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Acquiring intentions before the start of lower limb movement imagery is the key issue in providing precise control strategies for the lower limb neurorehabilitation system. To this end, the feasibility of using electroencephalogram (EEG) signals prior to movement imagery to fully predict the movement imagery start‐stop intention and intention types of lower limb ambulation is investigated. The EEG signal is pre‑processed and the movement‑related cortical potential (MRCP) is extracted. 15 channels with significant discriminability are selected based on MRCP. The temporal convolutional network models are used to decode the movement imagery intention and intention types of lower limb ambulation from the selected MRCP channel features. The results show that the EEG channel signals selected by MRCP morphology are with significant separable differences in both start‐stop intention and intention types, verifying that using only pre‑movement imagery EEG signals is capable of fully prediction the movement imagery start‐stop intention and intention types of lower limb ambulation movement in humans. [ABSTRACT FROM AUTHOR]

Attention deficit hyperactivity disorder (ADHD) is a neuro-developmental disorder that affects approximately 5–10% of school-aged children worldwide. Early diagnosis and intervention are essential to improve the quality of life of patients and their families. In this study, we propose ConvMixer-ECA, a novel deep learning architecture that combines ConvMixer with efficient channel attention (ECA) blocks for the accurate diagnosis of ADHD using electroencephalogram (EEG) signals. The model was trained and evaluated using EEG recordings from 60 healthy children and 61 children with ADHD. A series of experiments were conducted to evaluate the performance of the ConvMixer-ECA. The results showed that the ConvMixer-ECA performed well in ADHD recognition with 94.52% accuracy. The incorporation of attentional mechanisms, in particular ECA, improved the performance of ConvMixer; it outperformed other attention-based variants. In addition, ConvMixer-ECA outperformed state-of-the-art deep learning models including EEGNet, CNN, RNN, LSTM, and GRU. t-SNE visualization of the output of this model layer validated the effectiveness of ConvMixer-ECA in capturing the underlying patterns and features that separate ADHD from typically developing individuals through hierarchical feature learning. These outcomes demonstrate the potential of ConvMixer-ECA as a valuable tool to assist clinicians in the early diagnosis and intervention of ADHD in children. [ABSTRACT FROM AUTHOR]

Developing real-time automatic intuitive devices based on electroencephalography (EEG) to improve hazard recognition performance (HRP) is essential in construction engineering management. However, existing studies generally developed devices based on unimodal data, failing to distinguish the role played by brain activities in different visual areas and ignoring the possible existence of other psychological mechanisms. Therefore, this study aims to use multimodal data based on brain-eye synergy to predict HRP and reveal the cognitive mechanism of hazard recognition to develop intuitive hazard recognition devices. Accordingly, we designed experiments to investigate this problem: (1) 53 construction workers completed a 120-trial hazard recognition task in a laboratory environment (N=6,360), (2) an EEG recorder collected the participants' EEG activities and an eye-tracking device recorded the data of eye movement, and (3) the power spectral density (PSD) of EEG was calculated and then imported into Matlab2018a in combination with EEGLAB for analysis. The results showed that the prediction results of hazard recognition have a time-variant effect. For fall-related hazards, EEG signals within 0–320 ms after the onset were significant in predicting HRP; for electric-related and fire-related hazards, EEG signals within 560–640 ms after the starting point were the most valuable. Also, compared with fall-related hazards, it took a significantly longer time to recognize electric-related and fire-related hazards, which consumed more attentional resources. Finally, the frontal lobe requires more attentional resources to finish cognitive activities than the occipital lobe to finish visual information processing activities, and the occipital lobe needs to process visual information first, and then transmit it to the frontal lobe for analysis and judgment. For the body of knowledge, this study, finding the time-variant effect of HRP, improves the theory of construction hazard recognition from an eye-brain synergy perspective and provides guidance for developing intuitive hazard recognition devices and improving management performance. This study combined eye-movement data and EEG data to predict the performance of hazard recognition using multimodal data. Not only did this study reveal the mechanism of hazard recognition, but it also proposed suggestions for project management and device development. Managers are suggested to implement refined management and regularly monitor the physiological signals of employees. Some construction companies started to use smart devices to measure workers' arousal, emotional valence, emotional regulation ability, fatigue, workload, or safety attention level before work every day. And it is recommended to provide safety reminders to employees based on whether there are any abnormalities in their physiological indicators. In addition to project management, the results of this study can serve the development or optimization of hazard recognition equipment. Developing devices cannot only consider EEG information, but also combine EEG information with eye-movement information. Meanwhile, developers can also consider different types of hazards and different brain regions to improve device performance. [ABSTRACT FROM AUTHOR]

Introduction: Visual attention is a cognitive skill related to visual perception and neural activity, and also moderated by expertise, in time-constrained professional domains (e.g., aviation, driving, sport, surgery). However, the contribution of both perceptual and neural processes on performance has been studied separately in the literature. Development: We defend an integration of visual and neural signals to offer a more complete picture of the visual attention displayed by professionals of different skill levels when performing free-viewing tasks. Specifically, we propose to zoom the analysis in data related to the quiet eye and P300 component jointly, as a novel signal processing approach to evaluate professionals' visual attention. Conclusion: This review highlights the advantages of using portable eye trackers and electroencephalogram systems altogether, as a promising technique for a better understanding of early cognitive components related to attentional processes. Altogether, the eye-fixation-related potentials method may provide a better understanding of the cognitive mechanisms employed by the participants in natural settings, revealing what visual information is of interest for participants and distinguishing the neural bases of visual attention between targets and non-targets whenever they perceive a stimulus during free viewing experiments. [ABSTRACT FROM AUTHOR]

Eyeblinks and other large artifacts can create two major problems in event‐related potential (ERP) research, namely confounds and increased noise. Here, we developed a method for assessing the effectiveness of artifact correction and rejection methods in minimizing these two problems. We then used this method to assess a common artifact minimization approach, in which independent component analysis (ICA) is used to correct ocular artifacts, and artifact rejection is used to reject trials with extreme values resulting from other sources (e.g., movement artifacts). This approach was applied to data from five common ERP components (P3b, N400, N170, mismatch negativity, and error‐related negativity). Four common scoring methods (mean amplitude, peak amplitude, peak latency, and 50% area latency) were examined for each component. We found that eyeblinks differed systematically across experimental conditions for several of the components. We also found that artifact correction was reasonably effective at minimizing these confounds, although it did not usually eliminate them completely. In addition, we found that the rejection of trials with extreme voltage values was effective at reducing noise, with the benefits of eliminating these trials outweighing the reduced number of trials available for averaging. For researchers who are analyzing similar ERP components and participant populations, this combination of artifact correction and rejection approaches should minimize artifact‐related confounds and lead to improved data quality. Researchers who are analyzing other components or participant populations can use the method developed in this study to determine which artifact minimization approaches are effective in their data. Blinks and the other large artifacts can significantly reduce effect sizes and create confounds. We propose a new method for assessing the effectiveness of artifact correction and rejection in maximizing effect sizes and minimizing confounds. We also apply this method to data from five ERP components to demonstrate that a common combination of artifact correction and rejection is reasonably effective. [ABSTRACT FROM AUTHOR]

Even though Electric Powered Wheelchairs (EPWs) are a useful tool for meeting the needs of people with disabilities, some disabled people find it difficult to use regular EPWs that are joystick-controlled. Smart wheelchairs that use Brain–Computer Interface (BCI) technology present an efficient solution to this problem. This article presents a cutting-edge intelligent control wheelchair that is intended to improve user involvement and security. The suggested method combines facial expression analysis via a camera with EEG signal processing using the EMOTIV Insight EEG dataset. The system generates control commands by identifying specific EEG patterns linked to facial expressions such as eye blinking, winking left and right, and smiling. Simultaneously, the system uses computer vision algorithms and inertial measurements to analyze gaze direction in order to establish the user's intended steering. The outcomes of the experiments prove that the proposed system is reliable and efficient in meeting the various requirements of people, presenting a positive development in the field of smart wheelchair technology. [ABSTRACT FROM AUTHOR]

