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This article explores the use of convolutional neural networks (CNNs) in the detection and diagnosis of Attention Deficit Hyperactivity Disorder (ADHD). CNNs, which are adept at image recognition, can analyze brain imaging data to automatically identify relevant brain structures and abnormalities associated with ADHD. The integration of deep learning technology into ADHD diagnosis represents a significant advancement, allowing for more objective and data-driven approaches. The article also highlights specific studies that demonstrate the effectiveness of CNNs in diagnosing ADHD using EEG signals and MRI scans. The authors suggest that future research should focus on integrating different types of data and developing personalized treatment plans while considering ethical considerations and explainable AI models. Overall, CNNs are seen as a promising tool in improving ADHD care and patient outcomes. [Extracted from the article]

Pattern recognition based on network connections has recently been applied to the brain-computer interface (BCI) research, offering new ideas for emotion recognition using Electroencephalogram (EEG) signal. However unified standards are currently lacking for selecting emotional signals in emotion recognition research, and potential associations between activation differences in brain regions and network connectivity pattern are often being overlooked. To bridge this technical gap, a data-driven signal auto-segmentation and feature fusion algorithm (DASF) is proposed in this paper. First, the Phase Locking Value (PLV) method was used to construct the brain functional adjacency matrix of each subject, and the dynamic brain functional network across subjects was then constructed. Next, tucker decomposition was performed and the Grassmann distance of the connectivity submatrix was calculated. Subsequently, different brain network states were distinguished and signal segments under emotional states were automatically extract using data-driven methods. Then, tensor sparse representation was adopted on the intercepted EEG signals to effectively extract functional connections under different emotional states. Finally, power-distribution related features (differential entropy and energy feature) and brain functional connection features were effectively combined for classification using the support vector machines (SVM) classifier. The proposed method was validated on ERN and DEAP datasets. The single-feature emotion classification accuracy of 86.57 % and 87.74 % were achieved on valence and arousal dimensions, respectively. The accuracy of the proposed feature fusion method was achieved at 89.14 % and 89.65 %, accordingly, demonstrating an improvement in emotion recognition accuracy. The results demonstrated the superior classification performance of the proposed data-driven signal auto-segmentation and feature fusion algorithm in emotion recognition compared to state-of-the-art classification methods. • A data-driven approach to capturing signals when emotions are triggered. Network parameters for different subjects were obtained through PLV calculations, forming a tensor. Tucker decomposition was then used to detect the brain network state of EEG signals, automatically identifying EEG segments during emotional activation. This method effectively screens signals in the state of emotional arousal for improved emotion recognition. • Using tensor to extract brain functional connectivity patterns under emotional states. Through tensor sparse representation, the functional brain connectivity network can be obtained to represent the emotional excitation state, and then the network connection features can be extracted. • Fusion features offer richer compensatory information for emotion recognition by combining activation difference features with connectivity features. [ABSTRACT FROM AUTHOR]

AbstractDriving with anger can result in hazardous driving behaviors, often arising from inadequate communication among Traffic Participants (TPs). Currently, external Human-Machine Interface (eHMI) technology provides a novel channel for enhancing communication among TPs, yet its role in regulating driving emotions remains uncertain. This study investigates the impact of eHMI technology on the regulation of driving anger. We simulated a driving scenario in which the preceding vehicle abruptly applied brakes to elicit anger from the following driver. Three eHMI formats (Symbol, Text, and Symbol + Text) positioned at the rear of the preceding vehicle were implemented to explain the sudden braking event and regulate drivers’ emotions. Objective electroencephalogram (EEG) signals and subjective assessment data were analyzed to assess the effects of eHMIs on driving anger regulation. The results indicate that driving with eHMIs exhibited significant regulatory quality of drivers’ anger. Among the three formats, Symbol + Text eHMI exerted a notable regulatory impact, while both Symbol and Text eHMI contributed to emotional regulation. This study illustrates the positive impact of eHMI on emotion regulation in traffic environments, providing valuable backing for traffic management authorities in developing guidelines and policies, and contributing to the standardization of future eHMI technologies. [ABSTRACT FROM AUTHOR]

EEG-based classification from human states is still challenging in human-computer interaction (HCI). Since it reflects brain activity directly, electroencephalography (EEG) has significant advantages in emotion classification research. This study recorded EEG signals from 12 participants while they perceived emotional audio-visual stimulation movie clips for four minutes. The six emotions studied were anger, excitement, fear, happiness, sadness, and a neutral state. We also perform raw data preprocessing to obtain clean data, extracting the power spectrum using Fast Fourier Transform (FFT) to generate feature vectors. In addition, we conduct extensive experiments to validate the classification of human states using subject-independent machine-learning techniques. As a result, the LSTM network achieved the highest classification accuracy of 81.46% for six emotional states, while the SVM network achieved only 68.64%. In addition, we achieved 82.89% accuracy in the Bi-LSTM network with two layers when applying the deep learning methods to different layers. In conclusion, extensive experiments were conducted on our collected dataset. Experimental results indicate that the LSTM network, a time-sequence-related model, has superior classification results of 82.89% than other methods. It also shows that the long duration of EEG signals is crucial for detecting the emotional state changes of various subject types, including individual subjects and cross-subjects. In future research, we will investigate multiple evaluation experiments utilizing deep learning models or propose novel EEG-based emotion classification features to improve the accuracy of emotion classification. [ABSTRACT FROM AUTHOR]

EEG-based emotion classification is considered to separate and observe the mental state or emotions. Emotion classification using EEG is used for medical, security and other purposes. Several deep learning and machine learning strategies are employed to classify the EEG emotion signals. They do not provide sufficient accuracy and have higher complexity and high error rate. In this manuscript, a novel Reinforced Spatio-Temporal Attentive Graph Neural Networks (RSTAGNN) and ContextNet for emotion classification with EEG signals is proposed (RSTAGNN-ContextNet-GWOA-EEG-EA). Here, the input EEG signals are taken from two benchmark datasets,namely DEAP and K-EmoCon datasets. Then, the input EEG signals are pre-processed,and the features are extracted utilizing ContextNet with Global Principal Component Analysis (GPCA). After that, the EEG signal emotions are classified using Reinforced Spatio-Temporal Attentive Graph Neural Networks method. RSTAGNN weight parameters are optimized under the Glowworm Swarm Optimization Algorithm (GWOA). The proposed model classifies the EEG signal emotions with high accuracy. The efficacy of the proposed method using the DEAP dataset attains higher accuracy by 24.05%, 12.64% related to existing systems, like Multi-domain feature fusion for emotion classification (DWT-SVM-EEGEA- DEAP), EEG emotion finding utilizing fusion mode of graph CNN with LSTM (GCNN-LSTM-EEG-EA-DEAP) respectively. The efficiency of the proposed method using the K-EmoCon dataset attains higher accuracy 32.64%, 15.65% related to existing systems, like Toward Robust Wearable Emotion Realization along Contrastive Representation Learning (CAT-EEG-EA-K-EmoCon) and Human Emotion Recognition using Physiological Signals (CATEEG- EA-K-EmoCon) respectively. [ABSTRACT FROM AUTHOR]

