signal processing, we investigate how this relationship varies across electrical and hemodynamic networks and different brain states. Results show that fNIRS structure–function coupling resembles slower-frequency EEG coupling at rest, with variations across brain states and oscillations. Locally, the relationship is heterogeneous, with greater coupling in the sensory cortex and increased decoupling in the association cortex, following the unimodal to transmodal gradient. Discrepancies between EEG and fNIRS are noted, particularly in the frontoparietal network. Cross-band representations of neural activity revealed lower correspondence between electrical and hemodynamic activity in the transmodal cortex, irrespective of brain state while showing specificity for the somatomotor network during a motor imagery task. Overall, these findings initiate a multimodal comprehension of structure–function relationship and brain organization when using affordable functional brain imaging. [ABSTRACT FROM AUTHOR]

First, research on sleep stage staging in older individuals is highly valuable because it helps to obtain insights into sleep quality and structure changes that occur in older persons. This approach was used to better understand the correlations with cognitive functioning, the immune system, and mental health, among other factors. Second, identifying the features of older persons in various stages of sleep could lead to more specific guidance for managing and treating personalized sleep to significantly improve the quality of life of these individuals. Finally, in-depth research on sleep in older persons can assist in the development of preventative and intervention measures for older adults, which can help to reduce the negative consequences that age-related sleep issues have on general health. Categorizing sleep EEG data is the focus of this research to present an improved squeeze-and-excitation network (SENet) for application in the classification process. For the purpose of this study, electroencephalogram (EEG) features were extracted using continuous wavelet transform. Subsequently, the lightweight context transform (LCT), which is a combination of normalization, linear transformation, and SENet, is carried out to achieve sleep EEG classification. Extraction using the continuous wavelet transform has the potential to more accurately depict the changes and characteristics of sleep stages. As a result of incorporating LCT, the computational cost of the model is reduced, and the model becomes more applicable to real-world situations. Experiments conducted on two public datasets, Sleep-EDF-20 and Sleep-EDF-78, demonstrated that the strategy presented in this study can achieve the requisite classification performance and the model converges faster. [ABSTRACT FROM AUTHOR]

Objective Methods Results Significance Few studies have evaluated the efficacy of antiseizure medications (ASMs) according to the etiology of neonatal acute provoked seizures. We aimed to investigate the response to ASMs in term/near term neonates with acute arterial ischemic stroke (AIS), as well as the type of seizure at presentation and the monitoring approach.We retrospectively evaluated neonates from 15 European level IV neonatal intensive care units who presented with seizures due to AIS and were monitored by continuous electroencephalography (cEEG) and/or amplitude‐integrated EEG (aEEG) in whom actual recordings, timing, doses, and response to ASMs were available for review.One hundred seven neonates were referred, and 88 were included. Of those, 56 met the criteria for evaluating the treatment response. The mean time to treatment was 7.9 h (SD = 16.4), and the most frequently administered first‐line ASM was phenobarbital (PB; 74/88, 84.1%). Seizures were controlled within 24 h from onset of symptoms in 64.3% (36/56) of neonates. Phenytoin (PHT) was effective in almost all neonates in whom it was trialed (24/25, 96.0%), whereas PB was effective in only 22.0% of patients (11/50). Infants treated with PB or PHT as first‐line treatment (53/56, 94.6%) showed a higher response rate with PHT (6/6, 100.0%) than with PB (11/47, 23.4%). Monitoring approach and seizure types were evaluated in 88 infants. Forty‐six of 88 (52.3%) were monitored with cEEG and 47.7% (42/88) with aEEG, with or without intermittent cEEG. The mean monitoring duration was 65.8 h (SD = 39.21). In 83 of 88 (94.3%) infants, the type of seizure suspected clinically prior to monitoring was confirmed afterward. Unilateral focal clonic seizures were seen in 71 of 88 infants (80.7%), whereas 11 of 88 (12.5%) presented with ictal apneas.Our findings provide evidence in a large, homogenous cohort that PHT is more effective than PB in treating neonatal acute symptomatic seizures due to AIS. [ABSTRACT FROM AUTHOR]

While the impact of task difficulty level on working memory (WM) has been addressed using a visual N-Back task, only few studies considered the effect of stimulus blur level. We investigated the effect of N-Back difficultly level, blur level as well as stimulus type using, for the first time, electroencephalogram (EEG) responses. We recruited 18 healthy young individuals and recorded their behavioural and EEG responses when participating in a single N-Back session. Our results showed interactions between blur level and stimulus type. Frequency band differences were found for all three stimulus types (faces, objects and natural scenes) when comparing no blur, modest, and severe blur. Additionally, when comparing stimuli, we found differences in higher frequency bands for faces compared to objects and natural scenes, for all three N-Back levels. Only in the frontal areas, differences in higher frequency bands were identified in objects compared to faces and natural scenes. These results offer new insights into the neural dynamics underlying WM. [ABSTRACT FROM AUTHOR]