Emotion recognition utilizing EEG signals has emerged as a pivotal component of human–computer interaction. In recent years, with the relentless advancement of deep learning techniques, using deep learning for analyzing EEG signals has assumed a prominent role in emotion recognition. Applying deep learning in the context of EEG-based emotion recognition carries profound practical implications. Although many model approaches and some review articles have scrutinized this domain, they have yet to undergo a comprehensive and precise classification and summarization process. The existing classifications are somewhat coarse, with insufficient attention given to the potential applications within this domain. Therefore, this article systematically classifies recent developments in EEG-based emotion recognition, providing researchers with a lucid understanding of this field's various trajectories and methodologies. Additionally, it elucidates why distinct directions necessitate distinct modeling approaches. In conclusion, this article synthesizes and dissects the practical significance of EEG signals in emotion recognition, emphasizing its promising avenues for future application. [ABSTRACT FROM AUTHOR]

Mental stress has become one of the major reasons for the failure of students or their poor performance in the traditional limited-duration examination system. In this study, we aim to find the relationship between the student's level of stress and the deterioration of their subsequent examination results. Furthermore, we want to explore if different EEG frequency bands can be used as biomarkers of stress levels. We collected EEG data from the students while they performed the Montreal Imaging Stress Task-based Mental Arithmetic Tasks (MAT). They performed tests of the same level of difficulty twice; once without any limitation of time (and/or feedback) and next under limited time followed by feedback to induce more stress. We observed that the average score of 95% in the untimed test was dropped to 78% in the case of a timed test and a substantial difference in spectral powers of beta, alpha, and theta frequency bands of EEG. We took this limitation of time as a stressor and comprised three classes based on three stress levels (relaxed during rest, low during an untimed test, and high during a timed test). We used two different deep learning frameworks to classify this data and an accuracy of 70.67% was achieved using Long Short-Term Memory (LSTM) and 90.46% with convolutional neural networks (CNN). We found that time limitation increases the stress level of students and impairs performance, while EEG frequency bands converted to brainwave images, serve as potential biomarkers for stress detection. [ABSTRACT FROM AUTHOR]

Facial expressions are one of the most effective and straightforward ways of conveying our emotions and intentions. Therefore, it is crucial to conduct research aimed at developing a Brain Computer Interface (BCI) that can assist individuals with facial motor disabilities in expressing their emotions. This paper proposes a hybrid GAN-based model that reconstructs a personalized facial expression based on their corresponding emotional ElectroEncephaloGram (EEG) signals, utilizing a conditional Generative Adversarial Network (cGAN). To recognize emotions using EEG signals, a novel method called CSP-BiLSTM is introduced, which combines the Common Spatial Pattern (CSP) and Bidirectional Long Short-Term Memory (BiLSTM) network to explore the spatial and temporal dependencies of the raw EEG signals. Finally, a Fully Connected (FC) layer with a Softmax activation function are applied to the extracted spatiotemporal features to recognize the label of the EEG signals. The predicted emotional label is then input into a cGAN, along with a neutral facial image, to emotionally reconstruct the input image. Experimental results on the SEED dataset demonstrate that our proposed CSP-BiLSTM model outperforms previous models in both subject-dependent and cross-subject scenarios, achieving 99.97% and 99.93% accuracy, respectively, for three classification tasks. A thorough evaluation of the images generated by cGAN is conducted using three challenging facial expression benchmarks: AffectNet, CK + , and CelebA. The results of applying FID, emotion classification using pre-trained Arcface model, and human evaluation indicated that samples synthesized using this model outperformed those generated by recent techniques. [ABSTRACT FROM AUTHOR]

Understanding pedestrians' cognitive processes in traffic environments is crucial for developing strategies to enhance safety and reduce accidents. This study assesses the efficacy of virtual reality (VR) in evaluating pedestrian behavior in simulated road-crossing scenarios. It investigates VR's capability to realistically mimic the cognitive load experienced in real-world settings. It examines the technical integration of VR with psychophysiological recording to capture cognitive demand indicators accurately. Utilizing a dedicated VR application and electroencephalogram (EEG) measurements, this research aims to elicit significant Event-Related Potentials (ERP), like P3 and Contingent Negative Variation (CNV), associated with decision-making processes. The initial results demonstrate VR's effectiveness in creating realistic environments for investigating cognitive mechanisms and the balance between induced immersion and experienced discomfort. Additionally, the tasks involving time-to-arrival estimations and oddball scenarios elicited the anticipated components related to attentional and decision-making processes. Despite increased discomfort with extended VR exposure, our results show that it did not negatively impact the cognitive workload. These outcomes highlight VR's efficacy in replicating the cognitive demands of real-world settings and provide evidence to understand the neurophysiological and behavioral dynamics of vulnerable road users (VRUs) in traffic scenarios. Furthermore, these findings support VR's role in behavioral and neurophysiological research to design specific safety interventions for VRUs. [ABSTRACT FROM AUTHOR]

Digital health tracking is a source of valuable insights for public health research and consumer health technology. The brain is the most complex organ, containing information about psychophysical and physiological biomarkers that correlate with health. Specifically, recent developments in electroencephalogram (EEG), functional near-infra-red spectroscopy (fNIRS), and photoplethysmography (PPG) technologies have allowed the development of devices that can remotely monitor changes in brain activity. The inclusion criteria for the papers in this review encompassed studies on self-applied, remote, non-invasive neuroimaging techniques (EEG, fNIRS, or PPG) within healthcare applications. A total of 23 papers were reviewed, comprising 17 on using EEGs for remote monitoring and 6 on neurofeedback interventions, while no papers were found related to fNIRS and PPG. This review reveals that previous studies have leveraged mobile EEG devices for remote monitoring across the mental health, neurological, and sleep domains, as well as for delivering neurofeedback interventions. With headsets and ear-EEG devices being the most common, studies found mobile devices feasible for implementation in study protocols while providing reliable signal quality. Moderate to substantial agreement overall between remote and clinical-grade EEGs was found using statistical tests. The results highlight the promise of portable brain-imaging devices with regard to continuously evaluating patients in natural settings, though further validation and usability enhancements are needed as this technology develops. [ABSTRACT FROM AUTHOR]

Objective: Electroconvulsive therapy (ECT) is used to treat several mental illnesses. Seizure duration is used to determine if the administered stimulus was adequate. Duration is estimated by electroencephalogram (EEG) interpretation and/or observed motor response (OMR). Neither method is considered the gold standard. This study investigated the relationship between the 2 methods. The hypothesis was that both EEG and OMR would be significantly positively correlated. Previous researchers have suggested that the 2 methods resulted in different estimates. Methods: A case series was conducted using recorded estimates obtained prospectively from 102 ECTs on adult psychiatric inpatients. Results: A strong positive association was not observed in this study, correlation coefficient 0.510 (p < 0.001). Conclusions: This study suggests that the 2 methods differ, and further research is needed to determine the best indicator of adequate treatment. [ABSTRACT FROM AUTHOR]