Electroencephalogram (EEG) signals are derived from the central nervous system and inherently difficult to camouflage, leading to the recent popularity of EEG-based emotion recognition. However, due to the non-stationary nature of EEG, inter-subject variabilities become obstacles for recognition models to well adapt to different subjects. In this paper, we propose a novel approach called semi-supervised bipartite graph construction with active EEG sample selection (SBGASS) for cross-subject emotion recognition, which offers two significant advantages. Firstly, SBGASS adaptively learns a bipartite graph to characterize the underlying relationships between labeled and unlabeled EEG samples, effectively implementing the semantic connection for samples from different subjects. Secondly, we employ active sample selection technique in this paper to reduce the impact of negative samples (outliers or noise in the data) on bipartite graph construction. Drawing from the experimental results with the SEED-IV data set, we have gained the following three insights. (1) SBGASS actively rejects negative labeled samples, which helps mitigate the impact of negative samples when constructing the optimal bipartite graph and improves the model performance. (2) Through the learned optimal bipartite graph in SBGASS, the transferability of labeled EEG samples is quantitatively analyzed, which exhibits a decreasing tendency as the distance between each labeled sample and the corresponding class centroid increases. (3) Besides the improved recognition accuracy, the spatial-frequency patterns in emotion recognition are investigated by the acquired projection matrix. [ABSTRACT FROM AUTHOR]

Depressive states in both healthy individuals and those with major depressive disorder exhibit differences primarily in symptom severity rather than symptom type, suggesting that there is a spectrum of depressive symptoms. The increasing prevalence of mild depression carries lifelong implications, emphasizing its clinical and social significance, which parallels that of moderate depression. Early intervention and psychotherapy have shown effective outcomes in subthreshold depression. Electroencephalography serves as a non-invasive, powerful tool in depression research, with many studies employing it to discover biomarkers and explore underlying mechanisms for the identification and diagnosis of depression. However, the efficacy of these biomarkers in distinguishing various depressive states in healthy individuals and in understanding the associated mechanisms remains uncertain. In our study, we examined the power spectrum density and the region-based phase-locking value in healthy individuals with various depressive states during their resting state. We found significant differences in neural activity, even among healthy individuals. Participants were categorized into high, middle, and low depressive state groups based on their response to a questionnaire, and eyes-open resting-state electroencephalography was conducted. We observed significant differences among the different depressive state groups in theta- and betaband power, as well as correlations in the theta–beta ratio in the frontal lobe and phase-locking connections in the frontal, parietal, and temporal lobes. Standardized low-resolution electromagnetic tomography analysis for source localization comparing the differences in resting-state networks among the three depressive state groups showed significant differences in the frontal and temporal lobes. We anticipate that our study will contribute to the development of effective biomarkers for the early detection and prevention of depression. [ABSTRACT FROM AUTHOR]

This study aimed to perform a comparative study of the functional connectivity of different frequency bands for the identification of resting and arithmetic cognitive workload EEG using machine learning techniques. Functional connectivity was calculated from preprocessed EEGs for both rest and task states in 5 EEG sub-bands: alpha (8–13 Hz), theta (4–8 Hz), delta (1–4 Hz), gamma (30–45 Hz), and beta (13–30 Hz). This was done through Weighted Phase Lag Index (WPLI). After that, PCA was applied to the calculated feature vectors to decrease the dimensionality of the feature space. Eventually, the normalized chosen features were used as input for different machine learning-based classification models, and the performance was assessed through the leave-one-subject cross-validation (LOSOCV) algorithm. Experimental results showed that the classification results on the basis of the connectivity features of delta, theta, alpha, beta, and gamma frequency bands were 90.27%, 77.78%, 62.50%, 62.50%, and 76.39%, respectively. The obtained results showed that used machine learning models and functional connectivity technique are successfully applied to detect mental workload from rest- and task-EEG. In summary, EEG functional connectivity in the delta frequency is a potent tool for comprehending the neural basis of mental workload and has significant applications in various fields. [ABSTRACT FROM AUTHOR]

Auditory binaural beats (BBs) stimulation is a method used to support the assumption that artificial external stimuli can generate specific brain waves. This study aimed to examine the effects of BBs that induce brain waves only based on a different frequency using an inaudible baseline frequency. The experiment was conducted among 18 adults in their 20s (11 male and 7 female). A baseline frequency of 18,000Hz and BBs with a difference frequency of 5Hz was presented, and the changes in electroencephalogram were observed. Power spectrum analysis was performed, and the absolute power of the theta-band frequency range (4–7Hz) was averaged. Absolute theta power was compared between phases without binaural beats (non-BBs) and with binaural beats (θ-BBs) using the Wilcoxon signed-rank test. Theta waves in θ-BBs significantly increased in most brain regions (central, parietal, temporal, and occipital) compared to those in non-BBs. This study is significant because it focused solely on the effects of the BBs-inducing theta waves using a frequency difference, excluding the effect of sound perception. [ABSTRACT FROM AUTHOR]

Protein phosphatase 2 regulatory subunit B56δ related neurodevelopmental disorder (PPP2R5D‐related NDD) is largely caused by de novo heterozygous missense PPP2R5D variants. We report medical characteristics, longitudinal adaptive functioning, and in‐person neurological, motor, cognitive, and electroencephalogram (EEG) activity for PPP2R5D‐related NDD. Forty‐two individuals (median age 6 years, range = 0.8–25.3) with pathogenic/likely pathogenic PPP2R5D variants were assessed, and almost all variants were missense (97.6%) and de novo (85.7%). Common clinical symptoms were developmental delay, hypotonia, macrocephaly, seizures, autism, behavioral challenges, and sleep problems. The mean Gross motor functional measure‐66 was 60.2 ± 17.3% and the mean Revised upper limb module score was 25.9 ± 8.8. The Vineland‐3 adaptive behavior composite score (VABS‐3 ABC) at baseline was low (M = 61.7 ± 16.8). VABS‐3 growth scale value scores increased from baseline in all subdomains (range = 0.6–5.9) after a mean follow‐up of 1.3 ± 0.3 years. EEG beta and gamma power were negatively correlated with VABS‐3 score; p < 0.05. Individuals had a mean Quality‐of‐life inventory‐disability score of 74.7 ± 11.4. Twenty caregivers (80%) had a risk of burnout based on the Caregiver burden inventory. Overall, the most common clinical manifestations of PPP2R5D‐related NDD were impaired cognitive, adaptive function, and motor skills; and EEG activity was associated with adaptive functioning. This clinical characterization describes the natural history in preparation for clinical trials. [ABSTRACT FROM AUTHOR]

The assessment of the cognitive workload experienced by air traffic controllers is a complex and prominent issue in the research community. This study introduces new indicators related to gamma waves to detect controllers' workload and develops experimental protocols to capture their EEG data and NASA-TXL data. Then, statistical tests, including the Shapiro–Wilk test and ANOVA, were used to verify whether there was a significant difference between the workload data of the controllers in different scenarios. Furthermore, the Support Vector Machine (SVM) classifier was employed to assess the detection accuracy of these indicators across four categorizations. According to the outcomes, hypotheses suggesting a strong correlation between gamma waves and an air traffic controller's workload were put forward and subsequently verified; meanwhile, compared with traditional indicators, the indicators associated with gamma waves proposed in this paper have higher accuracy. In addition, to explore the applicability of the indicator, sensitive channels were selected based on the mRMR algorithm for the indicator with the highest accuracy, β + θ + α + γ, showcasing a recognition rate of a single channel exceeding 95% of the full channel, which meets the requirements of convenience and accuracy in practical applications. In conclusion, this study demonstrates that utilizing EEG gamma wave-associated indicators can offer valuable insights into analyzing workload levels among air traffic controllers. [ABSTRACT FROM AUTHOR]