• Study explored cognitive-motor interference as task difficulty increased in adults. • Cognitive errors rose with motor complexity during dual-task walking. • P3a amplitude increased, while P3b latency lengthened under task complexity. • P3 analysis revealed increased attentional demand with cognitive difficulty. • Results highlight need to balance attentional resources in cognitive-motor tasks. Dual-task paradigms, which involve performing cognitive and motor tasks simultaneously, are commonly used to study how attentional resources are allocated and managed under varying task demands. This study aimed to investigate cognitive-motor interferences (CMI) under different levels of cognitive and motor task difficulty without instruction on task prioritization. 17 healthy young adults performed an auditory oddball task with increasing cognitive and motor (walking vs. sitting) difficulty. Cognitive and motor performances, along with P3 (P3a and P3b) brainwave components, were analysed. Increasing cognitive difficulty resulted in more errors and increased P3a amplitude, reflecting enhanced attentional demand, while P3b remained unaffected. This suggests a threshold effect on attentional resources. Motor complexity lengthened P3a and P3b latencies without affecting amplitude, indicating delayed attentional resource recruitment. Additionally, walking with the most difficult cognitive task increased cognitive error, suggesting attentional resource limits. With increased motor and cognitive complexity, CMI emerged, leading to cognitive error increase and improved gait stability without amplitude changes in P3a and P3b. Two hypotheses were proposed: motor prioritization and motor facilitation. Our study suggests managing attentional resources to balance cognitive and motor tasks rather than linearly increasing task complexity. Viewing dual tasks as a new, integrated task is proposed, supported by previous neural network integration studies. Thus, understanding how the brain organizes tasks in response to constraints is crucial for comprehending complex task execution. [ABSTRACT FROM AUTHOR]

Neural entrainment has become a popular technique to non-invasively manipulate brain rhythms via external, periodic stimulation. However, there is still debate regarding its underlying mechanisms and effects on brain activity. Here, we used EEG recordings during a visual entrainment paradigm to assess characteristic changes in the spectral content of EEG signals due to entrainment. Our results demonstrate that entrainment not only increases synchrony between neural oscillations and the entraining stimulus but also elicits previously unreported spectral tuning effects and long-lasting after-effects. These findings offer compelling evidence for the presence of dedicated, flexible, and adaptive mechanisms for neural entrainment, which may have key roles in adjusting the sensitivity and dynamic range of brain oscillators in response to environmental temporal structures. [ABSTRACT FROM AUTHOR]

Seizures have a serious impact on the physical function and daily life of epileptic patients. The automated detection of seizures can assist clinicians in taking preventive measures for patients during the diagnosis process. The combination of deep learning (DL) model with convolutional neural network (CNN) and transformer network can effectively extract both local and global features, resulting in improved seizure detection performance. In this study, an enhanced transformer network named Inresformer is proposed for seizure detection, which is combined with Inception and Residual network extracting different scale features of electroencephalography (EEG) signals to enrich the feature representation. In addition, the improved transformer network replaces the existing Feedforward layers with two half-step Feedforward layers to enhance the nonlinear representation of the model. The proposed architecture utilizes discrete wavelet transform (DWT) to decompose the original EEG signals, and the three sub-bands are selected for signal reconstruction. Then, the Co-MixUp method is adopted to solve the problem of data imbalance, and the processed signals are sent to the Inresformer network for seizure information capture and recognition. Finally, discriminant fusion is performed on the results of three-scale EEG sub-signals to achieve final seizure recognition. The proposed network achieves the best accuracy of 100% on Bonn dataset and the average accuracy of 98.03%, sensitivity of 95.65%, and specificity of 98.57% on the long-term CHB-MIT dataset. Compared to the existing DL networks, the proposed method holds significant potential for clinical research and diagnosis applications with competitive performance. [ABSTRACT FROM AUTHOR]

This study aims to explore the correlation between college students’ digital literacy and mental health and proposes a method based on Twin Support Vector Machines (TWSVMs) classification and chi-square validation correlation analysis. First, a group of college students’ digital literacy data was collected by designing and distributing questionnaires. The questionnaire covers multiple aspects such as digital skills, information literacy, and technology application, to comprehensively evaluate the students’ digital literacy level. The collected digital literacy data were classified using TWSVM to obtain the digital literacy assessment results. Next, the electroencephalogram (EEG) signals of the same group were collected, and the EEG signals were subjected to power spectral density (PSD) feature extraction and TWSVM classification model training to obtain the mental health identification results of each student. Finally, after obtaining the digital literacy assessment and mental health identification results, the chi-square validation method was used for correlation analysis to evaluate the linear relationship between the two. Through the analysis, we found that students with higher digital literacy were more likely to have good mental health. In comparison, students with lower digital literacy were more likely to have mental health problems. This study revealed a significant correlation between college students’ digital literacy and mental health, providing theoretical support and practical guidance for educators and mental health professionals. Improving students’ digital literacy will not only help their academic and career development but may also have a positive impact on their mental health, thereby promoting their overall development. [ABSTRACT FROM AUTHOR]