• MTS was associated with a higher number of intracranial subclinical seizures in patients with MTLE. • The patients with MTS exhibited favorable short-term and long-term surgical outcomes. • Patients with MTS who achieved Engel I - II classification had higher numbers of i-SCSs than patients without MTS. To investigate the association between subclinical seizures detected on intracranial electroencephalographic (i-SCSs) recordings and mesial temporal sclerosis (MTS), as well as their impact on surgical outcomes of stereotactic laser amygdalohippocampotomy (SLAH). A retrospective review was conducted on 27 patients with drug-resistant mesial temporal lobe epilepsy (MTLE) who underwent SLAH. The number of seizures detected on scalp EEG and iEEG was assessed. Patients were followed for a minimum of 3 years after SLAH. Of the 1715 seizures recorded from mesial temporal regions, 1640 were identified as i-SCSs. Patients with MTS were associated with favorable short- and long-term surgical outcomes. Patients with MTS had a higher number of i-SCSs compared to patients without MTS. The numbers of i-SCSs were higher in patients with Engel I-II outcomes, but no significant statistical difference was found. However, it was observed that patients with MTS who achieved Engel I-II classification had higher numbers of i-SCSs than patients without MTS (P < 0.05). Patients with MTS exhibited favorable short-term and long-term surgical outcome after SLAH. A higher number of i-SCSs was significantly associated with MTS in patients with MTLE. The number of i-SCSs tended to be higher in patients with Engel Ⅰ-Ⅱ surgical outcomes. The association between i-SCSs, MTS, and surgical outcomes in MTLE patients undergoing SLAH has significant implications for understanding the underlying mechanisms and identifying potential therapeutic targets to enhance surgical outcomes. [ABSTRACT FROM AUTHOR]

The recognition task of visual stimuli based on EEG (Electroencephalogram) has become a major and important topic in the field of Brain–Computer Interfaces (BCI) research. Although the underlying spatial features of EEG can effectively represent visual stimulus information, it still remains a highly challenging task to explore the local–global information of the underlying EEG to achieve better decoding performance. Therefore, in this paper we propose a deep learning architecture called Linear-Attention-combined Convolutional Neural Network (LACNN) for visual stimuli EEG-based classification task. The proposed architecture combines the modules of Convolutional Neural Networks (CNN) and Linear Attention, effectively extracting local and global features of EEG for decoding while maintaining low computational complexity and model parameters. We conducted extensive experiments on a public EEG dataset from the Stanford Digital Repository. The experimental results demonstrate that LACNN achieves an average decoding accuracy of 54.13% and 29.83% in 6-category and 72-exemplar classification tasks respectively, outperforming the state-of-the-art methods, which indicates that our method can effectively decode visual stimuli from EEG. Further analysis of LACNN shows that the Linear Attention module improves the separability between different category features and localizes key brain region information that aligns with the paradigm principles. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the wake of the incessant evolution of virtual reality (VR) technology, its pervasive integration into an expanding array of sectors, notably the medical field, has become increasingly conspicuous. Amid these developments, post‐traumatic stress disorder (PTSD), a prevalent and debilitating mental health condition, has garnered heightened attention from the medical community. The contemporary therapeutic landscape predominantly revolves around psychological interventions, exemplified by exposure therapy and positive thinking therapy. However, a groundbreaking advancement, namely virtual reality exposure therapy (VRET), has emerged as a prominent achievement, effectively intertwining VR technology with medical applications, particularly in the nuanced treatment of PTSD. As the realms of human‐computer interaction and brain‐computer interface undergo continuous and dynamic expansion, electroencephalography (EEG) has surfaced as a pivotal convergence point across diverse disciplines. The confluence of VRET technology with EEG signals holds the promise of enhancing the precision and objectivity of patient diagnoses. Furthermore, leveraging EEG signals enables a more nuanced and individualized approach to treatment within the framework of VRET technology. Despite extant studies that offer comprehensive summaries of VRET technology, the integration with EEG signals has thus far been a relatively unexplored terrain. This research endeavors to direct attention toward recent advancements in VRET technology within the sphere of mental health treatment. A meticulous synthesis and exposition of existing techniques and applications are presented, providing a comprehensive overview of the current landscape. The exploration extends to the developmental trajectory and prospective implications arising from the amalgamation of VRET technology and EEG technology. Furthermore, a rigorous analysis of the tangible contributions and practical significance of EEG signals in the domain of mental health treatment is undertaken, underscoring the potential avenues for their future applications and the transformative impact on the trajectory of therapeutic interventions. [ABSTRACT FROM AUTHOR]

Physiological signals, such as EEG and eye movements, have emerged as promising research topics in emotion recognition due to their inherent advantages of objectivity, high recognition accuracy, and cost-effectiveness. However, most existing methods for fusing EEG and eye movement signals use concatenation or weighted summation, which may lead to information loss and limited ability to resist noise. To tackle this issue, in this paper, we propose a Coordinated-representation Decision Fusion Network (CoDF-Net) to efficiently fuse the representation of EEG and eye movement signals. Specifically, CoDF-Net first learns personalized information by maximizing the correlation between modalities. Next, the Decision-level Fusion Broad Learning System (DF-BLS) is developed to construct multiple sub-systems to obtain the final emotional states via the effective decision-making mechanism. To evaluate the performance of the proposed method, subject-dependent and subject-independent experiments are designed on two public datasets. Extensive experiments demonstrate that the proposed method has superior emotion recognition performance over traditional approaches and current state-of-the-art methods. The CoDF-Net achieves 94.09 and 91.62% in the subject-dependent setting and 87.04 and 83.87% in the subject-independent setting on the SEED-CHN and SEED-GER datasets, respectively. Moreover, it is found that the proposed method exhibits a more significant ability to resist noise by adding Gaussian noise with different standard deviations. [ABSTRACT FROM AUTHOR]

Millions of people worldwide who suffer from epilepsy are severely impacted by frequent, erratic seizures. Preventing seizures from occurring could greatly enhance patient care and safety. This paper presents an approach to real-time epileptic seizure prediction utilizing a unique kind of neural network known as Bidirectional Long ShortTerm Memory (Bi-LSTM). The existing multidimensional CNN deep neural network models have average accuracy for multi-channel EEG data in predicting seizures. The proposed model was selected for its excellent ability to interpret complex seizure patterns from both historical and prospective data. We took advantage of a big dataset of real-time EEG recordings of brain activity. The testing yielded the results with the approach of 98% accuracy in predicting seizures. Using the Bi-LSTM model in real-time systems has shown that it can make precise predictions quickly, offering hope for improving the lives of those with epilepsy. [ABSTRACT FROM AUTHOR]

Recognizing familiar faces holds great value in various fields such as medicine, criminal investigation, and lie detection. In this paper, we designed a Complex Trial Protocol-based familiar and unfamiliar face recognition experiment that using self-face information, and collected EEG data from 147 subjects. A novel neural network-based method, the EEG-based Face Recognition Model (EEG-FRM), is proposed in this paper for cross-subject familiar/unfamiliar face recognition, which combines a multi-scale convolutional classification network with the maximum probability mechanism to realize individual face recognition. The multi-scale convolutional neural network extracts temporal information and spatial features from the EEG data, the attention module and supervised contrastive learning module are employed to promote the classification performance. Experimental results on the dataset reveal that familiar face stimuli could evoke significant P300 responses, mainly concentrated in the parietal lobe and nearby regions. Our proposed model achieved impressive results, with a balanced accuracy of 85.64%, a true positive rate of 73.23%, and a false positive rate of 1.96% on the collected dataset, outperforming other compared methods. The experimental results demonstrate the effectiveness and superiority of our proposed model. [ABSTRACT FROM AUTHOR]