About 70 million people globally have been diagnosed with epilepsy. Electroencephalogram (EEG) devices are the primary method for identifying and monitoring seizures. The use of EEG expands the preclinical research involving the long-term recording of neuro-activities in rodent models of epilepsy targeted towards the efficient testing of prospective antiseizure medications. Typically, trained epileptologists visually analyse long-term EEG recordings, which is time-consuming and subject to expert variability. Automated epileptiform discharge detection using machine learning or deep learning methods is an effective approach to tackling these challenges. This systematic review examined and summarised the last 30 years of research on detecting epileptiform discharge in rodent models of epilepsy using machine learning and deep learning methods. A comprehensive literature search was conducted on two databases, PubMed and Google Scholar. Following the PRISMA protocol, the 3021 retrieved articles were filtered to 21 based on inclusion and exclusion criteria. An additional article was obtained through the reference list. Hence, 22 articles were selected for critical analysis in this review. These articles revealed the seizure type, features and feature engineering, machine learning and deep learning methods, training methodologies, evaluation metrics so far explored, and models deployed for real-world validation. Although these studies have advanced the field of epilepsy research, the majority of the models are experimental. Further studies are required to fill in the identified gaps and expedite preclinical research in epilepsy, ultimately leading to translational research. [ABSTRACT FROM AUTHOR]

Herein, we investigate the effects of 3D printed electrodes on electrophysiological signals and identify the important design elements required for manufacturing better electrodes for high body adhesion for smart wear. Ten electrodes of different shapes (plain, check, stripe, circular, radial cut-out) and thicknesses (0.5 mm and 1.0 mm) were manufactured. The electrodes were evaluated by testing on 20 healthy individuals (10 men and 10 women). To measure the electroencephalogram (EEG) of the participants, we used BIOS-S8 (BioBrain Inc., Korea), an 8-channel polygraph for multibody signal measurement. Data were analyzed using the SPSS 26.0 statistical program. The EEG values were significantly activated according to gender. For the male participants, relative alpha (RA), relative slow theta (RST), relative mid theta (RMT), and the ratio of SMR-mid beta to theta (RSMT) values were highly activated and for the female participants, RA, relative fast alpha (RFA), and relative slow theta (RSA) values were highly activated. There were no significant gendifferences in the EEG of both genders for the 10 types of electrodes. However, for the female participants, the 'RA' indices showed a significant difference based on electrode shape on the right temporal lobe (T4), but there was no significant difference based on the thickness. There was a significant difference in the subjective preference of the electrodes also. In the subjective evaluation, it was found that the differences based on the shape and thickness of the electrodes were sensitively recognized. [ABSTRACT FROM AUTHOR]

Electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) can objectively reflect a person's emotional state and have been widely studied in emotion recognition. However, the effective feature fusion and discriminative feature learning from EEG–fNIRS data is challenging. In order to improve the accuracy of emotion recognition, a graph convolution and capsule attention network model (GCN-CA-CapsNet) is proposed. Firstly, EEG–fNIRS signals are collected from 50 subjects induced by emotional video clips. And then, the features of the EEG and fNIRS are extracted; the EEG–fNIRS features are fused to generate higher-quality primary capsules by graph convolution with the Pearson correlation adjacency matrix. Finally, the capsule attention module is introduced to assign different weights to the primary capsules, and higher-quality primary capsules are selected to generate better classification capsules in the dynamic routing mechanism. We validate the efficacy of the proposed method on our emotional EEG–fNIRS dataset with an ablation study. Extensive experiments demonstrate that the proposed GCN-CA-CapsNet method achieves a more satisfactory performance against the state-of-the-art methods, and the average accuracy can increase by 3–11%. [ABSTRACT FROM AUTHOR]

We present a new approach to classifying the sleep stage that incorporates a computationally inexpensive method based on permutations for channel selection and takes advantage of deep learning power, specifically the gated recurrent unit (GRU) model, along with other deep learning methods. By systematically permuting the electroencephalographic (EEG) channels, different combinations of EEG channels are evaluated to identify the most informative subset for the classification of the 5-class sleep stage. For analysis, we used an EEG dataset that was collected at the International Institute for Integrative Sleep Medicine (WPI-IIIS) at the University of Tsukuba in Japan. The results of these explorations provide many new insights such as the (1) drastic decrease in performance when channels are fewer than 3, (2) 3-random channels selected by permutation provide the same or better prediction than the 3 channels recommended by the American Academy of Sleep Medicine (AASM), (3) N1 class suffers the most in prediction accuracy as the channels drop from 128 to 3 random or 3 AASM, and (4) no single channel provides acceptable levels of accuracy in the prediction of 5 classes. The results obtained show the GRU's ability to retain essential temporal information from EEG data, which allows capturing the underlying patterns associated with each sleep stage effectively. Using permutation-based channel selection, we enhance or at least maintain as high model efficiency as when using high-density EEG, incorporating only the most informative EEG channels. [ABSTRACT FROM AUTHOR]

Convolutional Neural Networks (CNNs) have become increasingly popular in seizure detection and prediction research. While traditional CNNs are effective in image classification tasks, applying them to seizure signal analysis requires specific architectures. In this study, we propose a Signal-Based One-Dimensional Convolutional Neural Network (SB 1D CNN) model that is customized for seizure signals. The SB 1D CNN model replaces traditional ReLU and Pooling layers with counterparts that are better suited to negative signal fluctuations and adjusts the training procedure accordingly. Additionally, the model introduces time/frequency-sensitive kernels in the initial convolution layer to capture significant features across time and frequency domains. To evaluate the proposed SB 1D CNN model, we conducted experiments using epileptic EEG signals from the CHB-MIT database. We carried out two sets of experiments: the first to identify optimal EEG channels through single-channel evaluations, and the second to train a robust SB 1D CNN model for seizure prediction. Comparative analysis with a traditional 1D CNN with a similar structure revealed that the SB 1D CNN model excels in feature extraction and classification of epileptic EEGs. Notably, training 1D CNNs exclusively with relevant data significantly enhances their performance. Overall, this study highlights the importance of tailored architectures in improving the effectiveness of 1D CNNs in seizure prediction tasks. The proposed SB 1D CNN model offers a promising avenue for enhancing the accuracy and reliability of seizure prediction systems, with potential implications for improving patient care and management in epilepsy. [ABSTRACT FROM AUTHOR]

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Epilepsy is a serious neurological disorder that results in seizures. It can be diagnosed by analyzing the brain's electrical activity using an electroencephalogram (EEG). However, the detection of seizures from massive EEG datasets is a challenging task. To address this challenge, researchers have developed several machine‐learning classifiers and feature extraction techniques for detecting seizures. This paper proposes an energy‐efficient and fast field programmable gate array (FPGA) architecture for detecting epileptic seizures using minimal computational resources. The seizure detection system uses the one Hjorth parameter (mobility) and another statistical parameter (nonlinear energy) as features and employs two efficient classifiers, quadratic discriminant analysis and linear support vector machine (LSVM), for classifying signals into seizure and nonseizure categories. The feature extractor block is connected individually to each of these classifiers. Subsequently, the performance of these two proposed models is evaluated in terms of accuracy, sensitivity, power consumption, resource utilization, and other metrics. The results demonstrate that the SVM classifier‐based model achieved the highest accuracy (99.4%) and sensitivity (98.8%) while consuming minimal dynamic power (0.057 mW) and utilizing the minimum FPGA resources. Thus, the proposed hardware system offers a reliable and energy‐efficient solution for detecting seizures in clinical and real‐time applications. [ABSTRACT FROM AUTHOR]

Abstract Herein, we investigate the effects of 3D printed electrodes on electrophysiological signals and identify the important design elements required for manufacturing better electrodes for high body adhesion for smart wear. Ten electrodes of different shapes (plain, check, stripe, circular, radial cut-out) and thicknesses (0.5 mm and 1.0 mm) were manufactured. The electrodes were evaluated by testing on 20 healthy individuals (10 men and 10 women). To measure the electroencephalogram (EEG) of the participants, we used BIOS-S8 (BioBrain Inc., Korea), an 8-channel polygraph for multibody signal measurement. Data were analyzed using the SPSS 26.0 statistical program. The EEG values were significantly activated according to gender. For the male participants, relative alpha (RA), relative slow theta (RST), relative mid theta (RMT), and the ratio of SMR-mid beta to theta (RSMT) values were highly activated and for the female participants, RA, relative fast alpha (RFA), and relative slow theta (RSA) values were highly activated. There were no significant gendifferences in the EEG of both genders for the 10 types of electrodes. However, for the female participants, the ‘RA’ indices showed a significant difference based on electrode shape on the right temporal lobe (T4), but there was no significant difference based on the thickness. There was a significant difference in the subjective preference of the electrodes also. In the subjective evaluation, it was found that the differences based on the shape and thickness of the electrodes were sensitively recognized.