Based on electroencephalography (EEG) and video data, we propose a multimodal affective analysis approach in this study to examine the affective states of university students. This method is based on the findings of this investigation. The EEG signals and video data were obtained from 50 college students experiencing various emotional states, and then they were processed in great detail. The EEG signals are pre-processed to extract their multi-view characteristics. Additionally, the video data were processed by frame extraction, face detection, and convolutional neural network (CNN) operations to extract features. We take a feature splicing strategy to merge EEG and video data to produce a time series input to realize the fusion of multimodal features. This allows us to realize the fusion of multimodal features. In addition, we developed and trained a model for the classification of emotional states based on a long short-term memory network (LSTM). With the help of cross-validation, the experiments were carried out by dividing the dataset into a training set and a test set. The model’s performance was evaluated with the help of four metrics: accuracy, precision, recall, and F1-score. When compared to the single-modal method of sentiment analysis, the results demonstrate that the multimodal approach, which combines EEG and video, demonstrates considerable advantages in terms of sentiment detection. Specifically, the accuracy obtained from the multimodal approach is significantly higher. As part of its investigation, the study also investigates the respective contributions of EEG and video aspects to emotion detection. It discovers that these features complement each other in a variety of emotional states and have the potential to improve the overall recognition results. The multimodal sentiment analysis method that is based on LSTM offers a high level of accuracy and robustness when it comes to recognizing the affective states of college students. This is especially essential for enhancing the quality of education and providing support for mental health. [ABSTRACT FROM AUTHOR]

Humans categorize objects not only based on perceptual features (e.g. red, rounded), but also function (e.g. used to transport people). Category membership can be communicated via labelling (e.g. 'apple', 'vehicle'). While it is well established that even preverbal infants rely on labels to learn categories, it remains unclear what is the nature of those categories: whether they simply contain sets of visual features diagnostic of category membership, or whether they additionally contain abstract category markers or symbols (e.g. linguistic in the form of category labels or non-linguistic). To address this question, we first used labelling to teach two novel object categories, each composed of unfamiliar visually unrelated objects, to adults and nine-month-olds. Then, we assessed categorization in an electroencephalography category-oddball task. Both adults and infants displayed stronger neural responses to the infrequent category, which, in the absence of visual features shared by all category members, indicates that the categories they set up contained feature-independent category markers. Well before language production starts, labels help infants to discover categories without relying on perceptual similarities across objects and build category representations with summary elements that may be critical for the development of abstract thought. [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]

According to World Health Organization (WHO), it is estimated that approximately 50 million people have Epilepsy worldwide. 10 million people are effected in India by Epilepsy hardly very few come out with the disorder and undertake proper treatment rest risk their life without recognition of disease. Epilepsy is a chronic disease of the brain which is non-communicable. An epileptic seizure is the second most commonly occurring neurological disorder of the brain. It can be detected using Electroencephalography (EEG) signals, an effective diagnostic tool used to study brain anatomy. Technological advancements and modernization of analytical tools have improved the scope of validation and decision-making. It is important to predict and evaluate epilepsy in its initial stages to avoid the risk and complication. This review paper focuses on the methodology, techniques, and dataset used from different sources to classify and categorize epileptic seizures. The paper includes a detailed review of dataset sources, challenges, and ways forward in understanding epileptic seizures for futuristic learning and decision making. It also concentrates on the comparison of machine learning and deep learning methodology used with different datasets. [ABSTRACT FROM AUTHOR]

When the eye uses the brain and heart, the cardiovascular and nervous systems integrate and interact. Because changes in retinal microcirculation are independent predictors of cardiovascular events, the eye serves as a "display" to the cardiovascular system and brain. The eye, which has two circulatory systems and a rich vascular supply, is a prime candidate for this study and benefits from early damage to the target organ. Eye movements performed during the visual search pose a challenge in identifying critical points in the eye scene. Because it uses different brain pathways and relates to the cardiac cycle, humans' ability to spot anomalies under challenging circumstances means they are always needed for visual search. ECG (electrocardiogram), electroencephalogram (EEG), and eye tracking can improve visual search training and attention-tracking performance. EEG data can also be analyzed in real time using eye-tracking technology. Previous work has discussed the EEG or ECG concerning attraction during visual search. The eyeball's movement combined with the ECG in the previous investigation and introduced large electroencephalographic (EEG) artifacts. This assessment aims to (a) identify brain–heart coherent features influenced by the visual search task and (b) discover the behavior of EEG frequency bands and heart rate variability (HRV) features. EEG and ECG were used to analyze and predict inattention in individuals during a visual search task. The EEG determines human brain function and considers to detect the variability in the EEG frequency band. The work proposed a visual search task with EEG and ECG analysis. Five participants recorded EEG and ECG recordings in three different scenarios: rest, gaze tracking, and normal. Statistical evaluation was used to compare EEG and ECG characteristics and Pearson's correlation was employed for statistical analysis. Statistical ANOVA analysis revealed statistically significant (p > 0.05) differences between theta (F3) and alpha (F3) EEG and ECG features, as well as between theta (F4) and alpha (F4) EEG and ECG features. Additionally, alpha (F3) and theta (F3) were significant in the heart rate variability index (rMSSD), which monitored activity under eye tracking. There was also a significant difference between alpha (F3) and mean HR. Pearson's correlation between ECG and EEG shows that theta (O1) and alpha (O1) correlate with LF/HF and alpha (F3) and theta (F3) with rMSSD. Theta (F3) and mean heart rate were also correlated. Observing the above ECG and EEG characteristics can improve and control treatment options for conditions like neurovascular instability (NCVI), characterized by age-related changes in blood pressure and increased cerebral and cardiac leukoaraiosis. [ABSTRACT FROM AUTHOR]