Non-invasive Brain-Computer Interface (BCI) systems have gained a massive boost from the advancement and developments in signal processing applications and BCI systems can be used, used in several applications like robotic arms. Implementation of BCI systems massively depends on the analysis of Steady-State Visually Evoked Potential (SSVEP) (SSVEP) components. The correct extraction of Steady-State Visually Evoked Potential (SSVEP) components, however, is crucial and difficult. As a result, by assessing SSVEP components from EEG data, an Adaptive Spatial Filtering approach is presented to successfully identify targets for the use of the SSVEP-based BCI system. Here, methods for identifying targets using EEG analysis are discussed. Here, the minimization of computational complexity and the optimization of the Eigenvalue problem are covered. The features provided in the reference signal are pre-developed and lacked precision in the earlier SSVEP detection algorithms. Therefore, the development of reference signals can address the inaccuracy of frequency detection. A MAMEM SSVEP dataset is used to analyze the performance results of the proposed Adaptive Spatial Filtering based Component Exploration (ASFCE) model. Results are presented in terms of detection accuracy and Information transfer rate (ITR) using the proposed ASFCE model and compared against varied SSVEP acquisition methods. The mean target detection accuracy and ITR results considering all 11 subjects are 93.48% and 308.23 bpm, respectively. [ABSTRACT FROM AUTHOR]

Obsessive-compulsive disorder (OCD) is a chronic disease and psychosocial disorder that significantly reduces the quality of life of patients and affects their personal and social relationships. Therefore, early diagnosis of this disorder is of particular importance and has attracted the attention of researchers. In this research, new statistical differential features are used, which are suitable for EEG signals and have little computational load. Hilbert-Huang transform was applied to EEGs recorded from 26 OCD patients and 30 healthy subjects to extract instant amplitude and phase. Then, modified mean, variance, median, kurtosis and skewness were calculated from amplitude and phase data. Next, the difference of these statistical features between various pairs of EEG channels was calculated. Finally, different scenarios of feature classification were examined using the sparse nonnegative least squares classifier. The results showed that the modified mean feature calculated from the amplitude and phase of the interhemispheric channel pairs produces a high accuracy of 95.37%. The frontal lobe of the brain also created the most distinction between the two groups among other brain lobes by producing 90.52% accuracy. In addition, the features extracted from the frontal-parietal network produced the best classification accuracy (93.42%) compared to the other brain networks examined. The method proposed in this paper dramatically improves the accuracy of EEG classification of OCD patients from healthy individuals and produces much better results compared to previous machine learning techniques. [ABSTRACT FROM AUTHOR]

Introduction: Brain-computer interfaces (BCIs) are systems that acquire the brain's electrical activity and provide control of external devices. Since electroencephalography (EEG) is the simplest non-invasive method to capture the brain's electrical activity, EEG-based BCIs are very popular designs. Aside from classifying the extremity movements, recent BCI studies have focused on the accurate coding of the finger movements on the same hand through their classification by employing machine learning techniques. State-of-the-art studies were interested in coding five fingermovements by neglecting the brain's idle case (i.e., the state that brain is not performing any mental tasks). This may easily cause more false positives and degrade the classification performances dramatically, thus, the performance of BCIs. This study aims to propose a more realistic system to decode the movements of five fingers and the no mental task (NoMT) case from EEG signals. Methods: In this study, a novel praxis for feature extraction is utilized. Using Proper Rotational Components (PRCs) computed through Intrinsic Time Scale Decomposition (ITD), which has been successfully applied in different biomedical signals recently, features for classification are extracted. Subsequently, these features were applied to the inputs of well-known classifiers and their different implementations to discriminate between these six classes. The highest classifier performances obtained in both subject-independent and subject-dependent cases were reported. In addition, the ANOVA-based feature selection was examined to determine whether statistically significant features have an impact on the classifier performances or not. Results: As a result, the Ensemble Learning classifier achieved the highest accuracy of 55.0% among the tested classifiers, and ANOVA-based feature selection increases the performance of classifiers on five-finger movement determination in EEG-based BCI systems. Discussion: When compared with similar studies, proposed praxis achieved a modest yet significant improvement in classification performance although the number of classes was incremented by one (i.e., NoMT). [ABSTRACT FROM AUTHOR]

With the recent advent of smart wearable sensors for monitoring brain activities in real-time, the scopes for using Electroencephalograms (EEGs) and Magnetoencephalography (MEG) in mobile and dynamic environments have become more relevant. However, their application in dynamic and open environments, typical of mobile wearable use, poses challenges. Presently, there is limited clinical data on using EEG/MEG as wearables. To advance these technologies at a time when large-scale clinical trials are not feasible, many researchers have turned to realistic phantom heads to further explore EEG and MEG capabilities. However, to achieve translational results, such phantom heads should have matching geometric features and electrical properties. Here, we have designed and fabricated multilayer chopped carbon fiber–PDMS reinforced composites to represent phantom head tissues. Two types of phantom layers are fabricated, namely seven-layer and four-layer systems with a goal to achieve matching electrical conductivities in each layer. Desired electrical conductivities are obtained by varying the weight fraction of the carbon fibers in PDMS. Then, the prototype system was calibrated and tested with a 32-electrode EEG cap. The test results demonstrated that the phantom effectively generates a variety of scalp potential patterns, achieved through a finite number of internal dipole generators within the phantom sample. This innovative design holds potential as a valuable test platform for assessing wearable EEG technology as well as developing an EEG analysis process. [ABSTRACT FROM AUTHOR]

Transfer learning (TL) utilizes knowledge from the source domain (SD) to enhance the classification rate in the target domain (TD). It has been widely used to address the challenge of sessional and inter-subject variations in electroencephalogram (EEG)-based brain–computer interfaces (BCIs). However, utilizing knowledge from a combination of both related and non-related sources can significantly deteriorate the classification performance across individual target domains, resulting in a negative transfer (NT). Hence, NT becomes one of the most significant challenges for transfer learning algorithms. Notably, domain selection techniques have been developed to address the challenge of NT emerging from the transfer of knowledge from non-related sources. However, existing domain selection approaches iterate through domains and remove a single low-beneficial domain at a time, which can massively affect the classification performance in each iteration since SDs respond differently to other sources. In this paper, we compare domain selection techniques for a multi-source manifold feature transfer learning (MMFT) framework to address the challenge of NT and then evaluate the effect of beneficial and non-beneficial sources on TL performance. To evaluate the effect of low-beneficial and high beneficial sources on TL performance, some commonly used domain selection methods are compared, namely, domain transferability estimation (DTE), rank of domain (ROD), label similarity analysis, and enhanced multi-class MMFT (EMC-MMFT), using the same multi-class cross-session and cross-subject classification problems. The experimental results demonstrate the superiority of the EMC-MMFT algorithm in terms of minimizing the effect of NT. The highest classification accuracy (CA) of 100% is achieved when optimal combinations of high beneficial sources are selected for two-class SSMVEP problems, while the highest CAs of 98% and 87% are achieved for three- and four-class SSMVEP problems, respectively. The highest CA of 98% is achieved for two-class MI classification problems, while the highest CAs of 90% and 71.5% are obtained for three- and four-class MI problems, respectively. [ABSTRACT FROM AUTHOR]

The total amount of mental activity applied to working memory at a given point in time is called cognitive load, which is an important factor in various activities in daily life. We have proposed new feature quantities that reflect the time-series changes in the power of typical frequency bands in electroencephalogram (EEG) for use in examining the relationship between brain activity and behavior under cognitive load. We also measured heart rate variability (HRV) and spontaneous skin conductance responses (SCR) to examine functional associations among brain activity, autonomic activity, and behavior under cognitive load. Additionally, we applied our machine learning model previously developed using EEG to the estimation of arousal level to interpret the brain-autonomic-behavior functional association under cognitive load. Experimental data from 12 healthy undergraduate students showed that participants with higher levels of infra- slow fluctuations of alpha power have more cognitive resources and thus can process information under cognitive load more efficiently. In addition, HRV reflecting parasympathetic activity correlated with task accuracy. The arousal level estimated using our machine learning model showed its robust relationship with EEG. Despite the limitation of the sample size, the results of this pilot study suggest that the information processing efficiency of the brain under cognitive load is reflected by time-series fluctuations in EEG, which are associated with an individual's task performance. These findings can contribute to the evaluation of the internal state of humans associated with cognitive load and the prediction of human behaviors in various situations under cognitive load. [ABSTRACT FROM AUTHOR]