Abstract We present a new approach to classifying the sleep stage that incorporates a computationally inexpensive method based on permutations for channel selection and takes advantage of deep learning power, specifically the gated recurrent unit (GRU) model, along with other deep learning methods. By systematically permuting the electroencephalographic (EEG) channels, different combinations of EEG channels are evaluated to identify the most informative subset for the classification of the 5-class sleep stage. For analysis, we used an EEG dataset that was collected at the International Institute for Integrative Sleep Medicine (WPI-IIIS) at the University of Tsukuba in Japan. The results of these explorations provide many new insights such as the (1) drastic decrease in performance when channels are fewer than 3, (2) 3-random channels selected by permutation provide the same or better prediction than the 3 channels recommended by the American Academy of Sleep Medicine (AASM), (3) N1 class suffers the most in prediction accuracy as the channels drop from 128 to 3 random or 3 AASM, and (4) no single channel provides acceptable levels of accuracy in the prediction of 5 classes. The results obtained show the GRU’s ability to retain essential temporal information from EEG data, which allows capturing the underlying patterns associated with each sleep stage effectively. Using permutation-based channel selection, we enhance or at least maintain as high model efficiency as when using high-density EEG, incorporating only the most informative EEG channels.

Tunnel construction is crucial to the advancement of global transportation networks. Despite their importance, there have been few investigations on the safety and comfort of workers. Tunnel workers operate under various adverse conditions that lead to multiple forms of fatigue. Researchers have suggested that there is a specific type of fatigue stemming from insufficient natural or improper artificial lighting that has largely been overlooked in previous studies. To validate the existence of illumination-induced fatigue and explore control measures, this study conducted virtual reality experiments in a simulated tunnel construction setting and collected and analyzed data from scales, electroencephalogram, and eye-tracking. The results indicated significant variations in fatigue levels with changes in illumination, whereas the environment remained constant, confirming the presence of illumination fatigue. Moreover, the analysis of the scale results aligned with the EEG findings, ranking the comfort levels of the mucking illumination as 100>75>150>50>200>30>15>10>0 lx. The modeling of illumination fatigue identified three comfort levels of illumination, I: [75, 100], II: [15, 75) ∪ (100, 200], III: [0, 15], thereby establishing that illumination fatigue can be controlled through improved lighting. Subsequently, to address the issue of uneven illumination distribution at construction sites, key areas for mucking task lighting were identified using eye-tracking technology as the upper and lower edges of the bucket, debris being moved, and expected stacking area. Finally, by comparing and summarizing the divergent conclusions of previous studies, this study proposed various targeted lighting guidelines. This comprehensive study not only enriches the current understanding and experimental design reference for tunnel construction fatigue but also provides practitioners with specific and effective lighting guidance to significantly reduce workers' fatigue and improve their working conditions. Practical Applications: The results of laboratory studies indicate that inadequate or excessive artificial lighting during tunnel construction significantly increases worker fatigue. This research identified an illuminance range of 75–100 lx as the most comfortable range for mucking activities. Occasionally, the construction environment is complex, making it challenging to control the illuminance distribution uniformly. Therefore, this study further examines the attention distribution during the mucking processes, identifying key lighting points as the upper and lower edges of the bucket, debris being moved, and expected stacking area. Furthermore, the results of this study address the discrepancies in previous guidelines on illumination levels and propose different illumination ranges tailored to specific construction areas. Worldwide construction teams can determine the appropriate level of illumination within these ranges by considering the composition of the workforce, environmental factors, and policy requirements. These findings can help enhance worker comfort and improve construction safety, ultimately ensuring productivity and efficiency. [ABSTRACT FROM AUTHOR]

Abstract Affect recognition in a real-world, less constrained environment is the principal prerequisite of the industrial-level usefulness of this technology. Monitoring the psychological profile using smart, wearable electroencephalogram (EEG) sensors during daily activities without external stimuli, such as memory-induced emotions, is a challenging research gap in emotion recognition. This paper proposed a deep learning framework for improved memory-induced emotion recognition leveraging a combination of 1D-CNN and LSTM as feature extractors integrated with an Extreme Learning Machine (ELM) classifier. The proposed deep learning architecture, combined with the EEG preprocessing, such as the removal of the average baseline signal from each sample and extraction of EEG rhythms (delta, theta, alpha, beta, and gamma), aims to capture repetitive and continuous patterns for memory-induced emotion recognition, underexplored with deep learning techniques. This work has analyzed EEG signals using a wearable, ultra-mobile sports cap while recalling autobiographical emotional memories evoked by affect-denoting words, with self-annotation on the scale of valence and arousal. With extensive experimentation using the same dataset, the proposed framework empirically outperforms existing techniques for the emerging area of memory-induced emotion recognition with an accuracy of 65.6%. The EEG rhythms analysis, such as delta, theta, alpha, beta, and gamma, achieved 65.5%, 52.1%, 65.1%, 64.6%, and 65.0% accuracies for classification with four quadrants of valence and arousal. These results underscore the significant advancement achieved by our proposed method for the real-world environment of memory-induced emotion recognition.

As a typical representative of mental disorders, anxiety and depression disorders have occupied a large number of people after the outbreak of the new crown epidemic. However, anxiety and depressive disorders are detected by screening with clinical scales, potentially undermining the efficacy of detecting mental disorders, as it is heavily contingent upon the personal condition of the individual participant and the expertise of diagnosing physician. Therefore, to predict the feasibility of individual-level mental disorders, we developed a multi-bands joint graph convolution network (MBJ-GCN) framework based on electroencephalogram (EEG) brain functional connectivity (FC) networks. The functional connectivity networks are fused with orthogonal power envelopes correlation across five different frequency bands. Furthermore, the five frequency bands of brain FC networks were then integrated using locally weighted clustering coefficients (LWCC), which were then combined into feature vectors to represent the vertexes. After that, we design parallel GCN layers with different inputs via random embeddings, which can recognize intrinsic mental disorder maps from the embeddings in GCNs. Finally, we concatenate the outputs of all GCN layers in the fully connected layer for prediction. The experimental results have shown that our proposed method has low mean absolute error (MAE) and high Pearson correlation coefficient (PCC) performance compared with other prediction methods. It is possible to represent both individual characteristics and clinical information of potential patients using the proposed method. In conclusion, we extend traditional scale interviews to FC-based individual predictions, thereby taking a step towards the development of applicable techniques for quantitative real-world monitoring of mental disorders.