Investigating social context effects and emotional modulation of attention in a laboratory setting is challenging. Electroencephalography (EEG) requires a controlled setting to avoid confounds, which goes against the nature of social interaction and emotional processing in real life. To bridge this gap, we developed a new paradigm to investigate the effects of social context and emotional expressions on attention in a laboratory setting. We co-registered eye-tracking and EEG to assess gaze behavior and brain activity while participants performed a discrimination task followed by feedback. Video clips of one second in which a confederate displayed either positive, neutral or negative expressions were presented as feedback to the discrimination task. Participants’ belief was manipulated by telling them that the videos were selected either by the computer (non-social condition) or by the experimenter in the adjacent room that observed them via videochat (social condition). We found that emotional expressions modulated late attention processing in the brain (EPN and LPC), but neither early processing (P1) nor saccade latency. Social context did not influence any of the variables studied. We conclude this new paradigm serves as a stepping stone to the development of new paradigms to study social interaction within EEG experiments. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

Cognitive load is a critical internal state associated with learners' learning process and significantly influences learning outcomes. With the worldwide popularity of video‐based learning (VBL), tracking real‐time cognitive load variations becomes more and more important for the timely provision of adaptive learning support during the learning process. This study proposed and validated an electroencephalogram (EEG)‐supported approach to tracking real‐time cognitive load variations during continuous VBL. We recruited 108 healthy adult participants to watch a specially designed video lecture with a sequence of interconnected slides of equal length. EEG signals were continuously recorded throughout the session. The video lecture was designed with varying levels of content difficulty (ie, rated from 1 to 5) across slides and was narrated at three different speeds (ie, slow, normal and fast) to induce cognitive load variations. For each slide, the cognitive load was quantified using both subjective ratings (ie, self‐reported difficulty) and an EEG‐derived measure (ie, alpha power). Through linear mixed model analysis, we demonstrated the feasibility of using alpha power to track real‐time cognitive load variations during the continuous VBL process after controlling the effect of mental fatigue. This study provides a foundation for developing learning enhancement technologies that enable the timely provision of adaptive learning support in VBL. Practitioner notes What is already known about this topic Video‐based learning has become a prevailing learning method for the current generation. Tracking the internal learning state of learners is essential for the timely provision of adaptive learning support during the video‐based learning process. Cognitive load is a critical aspect of internal learning state. While EEG has been proven to be valuable in assessing average cognitive load of a task, few studies have investigated the feasibility of utilizing EEG to track real‐time cognitive load variations in a task. What this paper adds An EEG‐supported approach was proposed to track real‐time cognitive load variations in video‐based learning. A high consistency was found between subjective ratings and EEG‐derived measure of cognitive load. The presence of mental fatigue exerted a significant impact on EEG‐derived measure of cognitive load. Implications for practice and/or policy Generative AI can be leveraged to facilitate mass production of lectures required in the approach. Real‐time tracking of cognitive load variations in video‐based learning enables the timely provision of adaptive learning supports. Additional research is warranted to mitigate the effect of mental fatigue on real‐time tracking of cognitive load variations. What is already known about this topic Video‐based learning has become a prevailing learning method for the current generation. Tracking the internal learning state of learners is essential for the timely provision of adaptive learning support during the video‐based learning process. Cognitive load is a critical aspect of internal learning state. While EEG has been proven to be valuable in assessing average cognitive load of a task, few studies have investigated the feasibility of utilizing EEG to track real‐time cognitive load variations in a task. What this paper adds An EEG‐supported approach was proposed to track real‐time cognitive load variations in video‐based learning. A high consistency was found between subjective ratings and EEG‐derived measure of cognitive load. The presence of mental fatigue exerted a significant impact on EEG‐derived measure of cognitive load. Implications for practice and/or policy Generative AI can be leveraged to facilitate mass production of lectures required in the approach. Real‐time tracking of cognitive load variations in video‐based learning enables the timely provision of adaptive learning supports. Additional research is warranted to mitigate the effect of mental fatigue on real‐time tracking of cognitive load variations. [ABSTRACT FROM AUTHOR]

Emotion recognition enables machines to more acutely perceive and understand users' emotional states, thereby offering more personalized and natural interactive experiences. Given the regularity of the responses of brain activity to human cognitive processes, we propose a powerful and novel dual-stream multi-level graph convolution network (DMGCN) with the ability to capture the hierarchies of connectivity between cerebral cortex neurons and improve computational efficiency. This consists of a hierarchical dynamic geometric interaction neural network (HDGIL) and multi-level feature fusion classifier (M2FC). First, the HDGIL diversifies representations by learning emotion-related representations in multi-level graphs. Subsequently, M2FC integrates advantages from methods for early and late feature fusion and enables the addition of more details to final representations from EEG samples. We conducted extensive experiments to validate the superiority of our model over numerous state-of-the-art (SOTA) baselines in terms of classification accuracy, the efficiency of graph embedding and information propagation, achieving accuracies of 98.73%, 95.97%, 72.74% and 94.89% for our model as well as increases of up to 0.59%, 0.32%, 2.24% and 3.17% over baselines on the DEAP-Arousal, DEAP-Valence, DEAP and SEED datasets, respectively. Additionally, these experiments demonstrated the effectiveness of each module for emotion recognition tasks. [ABSTRACT FROM AUTHOR]