In this study, we compare the classification accuracy achievable with linear support vector machine (L-SVM), K-nearest neighbor (KNN), and multilayer perceptron (MLP) methods for a multi-class EEG signal. This can be done in three phases. In phase one, band-pass filtering is applied to raw electroencephalogram (EEG) signals to decompose into five different frequency subbands. In phase two, we extract 10 important features from each subband. In phase three, these extracted feature sets are used as input to L-SVM, KNN, and MLP classifiers which categorize the sample data into three classes namely yoga, meditation, and combined yoga–meditation. Various performance measures for each classifier are evaluated and then compared to know which classifier is effective in the classification of the EEG data into yoga, meditation, and combined yoga–meditation groups. Performance measures such as confusion matrix, accuracy, sensitivity, specificity, precision, and F1 score are used to validate the performance of classifiers. Kruskal–Wallis test has been conducted to compare the classification performance of the linear SVM, KNN, and MLP classifier models. By comparing the classification accuracy between the three classifiers, L-SVM achieved the highest accuracy of 91.67%. [ABSTRACT FROM AUTHOR]

Depressive disorder (DD) and generalized anxiety disorder (GAD), two prominent mental health conditions, are commonly diagnosed using subjective methods such as scales and interviews. Previous research indicated that machine learning (ML) can enhance our understanding of their underlying mechanisms. This study seeks to investigate the mechanisms of DD, GAD, and healthy controls (HC) while constructing a diagnostic framework for triple classifications. Specifically, the experiment involved collecting electroencephalogram (EEG) signals from 42 DD patients, 45 GAD patients, and 38 HC adults. The Phase Lag Index (PLI) was employed to quantify brain functional connectivity and analyze differences in functional connectivity among three groups. This study also explored the impact of time window feature computations on classification performance, including the XGBoost, CatBoost, LightGBM, and ensemble models. In order to enhance classification performance, a feature optimization algorithm based on Autogluon-Tabular was proposed. The results indicate that a 12 s time window provides optimal classification performance for the three groups, achieving the highest accuracy of 97.33% with the ensemble model. The analysis further reveals a significant reorganization of the brain, with the most pronounced changes observed in the frontal lobe and beta rhythm. These findings support the hypothesis of abnormal brain functional connectivity in DD and GAD, contributing valuable insights into the neural mechanisms underlying DD and GAD. [ABSTRACT FROM AUTHOR]

Walking rehabilitation following injury or disease involves voluntary gait modification, yet the specific brain signals underlying this process remains unclear. This aim of this study was to investigate the impact of an auditory cue on changes in brain activity when walking overground (O) and on a treadmill (T) using an electroencephalogram (EEG) with a 32-electrode montage. Employing a between-group repeated-measures design, 24 participants (age: 25.7 ± 3.8 years) were randomly allocated to either an O (n = 12) or T (n = 12) group to complete two walking conditions (self-selected speed control (sSC) and speed control (SC)). The differences in brain activities during the gait cycle were investigated using statistical non-parametric mapping (SnPM). The addition of an auditory cue did not modify cortical activity in any brain area during the gait cycle when walking overground (all p > 0.05). However, significant differences in EEG activity were observed in the delta frequency band (0.5–4 Hz) within the sSC condition between the O and T groups. These differences occurred at the central frontal (loading phase) and frontocentral (mid stance phase) brain areas (p < 0.05). Our data suggest auditory cueing has little impact on modifying cortical activity during overground walking. This may have practical implications in neuroprosthesis development for walking rehabilitation, sports performance optimization, and overall human quality-of-life improvement. [ABSTRACT FROM AUTHOR]

The severe effects of attention deficit hyperactivity disorder (ADHD) among adolescents can be prevented by timely identification and prompt therapeutic intervention. Traditional diagnostic techniques are complicated and time-consuming because they are subjective-based assessments. Machine learning (ML) techniques can automate this process and prevent the limitations of manual evaluation. However, most of the ML-based models extract few features from a single domain. Furthermore, most ML-based studies have not examined the most effective electrode placement on the skull, which affects the identification process, while others have not employed feature selection approaches to reduce the feature space dimension and consequently the complexity of the training models. This study presents an ML-based tool for automatically identifying ADHD entitled "ADHD-AID". The present study uses several multi-resolution analysis techniques including variational mode decomposition, discrete wavelet transform, and empirical wavelet decomposition. ADHD-AID extracts thirty features from the time and time–frequency domains to identify ADHD, including nonlinear features, band-power features, entropy-based features, and statistical features. The present study also looks at the best EEG electrode placement for detecting ADHD. Additionally, it looks into the location combinations that have the most significant impact on identification accuracy. Additionally, it uses a variety of feature selection methods to choose those features that have the greatest influence on the diagnosis of ADHD, reducing the classification's complexity and training time. The results show that ADHD-AID has provided scores for accuracy, sensitivity, specificity, F1-score, and Mathew correlation coefficients of 0.991, 0.989, 0.992, 0.989, and 0.982, respectively, in identifying ADHD with 10-fold cross-validation. Also, the area under the curve has reached 0.9958. ADHD-AID's results are significantly higher than those of all earlier studies for the detection of ADHD in adolescents. These notable and trustworthy findings support the use of such an automated tool as a means of assistance for doctors in the prompt identification of ADHD in youngsters. [ABSTRACT FROM AUTHOR]

Background: Parkinson's disease is one of the most common neurodegenerative disorders. While a definitive cure for Parkinson's disease remains elusive, a range of treatments are available to slow its progression and counteract its symptoms. Transcranial direct current stimulation (tDCS) represents a non-invasive method to induce brain plasticity. The aim of this study was to examine the effects of two weeks of tDCS on the left dorsolateral prefrontal cortex (DLPFC) on the neurophysiological functioning of Parkinson's patients. Methods: Thirty patients aged between 67 and 82 years with Parkinson's disease participated to the experiment. Fifteen underwent tDCS on the left DLPFC, while fifteen underwent sham tDCS. Neurophysiological functions were assessed before and after tDCS using electroencephalogram methods for alpha and beta band rhythms and P300 event-related potential latency. Results: tDCS led to a reduction in the onset latency of the P300 response and an increase in the power spectrum of the alpha and beta band rhythms. Conclusions: This research enhances our understanding of the potential effects of tDCS in the context of Parkinson's disease treatment, as the reduction in P300 latency and the increase in alpha and beta bands are associated with improvements in cognitive aspects. [ABSTRACT FROM AUTHOR]

Attention deficit hyperactivity disorder (ADHD) is one of the most common mental disorders. This disease includes a combination of disorders in maintaining attention, hyperactivity, and impulsive behaviors. Diagnosis of ADHD is primarily clinical and based on history and examination. This study aims to provide a method for a more accurate diagnosis of adult ADHD using electroencephalography (EEG) signals. EEG signals recorded from 37 ADHD and 42 healthy adults were used as a control group in the age range of 20–68 years. We designed a convolutional neural network with three convolutional layers, three max-pooling layers, and one fully connected layer and trained it using spectrogram images obtained from EEG signals. The Cz channel was used for the diagnosis ADHD in four different states. To evaluate the performance of the proposed method, metrics such as accuracy, sensitivity, specificity, and precision were calculated. The results showed that using only one Cz channel has a good performance in diagnosing of ADHD. The highest accuracy of classification was related to the classification of two groups in the state when their eyes-open and eyes-closed spectrogram images were subtracted from each other. The results showed that proposed method based on deep learning can be a suitable method for diagnosing ADHD. [ABSTRACT FROM AUTHOR]