Abstract Background Parkinson’s disease (PD) is a progressive neurodegenerative disease that usually happens to elderly people, with a wide range of motor and dementia symptoms. An objective and convenient biomarker for PD detection is extremely valuable, especially one that could be acquired non-invasively and low-costly. To this end, this study used resting-state scalp electroencephalography (EEG) signals to explore dynamic functional-connectivity (dFC) states between each pair of EEG recording channels, without source localization. Methods dFC refers to synchronization patterns over time between each pair of EEG channels. First, five frequency bands were extracted from EEG signals with fourth-order Butterworth bandpass filter, including delta (0.5–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), beta (8–30 Hz) and gamma (30–50 Hz). Then, after non-random joint fluctuation was measured with weighted symbolic mutual information (wSMI) algorithm, whole-brain dynamic channelwise dFC states were estimated, and classified with k-means clustering. At last, FC state occurrences were calculated, and ANOVA analyses were performed for each state. Two open-source resting-state EEG data sets ( https://doi.org/10.18112/openneuro.ds002778.v1.0.4 : 32 channels, 16 health controls and 15 PD subjects. https://doi.org/10.18112/openneuro.ds003490.v1.1.0 : 64 channels, 25 health controls and 25 PD subjects) were used to test our methods. Results Significant changes in proportions of various dFC states within beta frequency-band were consistently observed in these both data sets (p value

Affect recognition in a real-world, less constrained environment is the principal prerequisite of the industrial-level usefulness of this technology. Monitoring the psychological profile using smart, wearable electroencephalogram (EEG) sensors during daily activities without external stimuli, such as memory-induced emotions, is a challenging research gap in emotion recognition. This paper proposed a deep learning framework for improved memory-induced emotion recognition leveraging a combination of 1D-CNN and LSTM as feature extractors integrated with an Extreme Learning Machine (ELM) classifier. The proposed deep learning architecture, combined with the EEG preprocessing, such as the removal of the average baseline signal from each sample and extraction of EEG rhythms (delta, theta, alpha, beta, and gamma), aims to capture repetitive and continuous patterns for memory-induced emotion recognition, underexplored with deep learning techniques. This work has analyzed EEG signals using a wearable, ultra-mobile sports cap while recalling autobiographical emotional memories evoked by affect-denoting words, with self-annotation on the scale of valence and arousal. With extensive experimentation using the same dataset, the proposed framework empirically outperforms existing techniques for the emerging area of memory-induced emotion recognition with an accuracy of 65.6%. The EEG rhythms analysis, such as delta, theta, alpha, beta, and gamma, achieved 65.5%, 52.1%, 65.1%, 64.6%, and 65.0% accuracies for classification with four quadrants of valence and arousal. These results underscore the significant advancement achieved by our proposed method for the real-world environment of memory-induced emotion recognition. [ABSTRACT FROM AUTHOR]

In order to achieve high-precision detection of controller's workload, firstly, pre-experiments was designed to select representative and distinguishable control scene scenarios, based on which, experiments were designed to obtain the (electroencephalogram) EEG data and workload data of controllers in different work scenarios, and the validity of the selected experimental scenarios was verified by the Shapiro-Wilk (S-W) test. Subsequently, a series of EEG features were proposed and their correlation with workload was analysed. The results show that the correlation between the gamma wave-related indexes β + θ + α + γ and workload is the highest, which can be used for the workload detection of controllers. Finally, the “leave-one-out cross-validation method” was used to establish a decision tree regression model to detect the workload of controllers. The detection accuracy is up to 0. 979 ~ 0. 996, demonstrating that the power spectrum features associated with gamma waves can be used for controller workload detection. [ABSTRACT FROM AUTHOR]

This literature review explores the pivotal role of brain–computer interface (BCI) technology, coupled with electroencephalogram (EEG) technology, in advancing rehabilitation for individuals with damaged muscles and motor systems. This study provides a comprehensive overview of recent developments in BCI and motor control for rehabilitation, emphasizing the integration of user-friendly technological support and robotic prosthetics powered by brain activity. This review critically examines the latest strides in BCI technology and its application in motor skill recovery. Special attention is given to prevalent EEG devices adaptable for BCI-driven rehabilitation. The study surveys significant contributions in the realm of machine learning-based and deep learning-based rehabilitation evaluation. The integration of BCI with EEG technology demonstrates promising outcomes for enhancing motor skills in rehabilitation. The study identifies key EEG devices suitable for BCI applications, discusses advancements in machine learning approaches for rehabilitation assessment, and highlights the emergence of novel robotic prosthetics powered by brain activity. Furthermore, it showcases successful case studies illustrating the practical implementation of BCI-driven rehabilitation techniques and their positive impact on diverse patient populations. This review serves as a cornerstone for informed decision-making in the field of BCI technology for rehabilitation. The results highlight BCI's diverse advantages, enhancing motor control and robotic integration. The findings highlight the potential of BCI in reshaping rehabilitation practices and offer insights and recommendations for future research directions. This study contributes significantly to the ongoing transformation of BCI technology, particularly through the utilization of EEG equipment, providing a roadmap for researchers in this dynamic domain. [ABSTRACT FROM AUTHOR]

Epilepsy, as a serious neurological disorder, can be detected by analyzing the brain signals produced by neurons. Electroencephalogram (EEG) signals are the most important data source for monitoring these brain signals. However, these complex, noisy, nonlinear and nonstationary signals make detecting seizures become a challenging task. Feature-based seizure detection algorithms have become a dominant approach for automatic seizure detection. This study presents an algorithm for automatic seizure detection based on novel features with clinical and statistical significance. Our algorithms achieved the best results on two benchmark datasets, outperforming traditional feature-based methods and state-of-the-art deep learning algorithms. Accuracy exceeded 99.99% on both benchmark public datasets, with the 100% correct detection of all seizures on the second one. Due to the interpretability and robustness of our algorithm, combined with its minimal computational resource requirements and time consumption, it exhibited substantial potential value in the realm of clinical application. The coefficients of variation of datasets proposed by us makes the algorithm data-specific and can give theoretical guidance on the selection of appropriate random spectral features for different datasets. This will broaden the applicability scenario of our feature-based approach. [ABSTRACT FROM AUTHOR]

Currently, steady-state visual evoked potentials (SSVEPs) are applied in a variety of fields. In these applications, spatial filtering is the most commonly used method for decoding SSVEPs. However, existing methods for the SSVEP decoding require improvements in terms of the interaction speed, accuracy, and ease of use. Recent advances in performance have been attributed to the existing hybridisation-based approaches. This article presents and evaluates a novel approach for target identification of SSVEP. The suggested target recognition method significantly enhances the performance of SSVEP decoding by combining two existing methods: extended canonical correlation analysis (eCCA) and the sum of squared correlation (SSCOR). This combined approach is referred to as the extended CCA and sum of squared correlation approach (eCCA-SSCOR). The results of the offline experimental comparison are based on two publicly available datasets, including the San Diego dataset, which comprises 12 frequency targets recorded from 10 individuals, and the Benchmark dataset, comprising 40 frequency targets from 35 subjects. The statistical significance of the improvement was tested using paired samples t-tests and Wilcoxon rank-sum tests. The results indicate that the suggested eCCA-SSCOR approach significantly improves detection accuracy and ITR compared to four existing state-of-the-art methods: canonical correlation analysis (CCA), filter bank CCA (FBCCA), eCCA, and SSCOR. Our method achieved a high average accuracy for all subjects, with 99.5% for the San Diego dataset, and 98.60% for the Benchmark dataset corresponding to data length of 3.5 s and 5 s, respectively. Furthermore, a high ITR of 212.44 bits/min was achieved with a data duration ( T w ) of 0.75 s using the San Diego dataset and was 238.66 bits/min with a T w of 1 s for the Benchmark dataset. These offline results demonstrate that the proposed approach successfully combines eCCA and SSCOR to improve SSVEP decoding. Additionally, it is advantageous for applications based on offline processing and can be adapted for online analysis. Online applications can benefit from these results by providing fast interfaces with a large number of commands. [ABSTRACT FROM AUTHOR]