Background/Objectives: In this study, a medical decision support system is presented to assist physicians in epileptic focus detection by correlating MRI and EEG data of temporal lobe epilepsy patients. Methods: By exploiting the asymmetry in the hippocampus in MRI images and using voxel-based morphometry analysis, gray matter reduction in the temporal and limbic lobes is detected, and epileptic focus prediction is realized. In addition, an epileptic focus is also determined by calculating the asymmetry score from EEG channels. Finally, epileptic focus detection was performed by associating MRI and EEG data with a decision tree. Results: The results obtained from the proposed algorithm provide 100% overlap with the physician's finding on the EEG data. Conclusions: MRI and EEG correlation in epileptic focus detection was improved compared with physicians. The proposed algorithm can be used as a medical decision support system for epilepsy diagnosis, treatment, and surgery planning. [ABSTRACT FROM AUTHOR]

Introduction: Delirium in postoperative geriatric hip fracture patients is a serious and often preventable condition. If detected in time, it can be treated, but a delay in the diagnosis and initiation of treatment impairs outcomes. A novel approach to detect delirium is to use point-of-care electro-encephalogram (EEG) recording with automated analysis. In this study, the authors investigated whether screening for delirium with EEG recording and automated analysis resulted in reduced length of stay after surgery and superior screening performance in comparison to the Delirium Observation Screening Scale (DOS). Methods: This randomized control trial was conducted at two geriatric trauma centres in the Netherlands. Patients were eligible for inclusion if they were aged 70 years or above, were admitted to the geriatric trauma unit and undergoing surgery for a hip fracture. Patients were randomized to either the intervention (EEG-based brainwave analysis) or control group (DOSS screening tool). Participants were screened for delirium twice a day during three consecutive days starting at day 0 of the surgery, with the first measurement before the surgery. The primary outcome was length of stay. In addition, the screening performance for both automated EEG-based brainwave analysis and DOS was determined. Results: A total of 388 patients were included (189 per arm). There were no differences between groups in terms of median hospital length of stay (DOS 7 days (IQR 5.75–9) vs. EEG-based brainwave analysis 7 days (IQR 5–9); p = 0.867). The performance of EEG-based brainwave analysis was considerably lower than that of the DOSS in terms of discrimination between patients with and without postoperative delirium. Conclusions: Screening for postoperative delirium in geriatric hip fracture patients using automated EEG-based brainwave analysis did not result in a shorter length of stay. Additionally, the results of this study show no clear advantage in terms of the screening performance of EEG-based brainwave analysis over the current standard of care for geriatric patients with a hip fracture. [ABSTRACT FROM AUTHOR]

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 () 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. [ABSTRACT FROM AUTHOR]

The detection and identification of epileptic seizures attracted considerable relevance for the neurophysiologists. In order to accomplish the detection of epileptic seizures or equivalently ictal EEG states, this paper proposes the use of nonlinear and chaos features not computed over the raw EEG signals as it was commonly experienced, but instead over intrinsic mode functions (IMFs) extracted subsequently to the application of newly time-frequency signal decomposition methods on the basis of empirical mode decomposition (EMD) and variational mode decomposition (VMD) methods. The first step within the proposed methodology is to excerpt the various components of the IMFs by EMD and VMD decomposition methods on time EEG segments. The Hjorth parameters, the Hurst exponent, the Recurrence Quantification Analysis (RQA), the detrended fluctuation analysis (DFA), the Largest Lyapunov Exponent (LLE), The Higuchi and Katz fractal dimensions (HFD and KFD), seven nonlinear and chaos features computed over the IMFs were investigated and their classification performances evaluated using the k-nearest neighbor (KNN) and the multilayer perceptron neural network (MLPNN) classifiers. Furthermore, the combination of the best nonlinear features has also been examined in terms of sensitivity, specificity and overall classification accuracy. The publicly available Bonn EEG dataset has been has been employed to validate the efficiency of the proposed method for detecting ictal EEG signals from normal or interictal EEG segments. Among the several experiments involved in the current study, the ultimate results establish that the overall classification accuracy can achieve 100%, 99.45%, 99.8%, 99.8%, 98.6% and 99.1% for six different epileptic seizure detection case problems studied, confirming the ability of the proposed methodology in helping the clinic practitioners in the epilepsy detection care units to classify seizure events with a great confidence. [ABSTRACT FROM AUTHOR]