Introduction: Repetitive transcranial magnetic stimulation (rTMS) is a non-pharmacological treatment for drug-resistant major depressive disorder (MDD) patients. Since the success rate of rTMS treatment is about 50%-55%, it is essential to predict the treatment outcome before starting based on electroencephalogram (EEG) signals, leading to identifying effective biomarkers and reducing the burden of health care centers. Methods: To this end, pretreatment EEG data with 19 channels in the resting state from 34 drug-resistant MDD patients were recorded. Then, all patients received 20 sessions of rTMS treatment, and a reduction of at least 50% in the total beck depression inventory (BDI-II) score before and after the rTMS treatment was defined as a reference. In the current study, effective brain connectivity features were determined by the direct directed transfer function (dDTF) method from patients' pretreatment EEG data in all frequency bands separately. Then, the brain functional connectivity patterns were modeled as graphs by the dDTF method and examined with the local graph theory indices, including degree, out-degree, in-degree, strength, outstrength, in-strength, and betweenness centrality. Results: The results indicated that the betweenness centrality index in the Fp2 node and the δ frequency band are the best biomarkers, with the highest area under the receiver operating characteristic curve value of 0.85 for predicting the rTMS treatment outcome in drug-resistant MDD patients. Conclusion: The proposed method investigated the significant biomarkers that can be used to predict the rTMS treatment outcome in drug-resistant MDD patients and help clinical decisions. [ABSTRACT FROM AUTHOR]

To evaluate clinical characteristics and long-term outcomes in patients with guanidinoacetate methyltransferase (GAMT) deficiency with a special emphasis on seizures and electroencephalography (EEG) findings. We retrospectively analyzed the clinical and molecular characteristics, seizure types, EEG findings, neuroimaging features, clinical severity scores, and treatment outcomes in six patients diagnosed with GAMT deficiency. Median age at presentation and diagnosis were 11.5 months (8–12 months) and 63 months (18 months −11 years), respectively. Median duration of follow-up was 14 years. Global developmental delay (6/6) and seizures (5/6) were the most common symptoms. Four patients presented with febrile seizures. The age at seizure-onset ranged between 8 months and 4 years. Most common seizure types were generalized tonic seizures (n = 4) and motor seizures resulting in drop attacks (n = 3). Slow background activity (n = 5) and generalized irregular sharp and slow waves (n = 3) were the most common EEG findings. Burst-suppression and electrical status epilepticus during slow-wave sleep (ESES) pattern was present in one patient. Three of six patients had drug-resistant epilepsy. Post-treatment clinical severity scores showed improvement regarding movement disorders and epilepsy. All patients were seizure-free in the follow-up. Epilepsy is one of the main symptoms in GAMT deficiency with various seizure types and non-specific EEG findings. Early diagnosis and initiation of treatment are crucial for better seizure and cognitive outcomes. This long-term follow up study highlights to include cerebral creatine deficiency syndromes in the differential diagnosis of patients with global developmental delay and epilepsy and describes the course under treatment. [ABSTRACT FROM AUTHOR]

Epilepsy represents one of the most common neurological diseases that affects a substantial number of individuals worldwide, which is characterized by recurrent, unprovoked seizures detectable via electroencephalogram (EEG). To address this issue, we propose an Extreme Learning Machine (ELM) model for seizure prediction. Firstly, we optimized the ELM with a single hidden-layer feed-forward network using Particle Swarm Optimization (PSO) as a meta-heuristic. Secondly, we employed the Xavier method to initialize random variables and improve model performance. Our optimized classification model was evaluated using a dataset from the University of Bonn. Results from the experiments demonstrate our model's excellent classification performance with 98.13% sensitivity and 91.04% specificity. [ABSTRACT FROM AUTHOR]

ObjectiveThe brain-computer interface (BCI) systems based on rapid serial visual presentation (RSVP) have been widely utilized for the detection of target and non-target images. Collaborative brain-computer interface (cBCI) effectively fuses electroencephalogram (EEG) data from multiple users to overcome the limitations of low single-user performance in single-trial event-related potential (ERP) detection in RSVP-based BCI systems. In a multi-user cBCI system, a superior group mode may lead to better collaborative performance and lower system cost. However, the key factors that enhance the collaboration capabilities of multiple users and how to further use these factors to optimize group mode remain unclear.ApproachThis study proposed a group-member selection strategy to optimize the group mode and improve the system performance for RSVP-based cBCI. In contrast to the conventional grouping of collaborators at random, the group-member selection strategy enabled pairing each user with a better collaborator and allowed tasks to be done with fewer collaborators. Initially, we introduced the maximum individual capability and maximum collaborative capability (MIMC) to select optimal pairs, improving the system classification performance. The sequential forward floating selection (SFFS) combined with MIMC then selected a sub-group, aiming to reduce the hardware and labor expenses in the cBCI system. Moreover, the hierarchical discriminant component analysis (HDCA) was used as a classifier for within-session conditions, and the Euclidean space data alignment (EA) was used to overcome the problem of inter-trial variability for cross-session analysis.Main resultsIn this paper, we verified the effectiveness of the proposed group-member selection strategy on a public RSVP-based cBCI dataset. For the two-user matching task, the proposed MIMC had a significantly higher AUC and TPR and lower FPR than the common random grouping mode and the potential group-member selection method. Moreover, the SFFS with MIMC enabled a trade-off between maintaining performance and reducing the number of system users.SignificanceThe results showed that our proposed MIMC effectively optimized the group mode, enhanced the classification performance in the two-user matching task, and could reduce the redundant information by selecting the sub-group in the RSVP-based multi-user cBCI systems.

Abstract 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.

It has been found that mind wandering can impair motor control. However, it remains unclear whether the impact of mind wandering on motor control is modulated by movement difficulty and its associated neural mechanisms. To address this issue, we manipulated movement difficulty using handedness and finger dexterity separately in two signal-response tasks with identical experiment designs, in which right-handed participants performed key-pressing and key-releasing movements with the specified fingers, and they had to intermittently report whether their attention was “On task” or “Off task.” Key-releasing with the right index finger (RI) had a faster reaction time and stronger contralateral delta-theta (1–7 Hz) functional connectivity than with the left index (LI) in Experiment 1, and mind wandering only reduced the contralateral delta-theta functional connectivity and midfrontal delta-theta activity for key-releasing with RI. Key-pressing with right index and middle fingers (RIR) had a faster reaction time and stronger midfrontal delta-theta activity than with right index and ring fingers (RIR) in Experiment 2, and mind wandering only reduced the midfrontal delta-theta activity for key-pressing with RIM. Theta oscillations are vital in motor control. These findings suggest that mind wandering only impairs the motor control of relatively simple movements without affecting the difficult ones. It supports the notion that mind wandering competes for executive resources with the primary task. Moreover, the quantity of executive resources recruited for a task and how these resources are allocated is contingent upon the task difficulty, which may determine whether mind wandering would interfere with motor control.