Personal identification systems based on electroencephalographic (EEG) signals have their own strengths and limitations. The stability of EEG signals strongly affects such systems. The human emotional state is one of the important factors that affects EEG signals' stability. Stress is a major emotional state that affects individuals' capability to perform day-to-day tasks. The main objective of this work is to study the effect of mental and emotional stress on such systems. Two experiments have been performed. In the first, we used hand-crafted features (time domain, frequency domain, and non-linear features), followed by a machine learning classifier. In the second, raw EEG signals were used as an input for the deep learning approaches. Different types of mental and emotional stress have been examined using two datasets, SAM 40 and DEAP. The proposed experiments proved that performing enrollment in a relaxed or calm state and identification in a stressed state have a negative effect on the identification system's performance. The best achieved accuracy for the DEAP dataset was 99.67 % in the calm state and 96.67 % in the stressed state. For the SAM 40 dataset, the best accuracy was 99.67 % , 93.33 % , 92.5 % , and 91.67 % for the relaxed state and stress caused by identifying mirror images, the Stroop color-word test, and solving arithmetic operations, respectively. [ABSTRACT FROM AUTHOR]

The current study investigated the effectiveness of social robots in facilitating stress management interventions for university students by evaluating their physiological responses. We collected electroencephalogram (EEG) brain activity and Galvanic Skin Responses (GSRs) together with self-reported questionnaires from two groups of students who practiced a deep breathing exercise either with a social robot or a laptop. From GSR signals, we obtained the change in participants' arousal level throughout the intervention, and from the EEG signals, we extracted the change in their emotional valence using the neurometric of Frontal Alpha Asymmetry (FAA). While subjective perceptions of stress and user experience did not differ significantly between the two groups, the physiological signals revealed differences in their emotional responses as evaluated by the arousal–valence model. The Laptop group tended to show a decrease in arousal level which, in some cases, was accompanied by negative valence indicative of boredom or lack of interest. On the other hand, the Robot group displayed two patterns; some demonstrated a decrease in arousal with positive valence indicative of calmness and relaxation, and others showed an increase in arousal together with positive valence interpreted as excitement. These findings provide interesting insights into the impact of social robots as mental well-being coaches on students' emotions particularly in the presence of the novelty effect. Additionally, they provide evidence for the efficacy of physiological signals as an objective and reliable measure of user experience in HRI settings. [ABSTRACT FROM AUTHOR]

Affective computing is an important subfield of artificial intelligence, and with the rapid development of brain-computer interface technology, emotion recognition based on EEG signals has received broad attention. It is still a great challenge to effectively explore the multi-dimensional information in the EEG data in spite of a large number of deep learning methods. In this article, we propose a deep learning model called Attention-based Multiple Dimensions EEG Transformer (AMDET), which can leverage the complementarity among the spectral-spatial-temporal features of EEG data by employing the multi-dimensional global attention mechanism. We first transform the original EEG data into 3D temporal-spectral-spatial representations and then the AMDET would use spectral-spatial transformer blocks to extract effective features in the EEG signal and focus on the critical time frame with the temporal attention block. We conduct extensive experiments on the DEAP, SEED, and SEED-IV datasets to evaluate the performance of AMDET and the results outperform the state-of-the-art baseline on three datasets. Accuracy rates of 97.48%, 96.85%, 97.17%, 87.32% were achieved in the DEAP-Arousal, DEAP-Valence, SEED, and SEED-IV datasets, respectively. Based on AMDET, we can achieve over 90% accuracy with only eight channels, significantly improving the possibility of practical applications. [ABSTRACT FROM AUTHOR]

An affective brain-computer interface (aBCI) can detect affective states based on brain signals and might assist people in improving their emotion regulation abilities. However, individual differences in emotional brain patterns make cross-subject emotion identification extremely challenging. Traditional supervised single-subject classification schemes require considerable calibration samples from new individuals to train subject-dependent models. Individuals are easily fatigued with long-term EEG collection processes, which may affect performance in subsequent online experiments. In this study, we propose a real-time aBCI system using domain-fusion-based multisource style transfer mapping (DF-MS-STM) to detect positive, neutral, and negative emotional states without the need for additional training sessions. Sixteen subjects participated in our online experiments to test the performance of our aBCI system and an average online prediction accuracy of 72.17±12.25% was obtained for three-class emotion recognition tasks in the last three experimental sessions. Our proposed algorithm significantly outperformed numerous baseline methods in terms of cross-subject emotion classification. In addition, we identified distinct brain patterns in response to different emotional stimuli based on the results of event-related spectral perturbation (ERSP) analyses. These neural patterns might provide new insights for emotional brain mechanistic studies and related aBCIs. [ABSTRACT FROM AUTHOR]

The goal of measuring conceptual processing in numerical cognition is distanced by the possibility that neural responses to symbolic numerals are influenced by physical stimulus confounds. Here, we targeted conceptual responses to parity (even versus odd), using electroencephalogram (EEG) frequency-tagging with a symmetry/asymmetry design. Arabic numerals (2–9) were presented at 7.5 Hz in 50 s sequences; odd and even numbers were alternated to target differential, 'asymmetry' responses to parity at 3.75 Hz (7.5 Hz/2). Parity responses were probed with four different stimulus sets, increasing in intra-numeral stimulus variability, and with two control conditions composed of non-conceptual numeral alternations. Significant asymmetry responses were found over the occipitotemporal cortex to all conditions, even for the arbitrary controls. The large physical-differences control condition elicited the largest response in the stimulus set with the lowest variability (one font). Only in the stimulus set with the highest variability (20 drawn, coloured exemplars/numeral) did the response to parity surpass both control conditions. These findings show that physical differences across small sets of Arabic numerals can strongly influence, and even account for, automatic brain responses. However, carefully designed control conditions and highly variable stimulus sets may be used towards identifying truly conceptual neural responses. [ABSTRACT FROM AUTHOR]

Automated emotion recognition is crucial in identifying and monitoring psychological disorders. Although several electroencephalography (EEG)-based methods have been explored for emotion recognition, capturing the subtle oscillations within EEG signals associated with distinct emotional states remains a persistent challenge. Nonlinear phase-based functional connectivity (FC) can capture the intricate time-varying patterns of brain activity during the processing of emotions. In this work, an attempt has been made to characterize the EEG-based emotional states using nonlinear phase-based FC techniques. For this, the EEG signals are obtained from the publicly available DEAP database and decomposed into four frequency bands: Theta (4–7Hz), alpha (8–12Hz), beta (13–30Hz) and gamma (30–45Hz). Three nonlinear phase-based FC measures, namely phase lag index (PLI), phase locking value (PLV) and Rho index, are extracted from individual frequency bands. Two types of features, namely network features and FC indices, are fed to three classifiers, namely random forest (RF), extreme gradient boosting (XGB) and K-Nearest Neighbors (KNN). The results reveal that the proposed approach can capture EEG dynamics to characterize emotional states. The gamma band-based Rho index demonstrated prominence in discriminating arousal and valence emotional states. The utilization of the Rho index-based FC feature effectively reveals interactions among cortical brain regions in response to audio-visual stimuli. Thus, the proposed approach could be extended to classifying various emotional states in clinical settings. [ABSTRACT FROM AUTHOR]