This paper introduces a neural network‐based model designed for classifying emotional states by leveraging multimodal physiological signals. The model utilizes data from the AMIGOS and SEED‐V databases. The AMIGOS database integrates inputs from electroencephalogram (EEG), electrocardiogram (ECG), and galvanic skin response (GSR) to analyze emotional responses, while the SEED‐V database continuously updates EEG signals. We implemented a sequential neural network architecture featuring two hidden layers, which underwent substantial hyperparameter tuning to achieve optimal performance. Our model's effectiveness was tested through binary classification tasks focusing on arousal and valence, as well as a more complex four‐class classification that delineates emotional quadrants for the emotional tags: happy, sad, neutral, and disgust. In these varied scenarios, the model consistently demonstrated accuracy levels ranging from 79% to 86% in the AMIGOS database and up to 97% in SEED‐V. A notable aspect of our approach is the model's ability to accurately recognize emotions without the need for extensive signal preprocessing, a common challenge in multimodal emotion analysis. This feature enhances the practical applicability of our model in real‐world scenarios where rapid and efficient emotion recognition is essential. [ABSTRACT FROM AUTHOR]

Epilepsy is referred to as a neurological disorder, which is detected via examination and manual comprehension of Electroencephalogram (EEG) signals. In deep learning schemes, various enhancements have emerged to efficiently address complex issues by end-to-end learning. The major objective of this research is to propose a new seizure detection approach from EEG signals using a deep learning-based classification technique. The pre-processing is the initial stage, where denoising is performed using a Short-Time Fourier Transform (STFT). Subsequently, the statistical features, time-domain features and spectral features are extracted from the pre-processed signal. Finally, an efficient optimization approach, named Adadelta-Chameleon Swarm Algorithm (Adadelta-CSA), is proposed and employed to train Deep Neural Network (DNN) to carry out the precise seizure prediction. Here, the integration of the Adadelta concept in the Chameleon Swarm Algorithm (CSA) has resulted in Adadelta-CSA. At last, the performance of the Adadelta-CSA scheme-based DNN is compared with the existing techniques by considering accuracy, sensitivity and specificity, and it is found to produce better values of 0.951, 0.966, and 0.935, respectively. [ABSTRACT FROM AUTHOR]

[Display omitted] • N400 and P600 ERPs evoked in adolescents ignoring sentences with semantic errors. • Semantic processing is fundamentally different in passive versus active contexts. • We provide 22 control adolescent high-density electrophysiological BIDs datasets. Language comprehension requires semantic processing of individual words and their context within a sentence. Well-characterized event-related potential (ERP) components (the N400 and late positivity component (LPC/P600)) provide neuromarkers of semantic processing, and are robustly evoked when semantic errors are introduced into sentences. These measures are useful for evaluating semantic processing in clinical populations, but it is not known whether they can be evoked in more severe neurodevelopmental disorders where explicit attention to the sentence inputs cannot be objectively assessed (i.e., when sentences are passively listened to). We evaluated whether N400 and LPC/P600 could be detected in adolescents who were explicitly ignoring sentence inputs. Specifically, it was asked whether explicit attention to spoken inputs was required for semantic processing, or if a degree of automatic processing occurs when the focus of attention is directed elsewhere? High-density ERPs were acquired from twenty-two adolescents (12–17 years), under two experimental conditions: 1. individuals actively determined whether the final word in a sentence was congruent or incongruent with sentence context, or 2. passively listened to background sentences while watching a video. When sentences were ignored, N400 and LPC/P600 were robustly evoked to semantic errors, albeit with reduced amplitudes and protracted/delayed latencies. Statistically distinct topographic distributions during passive versus active paradigms pointed to distinct generator configurations for semantic processing as a function of attention. Covert semantic processing continues in neurotypical adolescents when explicit attention is withdrawn from sentence inputs. As such, this approach could be used to objectively investigate semantic processing in populations with communication deficits. [ABSTRACT FROM AUTHOR]

This research investigates the use of a 1D Convolutional Neural Network (CNN) to classify electroencephalography (EEG) signals into four categories of ischemia severity: normal, mild, moderate, and severe. The model's accuracy was lower in moderate instances (75%) and severe cases (65%) compared to normal cases (95%) and mild cases (85%). The preprocessing pipeline now incorporates Power Spectral Density (PSD) analysis, and segment lengths of 32, 64, and 128 s are thoroughly examined. The work highlights the potential of the model to identify ischemia in real time during carotid endarterectomy (CEA) to prevent perioperative stroke. The 1D-CNN effectively captures both temporal and spatial EEG signals, providing a combination of processing efficiency and accuracy when compared to existing approaches. In order to enhance the identification of moderate and severe instances of ischemia, future studies should prioritize the integration of more complex datasets, specifically for severe ischemia, as well as increasing the current dataset. Our contributions in this study are implementing a novel 1D-CNN model to achieve a classification accuracy of over 93%, improving feature extraction by utilizing Power Spectral Density (PSD), automating the ischemia detection procedure, and enhancing model performance using a well-balanced dataset. [ABSTRACT FROM AUTHOR]