Abstract This easy‐to‐follow handbook offers a straightforward guide to electroencephalogram (EEG) analysis using Python, aimed at all EEG researchers in cognitive neuroscience and related fields. It spans from single‐subject data preprocessing to advanced multisubject analyses. This handbook contains four chapters: Preprocessing Single‐Subject Data, Basic Python Data Operations, Multiple‐Subject Analysis, and Advanced EEG Analysis. The Preprocessing Single‐Subject Data chapter provides a standardized procedure for single‐subject EEG data preprocessing, primarily using the MNE‐Python package. The Basic Python Data Operations chapter introduces essential Python operations for EEG data handling, including data reading, storage, and statistical analysis. The Multiple‐Subject Analysis chapter guides readers on performing event‐related potential and time‐frequency analyses and visualizing outcomes through examples from a face perception task dataset. The Advanced EEG Analysis chapter explores three advanced analysis methodologies, Classification‐based decoding, Representational Similarity Analysis, and Inverted Encoding Model, through practical examples from a visual working memory task dataset using NeuroRA and other powerful packages. We designed our handbook for easy comprehension to be an essential tool for anyone delving into EEG data analysis with Python (GitHub website: https://github.com/ZitongLu1996/Python‐EEG‐Handbook; For Chinese version: https://github.com/ZitongLu1996/Python‐EEG‐Handbook‐CN).

Cognitive neuroscience investigates the intricate connections between brain function and mental processing to understand the cognitive architecture. Exploring the human brain, the epicenter of cognitive activity, offers valuable insights into underlying cognitive processes. To monitor brain states corresponding to various mental activities, appropriate measurement tools are essential. Electroencephalogram (EEG) signals serve as a valuable tool for recording patterns and changes in electrical brain activities. Leveraging non-linear signal processing techniques holds promise for advancing our understanding of brain activities during cognitive tasks. In this study, we analyze the electrical activity of the brain using EEG data collected from subjects engaged in a cognitive workload task. Employing wavelet-based analysis, we capture changes in the structure of EEG signals before and during a mental arithmetic task. Additionally, spectral analysis is conducted to discern alterations in the distribution of spectral contents of EEG signals. Our findings underscore the efficacy of wavelet-based analysis and spectral entropy in quantifying the time-varying and non-stationary nature of EEG recordings, offering effective frameworks for distinguishing between different cognitive activities. Consequently, these methods afford deeper insights into the cognitive architecture by tracking changes in the distribution of the time-varying spectrum.

Brain activities are a mixture of periodic and aperiodic components, manifesting in the power spectral density (PSD) as rhythmic oscillations with spectral peaks and broadband fluctuations. Periodic oscillatory properties of brain response to external stimulation are widely studied, while aperiodic component responses remain unclear. Here, we investigate spatiotemporal dynamics of periodic and aperiodic brain activity under peripheral nerve stimulation with acupuncture by parameterization of power spectra of EEG signals. Regarding periodic brain activity, spectral peak in delta band emerges in frontal and central brain regions indicates a typical phenomenon of neural entrainment, which is formed by coupling periodic brain activity to external rhythmic acupuncture stimulation. In addition, the statistical results show that alpha periodic power is an important indicator for characterizing the modulatory effects of acupuncture on periodic brain activity. As for aperiodic brain activity, broadband EEG spectral trend analysis demonstrates a steeper aperiodic slope in left parietal lobe and a stronger negative correlation with the aperiodic offset under acupuncture compared with resting state, with the absolute value of correlation coefficient increasing from 0.27 to 0.50. Based on the two parameters that can best characterize the acupuncture effect, alpha periodic power and aperiodic slope, the accurate decoding of acupuncture manipulation is realized with AUC = 0.87. This work shows the modulatory effect of peripheral nerve stimulation with acupuncture on the brain activity by characterizing the periodic and aperiodic spectrum features of EEG, providing new insights into the comprehensive understanding of the response processes of human brain to acupuncture stimulation.

Automated detection of epileptic seizures from scalp Electroencephalogram (EEG) is crucial for improving epilepsy diagnosis and management. This paper presents an automated inter-patient epileptic seizure detection method using multichannel EEG signals. The proposed method uses a scale mixture-based stochastic EEG model for feature extraction and a recurrent neural network for seizure detection. Specifically, the stochastic model, which accounts for uncertainties in EEG amplitude, is fitted to a specific frequency band to extract relevant seizure features. Then, a recurrent neural network-based recognition architecture learns the temporal evolution of these features. We evaluated our method using EEG data from 20 patients with focal epilepsy and conducted comprehensive assessments, including ablation studies on classifiers and features. Our results demonstrate that our approach outperforms static classifiers and existing feature sets, achieving high sensitivity while maintaining acceptable specificity. Furthermore, our feature set showed efficacy both independently and as a complement to existing features, indicating its robustness in seizure detection tasks. These findings reveal that learning the temporal evolution of the stochastic fluctuation and amplitude information of EEG extracted using a stochastic model enables highly accurate seizure detection, potentially advancing automated epilepsy diagnosis in clinical settings.

Electroencephalogram (EEG) is the graphical representation of Brain’s electrical activity. Mental stress can be detected in many ways and EEG is one of them. Regular mental stress gives rise to many mental disorders and it may cause various physiological and psychological diseases. As a result, early-stage detection of stress is very important. In this research, brain activity was recorded through EEG headset during inducing different levels of stress from audio-visual stimulus. Again, for better interaction between humans and machines, it is essential to analyze the power spectrum of the brain in response to different audio and visual stimulus. To better evaluate visual and auditory stress, an automated system is designed to differentiate among various audio and visual evoked potentials. This may further help for designing different assistive devices for the people having visual and hearing disability. In this paper, we proposed a framework to classify different levels of stress in response to audio and visual stimuli and also classified between these two stimuli by analyzing EEG signals. Raw EEG data was collected in lab environment and the necessary pre-processing steps were applied for denoising. By extracting robust features from the denoised audio and visual data, binary and multi-level stress were classified. A binary classification between audio and visual stimuli was also successfully done in this research. We achieved highest accuracy for binary stress classification 97.14% from visual stimuli, whereas we achieved 94.51% accuracy for auditory stimuli. Again, we achieved the accuracy for four level stress classification 89.59% for visual stimuli and 82.63% for audio stimuli.

Psychological stress represents a prevalent societal concern, particularly evident during crises such as epidemics, which exacerbate various forms of psychological strain. Prompt mitigation of psychological stress holds potential for enhancing quality of life and fostering societal advancement. In this study, drawing inspiration from the International Emotion Picture Library, we crafted four virtual reality (VR) scenes augmented with five emblematic animals, resulting in 20 VR scenarios for psychological stress assessment. Fifteen participants engaged in the test, which was preceded by the completion of the World Health Organization Quality of Life Questionnaire (WHOQOL-BREF). Throughout the test, EEG and heart rate data were meticulously recorded. Following each scenario, participants completed the Self-Assessment Manikin (SAM) scale, culminating in the formation of the VR psychological stress reduction dataset. We meticulously analyze the SAM scale. For EEG data, we devised a deep learning model leveraging a hybrid CNN-LSTM architecture for signal classification. The model was evaluated on both the publicly available DEAP dataset and our collected DSRP dataset, yielding superior performance. Leveraging the dataset amassed in this study, we scrutinize the alleviative impact of VR on human psychological stress, providing foundational insights for VR-based psychological stress reduction research. Moreover, the EEG classification model proposed herein enhances the categorization of EEG signals, thereby advancing the domain of emotion recognition grounded in EEG technology.

The classification of emotions using electroencephalography (EEG) signals is inherently challenging due to the intricate nature of brain activity. Overcoming inconsistencies in EEG signals and establishing a universally applicable sentiment analysis model are essential objectives. This study introduces an innovative approach to cross-subject emotion recognition, employing a genetic algorithm (GA) to eliminate non-informative frames. Then, the optimal frames identified by the GA undergo spatial feature extraction using common spatial patterns (CSP) and the logarithm of variance. Subsequently, these features are input into a Transformer network to capture spatial-temporal features, and the emotion classification is executed using a fully connected (FC) layer with a Softmax activation function. Therefore, the innovations of this paper include using a limited number of channels for emotion classification without sacrificing accuracy, selecting optimal signal segments using the GA, and employing the Transformer network for high-accuracy and high-speed classification. The proposed method undergoes evaluation on two publicly accessible datasets, SEED and SEED-V, across two distinct scenarios. Notably, it attains mean accuracy rates of 99.96% and 99.51% in the cross-subject scenario, and 99.93% and 99.43% in the multi-subject scenario for the SEED and SEED-V datasets, respectively. Noteworthy is the outperformance of the proposed method over the state-of-the-art (SOTA) in both scenarios for both datasets, thus underscoring its superior efficacy. Additionally, comparing the accuracy of individual subjects with previous works in cross subject scenario further confirms the superiority of the proposed method for both datasets.