This study introduces Multi-Threshold Recurrence Rate Plots (MTRRP), a novel methodology for analyzing dynamic patterns in complex systems, such as those influenced by neurodegenerative diseases in brain activity. MTRRP characterizes how recurrence rates evolve with increasing recurrence thresholds. A key innovation of our approach, Recurrence Complexity, captures structural complexity by integrating local randomness and global structural features through the product of Recurrence Rate Gradient and Recurrence Hurst, both derived from MTRRP. We applied this technique to resting-state EEG data from patients diagnosed with Alzheimer's Disease (AD), Frontotemporal Dementia (FTD), and age-matched healthy controls. The results revealed significantly higher recurrence complexity in the occipital areas of AD and FTD patients, particularly pronounced in the Alpha and Beta frequency bands. Furthermore, EEG features derived from MTRRP were evaluated using a Support Vector Machine with leave-one-out cross-validation, achieving a classification accuracy of 87.7%. These findings not only underscore the utility of MTRRP in detecting distinct neurophysiological patterns associated with neurodegenerative diseases but also highlight its broader applicability in time series analysis, providing a substantial tool for advancing medical diagnostics and research. [ABSTRACT FROM AUTHOR]

In recent years, fatigue driving has become the main cause of traffic accidents, leading to increased attention towards fatigue detection systems. However, the pooling and strided convolutional operations in fatigue detection algorithm based on traditional deep learning methods may led to the loss of some useful information. This paper proposed a novel τ -shaped convolutional network ( τ Net ) aiming to address this issue. Unlike traditional network structures, τ Net incorporates the operations of upsampling features and concatenating high- and low-level features, enabling full utilization of useful information. Moreover, considering that the fatigue state is a mental state involving temporal evolution, we proposed the novel long short-term memory (LSTM)– τ -shaped convolutional network (LSTM- τ Net ), a parallel structure composed of LSTM and τ Net for fatigue detection, where τ Net extracts time-invariant features with location information, and LSTM extracts long temporal dependencies. We compared LSTM- τ Net with six competing methods based on two datasets. Results showed that the proposed algorithm achieved higher classification accuracy than the other methods, with 94.25% on EEG data (binary classification) and 82.19% on EOG data (triple classification). Additionally, the proposed algorithm exhibits low computational cost, good training stability, and robustness against insufficient training. Therefore, it is promising for further implementation of fatigue online detection systems. [ABSTRACT FROM AUTHOR]

The application of wearable electroencephalogram (EEG) devices is growing in brain–computer interfaces (BCI) owing to their good wearability and portability. Compared with conventional devices, wearable devices typically support fewer EEG channels. Devices with few-channel EEGs have been proven to be available for steady-state visual evoked potential (SSVEP)-based BCI. However, fewer-channel EEGs can cause the BCI performance to decrease. To address this issue, an attention-based complex spectrum–convolutional neural network (atten-CCNN) is proposed in this study, which combines a CNN with a squeeze-and-excitation block and uses the spectrum of the EEG signal as the input. The proposed model was assessed on a wearable 40-class dataset and a public 12-class dataset under subject-independent and subject-dependent conditions. The results show that whether using a three-channel EEG or single-channel EEG for SSVEP identification, atten-CCNN outperformed the baseline models, indicating that the new model can effectively enhance the performance of SSVEP-BCI with few-channel EEGs. Therefore, this SSVEP identification algorithm based on a few-channel EEG is particularly suitable for use with wearable EEG devices. [ABSTRACT FROM AUTHOR]

Current assessments for generalized anxiety disorder (GAD) are often subjective and do not rely on a standardized measure to evaluate the GAD across its severity levels. The lack of objective and multi-level quantitative diagnostic criteria poses as a significant challenge for individualized treatment strategies. To address this need, this study aims to establish a GAD grading and quantification diagnostic model by integrating an electroencephalogram (EEG) and ensemble learning. In this context, a total of 39 normal subjects and 80 GAD patients were recruited and divided into four groups: normal control, mild GAD, moderate GAD, and severe GAD. Ten minutes resting state EEG data were collected for every subject. Functional connectivity features were extracted from each EEG segment with different time windows. Then, ensemble learning was employed for GAD classification studies and brain mechanism analysis. Hence, the results showed that the Catboost model with a 10 s time window achieved an impressive 98.1% accuracy for four-level classification. Particularly, it was found that those functional connections situated between the frontal and temporal lobes were significantly more abundant than in other regions, with the beta rhythm being the most prominent. The analysis framework and findings of this study provide substantial evidence for the applications of artificial intelligence in the clinical diagnosis of GAD. [ABSTRACT FROM AUTHOR]

Electroencephalogram (EEG) studies have suggested compensatory brain overactivation in cognitively healthy (CH) older adults with pathological beta-amyloid(Aβ42)/tau ratios during working memory and interference processing. However, the association between glutamatergic metabolites and brain activation proxied by EEG signals has not been thoroughly investigated. We aim to determine the involvement of these metabolites in EEG signaling. We focused on CH older adults classified under (1) normal CSF Aβ42/tau ratios (CH-NATs) and (2) pathological Aβ42/tau ratios (CH-PATs). We measured plasma glutamine, glutamate, pyroglutamate, and γ-aminobutyric acid concentrations using tandem mass spectrometry and conducted a correlational analysis with alpha frequency event-related desynchronization (ERD). Under the N-back working memory paradigm, CH-NATs presented negative correlations (r = ~−0.74–−0.96, p = 0.0001–0.0414) between pyroglutamate and alpha ERD but positive correlations (r = ~0.82–0.95, p = 0.0003–0.0119) between glutamine and alpha ERD. Under Stroop interference testing, CH-NATs generated negative correlations between glutamine and left temporal alpha ERD (r = −0.96, p = 0.037 and r = −0.97, p = 0.027). Our study demonstrated that glutamine and pyroglutamate levels were associated with EEG activity only in CH-NATs. These results suggest cognitively healthy adults with amyloid/tau pathology experience subtle metabolic dysfunction that may influence EEG signaling during cognitive challenge. A longitudinal follow-up study with a larger sample size is needed to validate these pilot studies. [ABSTRACT FROM AUTHOR]

In this study, we have developed a novel method based on deep learning and brain effective connectivity to classify responders and non-responders to selective serotonin reuptake inhibitors (SSRIs) antidepressants in major depressive disorder (MDD) patients prior to the treatment using EEG signal. The effective connectivity of 30 MDD patients was determined by analyzing their pretreatment EEG signals, which were then concatenated into delta, theta, alpha, and beta bands and transformed into images. Using these images, we then fine tuned a hybrid Convolutional Neural Network that is enhanced with bidirectional Long Short-Term Memory cells based on transfer learning. The Inception-v3, ResNet18, DenseNet121, and EfficientNet-B0 models are implemented as base models. Finally, the models are followed by BiLSTM and dense layers in order to classify responders and non-responders to SSRI treatment. Results showed that the EfficiencyNet-B0 has the highest accuracy of 98.33, followed by DensNet121, ResNet18 and Inception-v3. Therefore, a new method was proposed in this study that uses deep learning models to extract both spatial and temporal features automatically, which will improve classification results. The proposed method provides accurate identification of MDD patients who are responding, thereby reducing the cost of medical facilities and patient care. [ABSTRACT FROM AUTHOR]

Detecting epileptic seizures automatically through intelligent methods is a main challenge in recent years. This is because neurologists are burdened with analyzing electroencephalogram (EEG) data via visual inspection, and automating the process can reduce their workload. However, one of the challenges of automatic seizure detection using EEG analysis is extracting optimal features that can distinguish between different states of epilepsy. To address this issue, this research proposes a new approach for automatically identifying epileptic seizures using a deep convolutional network. The network has 9 convolutional layers and 1 fully-connected layer, which learn the features hierarchically and identify epileptic seizures through the EEG analysis. The designed deep network was applied to the epileptic EEG dataset from the University of Bonn. The results showed that 100% accuracy, 100% sensitivity, and 100% specificity were achieved using the proposed method and 10fold cross-validation for classifying the three investigated EEG conditions (i.e., normal, preictal and ictal states). The proposed architecture was very efficient in classifying epileptic EEG data. Due to the high accuracy of the algorithm, it can be used for automatic detection of different stages of epilepsy for big EEG data. [ABSTRACT FROM AUTHOR]