Emotion classification is a challenge in affective computing, with applications ranging from human–computer interaction to mental health monitoring. In this study, the classification of emotional states using electroencephalography (EEG) data were investigated. Specifically, the efficacy of the combination of various feature selection methods and hyperparameter tuning of machine learning algorithms for accurate and robust emotion recognition was studied. The following feature selection methods were explored: filter (SelectKBest with analysis of variance (ANOVA) F-test), embedded (least absolute shrinkage and selection operator (LASSO) tuned using Bayesian optimization (BO)), and wrapper (genetic algorithm (GA)) methods. We also executed hyperparameter tuning of machine learning algorithms using BO. The performance of each method was assessed. Two different EEG datasets, EEG Emotion and DEAP Dataset, containing 2548 and 160 features, respectively, were evaluated using random forest (RF), logistic regression, XGBoost, and support vector machine (SVM). For both datasets, the experimented three feature selection methods consistently improved the accuracy of the models. For EEG Emotion dataset, RF with LASSO achieved the best result among all the experimented methods increasing the accuracy from 98.78% to 99.39%. In the DEAP dataset experiment, XGBoost with GA showed the best result, increasing the accuracy by 1.59% and 2.84% for valence and arousal. We also show that these results are superior to those by the previous other methods in the literature. [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]

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]

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]

Summary The transition from non‐rapid eye movement (NREM) to rapid eye movement (REM) sleep is considered a transitional or intermediate stage (IS), characterised by high amplitude spindles in the frontal cortex and theta activity in the occipital cortex. Early reports in rats showed an IS lasting from 1 to 5 s, but recent studies suggested a longer duration of this stage of up to 20 s. To further characterise the IS, we analysed its spectral characteristics on electrocorticogram (ECoG) recordings of the olfactory bulb (OB), primary motor (M1), primary somatosensory (S1), and secondary visual cortex (V2) in 12 Wistar male adult rats. By comparing the IS with consolidated NREM/REM epochs, our results reveal that the IS has specific power spectral patterns that fall out of the NREM and REM sleep state power distribution. Specifically, the main findings were that sigma (11–16 Hz) power in OB, M1, S1, and V2 increased during the IS compared with NREM and REM sleep, which started first in the frontal part of the brain (OB −54 s, M1 −53 s) prior to the last spindle occurrence. The beta band (17–30 Hz) power showed a similar pattern to that of the sigma band, starting −54 s before the last spindle occurrence in the M1 cortex. Notably, sigma infraslow coupling (~0.02 Hz) increased during the IS but occurred at a slower frequency (~0.01 Hz) compared with NREM sleep. Thus, we argue that the NREM to REM transition contains its own local spectral profile, in accordance with previous reports, and is more extended than described previously. [ABSTRACT FROM AUTHOR]

Key event-related potentials (ERPs) of perceptual decision-making such as centroparietal positivity (CPP) elucidate how evidence is accumulated toward a given choice. Furthermore, this accumulation can be impacted by visual target selection signals such as the N2 contralateral (N2c). How these underlying neural mechanisms of perceptual decision-making are influenced by the spatial congruence of distractors relative to target stimuli remains unclear. Here, we used electroencephalography (EEG) in humans of both sexes to investigate the effect of distractor spatial congruency (same vs different hemifield relative to targets) on perceptual decision-making.We confirmed that responses for perceptual decisions were slower for spatially incongruent versus congruent distractors of high salience. Similarly, markers of target selection (N2c peak amplitude) and evidence accumulation (CPP slope) were found to be lower when distractors were spatially incongruent versus congruent. To evaluate the effects of congruency further, we applied drift diffusion modeling to participant responses, which showed that larger amplitudes of both ERPs were correlated with shorter nondecision times when considering the effect of congruency. The modeling also suggested that congruency's effect on behavior occurred prior to and during evidence accumulation when considering the effects of the N2c peak and CPP slope. These findings point to spatially incongruent distractors, relative to congruent distractors, influencing decisions as early as the initial sensory processing phase and then continuing to exert an effect as evidence is accumulated throughout the decision-making process. Overall, our findings highlight how key electrophysiological signals of perceptual decision-making are influenced by the spatial congruence of target and distractor. [ABSTRACT FROM AUTHOR]

The subcortical visual pathway to the amygdala has long been considered a rapid and crude stream for processing emotionally salient information that is reliant on low spatial frequency (LSF) information. Recently, research has called this LSF dependency into question. To resolve this debate, we take advantage of an anatomical hemiretinal asymmetry, whereby the nasal hemiretina sends a higher proportion of information through the subcortical pathway than the temporal hemiretina. We recorded brain activity using electroencephalography (EEG) in human participants (N = 40) while they completed a monocular viewing paradigm. Pairs of faces (one fearful and one neutral, or both neutral) were projected simultaneously to the nasal and temporal hemiretina in three contrast-equated blocks; faces filtered to display only (i) LSF, (ii) high spatial frequency (HSF), or (iii) unfiltered information (broadband spatial frequency; BSF). BSF fearful faces were found to produce a greater naso-temporal asymmetry, with greater N170 amplitudes evoked by BSF faces in the nasal field, compared to HSF faces. Conversely, the naso-temporal asymmetry for LSF fearful faces did not differ between BSF and HSF. Collectively, these findings provide crucial evidence that the subcortical pathway carries combined spatial frequency visual signals, with a potential bias against HSF content. [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]

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]

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 FROM AUTHOR]