Detecting the presence of alcohol in individuals poses a significant challenge due to the limitations of conventional devices that rely on odor, which is not always reliable. Electroencephalography (EEG), a widely-used technique for measuring brain activity, has emerged as a promising tool for evaluating subjects with alcoholism. Present study intends to use various types of linear and nonlinear analysis of EEG signal to classify alcoholics and non-alcoholics and provide a direct comparison of the efficiency of each of the analysis methods. After EEG preprocessing, spectral analysis was done to calculate linear features. Then, some nonlinear features were calculated through fractal dimension, entropy analysis, Hurst exponent, Lempel-Ziv complexity and detrended fluctuation analysis. Feature classification was done through KNN, Naive Bayes and AdaBoost classifiers. The suggested methods were assessed on a publicly UCI alcoholic EEG database. Experimental results showed that linear and nonlinear features achieved an accuracy of 74.96% and 93.62%, respectively, for EEG classification of alcoholics and non-alcoholics. Furthermore, Katz fractal dimension had a high accuracy of 95.74%, sensitivity of 98.82% and specificity of 92.20% in distinguishing EEG signals of alcoholics and non-alcoholics. The findings showed that nonlinear features perform better than linear features for alcoholism detection. Therefore, it is recommended to use and investigate nonlinear signal processing methods in future studies for the detection of alcoholic EEG. [ABSTRACT FROM AUTHOR]

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Vascular dementia, the second most common type of dementia, currently lacks a definitive cure. In the pursuit of therapies aimed at slowing its progression and alleviating symptoms, transcranial direct current stimulation (tDCS) emerges as a promising approach, characterized by its non-invasive nature and the ability to promote brain plasticity. In this study, the primary objective was to investigate the effects of a two-week cycle of tDCS on the dorsolateral prefrontal cortex (DLPFC) and neurophysiological functioning in thirty patients diagnosed with vascular dementia. Each participant was assigned to one of two groups: the experimental group, which received anodal tDCS to stimulate DPCFL, and the control group, which received sham tDCS. Neurophysiological functions were assessed before and after tDCS using P300 event-related potentials (ERPs), while neuropsychological function was evaluated through a Mini-Mental State Examination (MMSE). The results showed a reduction in P300 latency, indicating a faster cognitive process; an increase in P300 amplitude, suggesting a stronger neural response to cognitive stimuli; and a significant improvement in MMSE scores compared to the control group, indicating an overall enhancement in cognitive functions. These findings suggest that tDCS could represent a promising therapeutic option for improving both neurophysiological and cognitive aspects in patients with vascular dementia. [ABSTRACT FROM AUTHOR]

Humans are able to pay selective attention to music or speech in the presence of multiple sounds. It has been reported that in the speech domain, selective attention enhances the cross-correlation between the envelope of speech and electroencephalogram (EEG) while also affecting the spatial modulation of the alpha band. However, when multiple music pieces are performed at the same time, it is unclear how selective attention affects neural entrainment and spatial modulation. In this paper, we hypothesized that the entrainment to the attended music differs from that to the unattended music and that spatial modulation in the alpha band occurs in conjunction with attention. We conducted experiments in which we presented musical excerpts to 15 participants, each listening to two excerpts simultaneously but paying attention to one of the two. The results showed that the cross-correlation function between the EEG signal and the envelope of the unattended melody had a more prominent peak than that of the attended melody, contrary to the findings for speech. In addition, the spatial modulation in the alpha band was found with a data-driven approach called the common spatial pattern method. Classification of the EEG signal with a support vector machine identified attended melodies and achieved an accuracy of 100% for 11 of the 15 participants. These results suggest that selective attention to music suppresses entrainment to the melody and that spatial modulation of the alpha band occurs in conjunction with attention. To the best of our knowledge, this is the first report to detect attended music consisting of several types of music notes only with EEG. [ABSTRACT FROM AUTHOR]

Growing electroencephalogram (EEG) studies have linked the abnormities of functional brain networks with disorders of consciousness (DOC). However, due to network data's high-dimensional and non-Euclidean properties, it is difficult to exploit the brain connectivity information that can effectively detect the consciousness levels of DOC patients via deep learning. To take maximum advantage of network information in assessing impaired consciousness, we utilized the functional connectivity with convolutional neural network (CNN) and employed three rearrangement schemes to improve the evaluation performance of brain networks. In addition, the gradient-weighted class activation mapping (Grad-CAM) was adopted to visualize the classification contributions of connections among different areas. We demonstrated that the classification performance was significantly enhanced by applying network rearrangement techniques compared to those obtained by the original connectivity matrix (with an accuracy of 75.0%). The highest classification accuracy (87.2%) was achieved by rearranging the alpha network based on the anatomical regions. The inter-region connections (i.e., frontal-parietal and frontal-occipital connectivity) played dominant roles in the classification of patients with different consciousness states. The effectiveness of functional connectivity in revealing individual differences in brain activity was further validated by the correlation between behavioral performance and connections among specific regions. These findings suggest that our proposed assessment model could detect the residual consciousness of patients. [ABSTRACT FROM AUTHOR]

The motor imagery brain-computer interface (MI-BCI) has the ability to use electroencephalogram (EEG) signals to control and communicate with external devices. By leveraging the unique characteristics of task-related brain signals, this system facilitates enhanced communication with these devices. Such capabilities hold significant potential for advancing rehabilitation and the development of assistive technologies. In recent years, deep learning has received considerable attention in the MI-BCI field due to its powerful feature extraction and classification capabilities. However, two factors significantly impact the performance of deep-learning models. The size of the EEG datasets influences how effectively these models can learn. Similarly, the ability of classification models to extract features directly affects their accuracy in recognizing patterns. In this paper, we propose a Multi-Scale Spatio-Temporal and Dynamic Graph Convolution Fusion Network (MST-DGCN) to address these issues. In the data-preprocessing stage, we employ two strategies, data augmentation and transfer learning, to alleviate the problem of an insufficient data volume in deep learning. By using multi-scale convolution, spatial attention mechanisms, and dynamic graph neural networks, our model effectively extracts discriminative features. The MST-DGCN mainly consists of three parts: the multi-scale spatio-temporal module, which extracts multi-scale information and refines spatial attention; the dynamic graph convolution module, which extracts key connectivity information; and the classification module. We conduct experiments on real EEG datasets and achieve an accuracy of 77.89% and a Kappa value of 0.7052, demonstrating the effectiveness of the MST-DGCN in MI-BCI tasks. Our research provides new ideas and methods for the further development of MI-BCI systems. [ABSTRACT FROM AUTHOR]

Game platforms have different impacts on player experience in terms of affective states and workloads. By studying these impacts, we can uncover detailed aspects of the gaming experience. Traditionally, understanding player experience has relied on subjective methods, such as self-reported surveys, where players reflect on their experience and effort levels. However, complementing these subjective measures with electroencephalogram (EEG) analysis introduces an objective approach to assessing player experience. In this study, we examined player experiences across PlayStation 5, Nintendo Switch, and Meta Quest 2. Using a mixed-methods approach, we merged subjective user assessments with EEG data to investigate brain activity, affective states, and workload during low- and high-stimulation games. We recruited 30 participants to play two games across three platforms. Our findings reveal that there is a statistically significant difference between these three platforms for seven out of nine experience factors. Also, three platforms have different impacts on play experience and brain activity. Additionally, we utilized a linear model to associate player experience aspects such arousal, frustration, and mental workload with different brain regions using EEG data. [ABSTRACT FROM AUTHOR]