Learning never stops. As we navigate life, we continuously acquire and update knowledge to optimize memory-guided action, with a gradual shift from the former to the latter as we master our environment. How are these learning dynamics expressed in the brain and in behavioral patterns? Here, we devised a spatiotemporal image learning task ("Memory Arena") in which participants learn a set of 50 items to criterion across repeated exposure blocks. Critically, brief task-free periods between successive image presentations allowed us to assess multivariate electroencephalogram (EEG) patterns representing the previous and/or upcoming image identity, as well as anticipatory eye movements toward the upcoming image location. As expected, participants eventually met the performance criterion, albeit with different learning rates. During task-free periods, we were able to readily decode representations of both previous and upcoming image identities. Importantly though, decoding strength followed opposing slopes for previous vs. upcoming images across time, with a gradual decline of evidence for the previous image and a gradual increase of evidence for the upcoming image. Moreover, the ratio of upcoming vs. previous image evidence directly followed behavioral learning rates. Finally, eye movement data revealed that participants increasingly used the task-free period to anticipate upcoming image locations, with target-precision slopes paralleling both behavioral performance measures as well as EEG decodability of the upcoming image across time. Together, these results unveil the neural and behavioral dynamics underlying the gradual transition from learning to memory-guided action. [Display omitted] • Adaptive behavior requires a gradual switch from learning to memory-guided action • Brain activity patterns shift from encoding past stimuli to retrieving future stimuli • Eye movements indicate increasing anticipation of upcoming stimulus locations • Brain pattern and eye movement dynamics correlate with learning rates This study reveals how the brain dynamically shifts from learning to memory-guided behavior. Büchel et al. use electroencephalography (EEG) and eye tracking in a spatiotemporal learning task to show a gradual transition from encoding past stimuli to anticipating future ones, aligning neural and behavioral measures with learning rates. [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]

Background: Acquired brain injury (ABI) leads to cognitive deficiencies, alteration of brain activity associated with an increase in slow‐wave (delta and theta bands) power, and reduced fast‐wave (alpha, beta, and gamma bands) power. To compensate for the cognitive deficits that impact autonomy and quality of life, patients in a chronic phase can benefit from cognitive intervention. Objective: This study explores the effects of cognitive intervention on brain activity, measured by electroencephalography (EEG), and on executive functioning, assessed by the Test of Attentional Performance (TAP) battery. Method: We provided an ecological rehabilitation intervention, simulating real‐life tasks adapted for patients with chronic cognitive disorders. A single‐case experimental design (SCED) assessed patients' performance in terms of correct responses percentage (CRs) and reaction times (RTs), and EEG spectral powers before and 1 month after the intervention. The TAP tasks included working memory (WM), divided attention (DA), inhibition (GO), and flexibility (FL). EEG frequency powers were also measured during resting states. Results: One month after the intervention, significant improvements were observed in CRs and RTs for the FL task. Increases in all frequency band powers occurred during FL, WM, and DA tasks, except for alpha bands in DA. In the GO task, delta and gamma power also increased after the intervention. No significant changes were found during resting‐state EEG. The results of this open study, without a control group, are preliminary. Conclusion: The effects of the therapy are mostly reflected by changes in mental FL performance and altered EEG patterns during cognitive tasks, particularly in slow and fast‐frequency bands. We argue that cognitive intervention could amplify the compensatory mechanisms following brain damage and/or ease restoration mechanisms in the fast‐frequency activity bands. Further SCEDs or studies with control groups are needed to confirm these findings and the role of EEG biomarkers in rehabilitation. [ABSTRACT FROM AUTHOR]

Visual symmetry activates the extrastriate cortex. This symmetry activation generates an Event Related Potential (ERP) named the Sustained Posterior Negativity (SPN). The SPN is larger when participants engage in regularity discrimination tasks than luminance discrimination tasks. Two recent studies suggest that the type of task matters more when the stimulus is presented briefly. We tested this claim with a new sample of 52 participants in a within-subjects design. As predicted, the SPNs were larger in Regularity tasks than Luminance tasks. However, contrary to predictions, the effect of the task was very similar in long and short presentation duration blocks. It remains unclear what factors modulate the task effect. However, presentation duration is not as important as previous results suggest. [ABSTRACT FROM AUTHOR]

Neuroimaging is revolutionizing our ability to investigate the brain's structural and functional properties, enabling us to visualize brain activity during diverse mental processes and actions. One of the most widely used neuroimaging techniques is electroencephalography (EEG), which records electrical activity from the brain using electrodes positioned on the scalp. EEG signals capture both spatial (brain region) and temporal (time-based) data. While a high temporal resolution is achievable with EEG, spatial resolution is comparatively limited. Consequently, capturing both spatial and temporal information from EEG data to recognize mental activities remains challenging. In this paper, we represent spatial and temporal information obtained from EEG signals by transforming EEG data into sequential topographic brain maps. We then apply hybrid deep learning models to capture the spatiotemporal features of the EEG topographic images and classify imagined English words. The hybrid framework utilizes a sequential combination of three-dimensional convolutional neural networks (3DCNNs) and recurrent neural networks (RNNs). The experimental results reveal the effectiveness of the proposed approach, achieving an average accuracy of 77.8% in identifying imagined English speech. [ABSTRACT FROM AUTHOR]