Affective data is the basis of emotion recognition, which is mainly acquired through extrinsic elicitation. To investigate the enhancing effects of multi-sensory stimuli on emotion elicitation and emotion recognition, we designed an experimental paradigm involving visual, auditory, and olfactory senses. A multimodal emotional dataset (OVPD-II) that employed the video-only or video-odor patterns as the stimuli materials, and recorded the electroencephalogram (EEG) and electrooculogram (EOG) signals, was created. The feedback results reported by subjects after each trial demonstrated that the video-odor pattern outperformed the video-only pattern in evoking individuals’ emotions. To further validate the efficiency of the video-odor pattern, the transformer was employed to perform the emotion recognition task, where the highest accuracy reached 86.65% (66.12%) for EEG (EOG) modality with the video-odor pattern, which improved by 1.42% (3.43%) compared with the video-only pattern. What’s more, the hybrid fusion (HF) method combined with the transformer and joint training was developed to improve the performance of the emotion recognition task, which achieved classify accuracies of 89.50% and 88.47% for the video-odor and video-only patterns, respectively.

Researchers suggest that a short video clip, when tagged with a single label, is sufficient for classification into an emotional category. However, when subjects view an emotional film tagged similarly, there is no guarantee that the designated emotion persists throughout the duration, or when exactly the emotion is elicited. This inconsistency adversely affects the performance of emotion recognition (ER) systems. In this study, we propose a multimodal ER system employing an eye-tracking gated strategy to identify the most effective timing for emotional categorization. Initially, common eye-tracking features are extracted and selected using the minimum-redundancy-maximum-relevance (mRMR) method. Subsequently, the most discriminative feature is employed as a threshold to pinpoint the most relevant timing. EEG signals from these moments are then decomposed into five standard frequency bands using the Daubechies wavelet function (order 4). Furthermore, four types of entropy features are extracted from four-second segments of 62 and 32 channels for the SEED-IV and MAHNOB-HCI databases, respectively. The best features, as determined by the mRMR method, are fed into a Sugeno-fuzzy inference system (S-FIS) designed to derive rules for discriminating between the four emotional categories of happiness, fear, sadness, and neutrality. The S-FIS rules were refined using a genetic algorithm (GA), leading to most discriminative rules achieving average accuracies of 94.42% and 85.20% for the alpha frequency bands of the SEED-IV and MAHNOB-HCI databases, respectively. The results of this study demonstrate the effectiveness of fuzzy rule extraction in enhancing the performance of multimodal ER systems

The daily experience of mental stress profoundly influences our health and work performance while concurrently triggering alterations in brain electrical activity. Electroencephalogram (EEG) is a widely adopted method for assessing cognitive and affective states. This study delves into the EEG correlates of stress and the potential use of resting EEG in evaluating stress levels. Over 13 weeks, our longitudinal study focuses on the real-life experiences of college students, collecting data from each of the 18 participants across multiple days in classroom settings. To tackle the complexity arising from the multitude of EEG features and the imbalance in data samples across stress levels, we use the sequential backward selection (SBS) method for feature selection and the adaptive synthetic (ADASYN) sampling algorithm for imbalanced data. Our findings unveil that delta and theta features account for approximately 50% of the selected features through the SBS process. In leave-one-out (LOO) cross-validation, the combination of band power and pair-wise coherence (COH) achieves a maximum balanced accuracy of 94.8% in stress-level detection for the above daily stress dataset. Notably, using ADASYN and borderline synthesized minority over-sampling technique (borderline-SMOTE) methods enhances model accuracy compared to the traditional SMOTE approach. These results provide valuable insights into using EEG signals for assessing stress levels in real-life scenarios, shedding light on potential strategies for managing stress more effectively.

Numerous studies have shown that musical stimulation can activate corresponding functional brain areas. Electroencephalogram (EEG) activity during musical stimulation can be used to assess the consciousness states of patients with disorders of consciousness (DOC). In this study, a musical stimulation paradigm and verifiable criteria were used for consciousness assessment. Twenty-nine participants (13 healthy subjects, 6 patients in a minimally conscious state (MCS) and 10 patients in a vegetative state (VS)) were recruited, and EEG signals were collected while participants listened to preferred and relaxing music. Fusion features based on differential entropy (DE), common spatial pattern (CSP), and EEG-based network pattern (ENP) features were extracted from EEG signals, and a convolutional neural network-long short-term memory (CNN-LSTM) model was employed to classify preferred and relaxing music.The results showed that the average classification accuracy for healthy subjects reached 85.58%. For two of the patients in the MCS group, the classification accuracies reached 78.18% and 66.14%, and they were diagnosed with emergence from MCS (EMCS) two months later. The accuracies of three patients in the VS group were 58.18%, 64.32% and 62.05%, with two patients showing slight increases in scale scores. Our study suggests that musical stimulation could be an effective method for consciousness detection, with significant diagnostic implications for patients with DOC.

Although three-dimensional visual training (3DVT) has been used for myopia intervention, its neural mechanisms remain largely unknown. In this study, visual function was examined before and after 3DVT, while resting-state EEG-fNIRS signals were recorded from 38 myopic participants. A graph theoretical analysis was applied to compute the neurovascular properties, including static brain networks (SBNs), dynamic brain networks (DBNs), and dynamic neurovascular coupling (DNC). Correlations between the changes in neurovascular properties and the changes in visual functions were calculated. After 3DVT, the local efficiency and node efficiency in the frontal lobes increased in the SBNs constructed from EEG $\delta $ -band; the global efficiency and node efficiency in the frontal-parietal lobes decreased in the DBNs variability constructed from EEG $\delta $ -band. For the DNC constructed with EEG $\alpha $ -band and oxyhemoglobin (HbO), the local efficiency decreased, for EEG $\alpha $ -band and deoxyhemoglobin (HbR), the node efficiency in the frontal-occipital lobes decreased. For the SBNs constructed from HbO, the functional connectivity (FC) between the frontal-occipital lobes increased. The DNC constructed between the FC of the frontal-parietal lobes from EEG $\beta $ -band and the FC of the frontal-occipital lobes from HbO increased, and between the FC of the frontal-occipital lobes from EEG $\beta $ -band and the FC of the inter-frontal lobes from HbR increased. The neurovascular properties were significantly correlated with the amplitude of accommodation and accommodative facility. The result indicated the positive effects of 3DVT on myopic participants, including improved efficiency of brain networks, increased FC of SBNs and DNC, and enhanced binocular accommodation functions.

Electroencephalogram (EEG) signals are widely utilized in the field of cognitive workload decoding (CWD). However, when the recognition scenario is shifted from subject-dependent to subject-independent or spans a long period, the accuracy of CWD deteriorates significantly. Current solutions are either dependent on extensive training datasets or fail to maintain clear distinctions between categories, additionally lacking a robust feature extraction mechanism. In this paper, we tackle these issues by proposing a Bi-Classifier Joint Domain Adaptation (BCJDA) model for EEG-based cross-time and cross-subject CWD. Specifically, the model consists of a feature extractor, a domain discriminator, and a Bi-Classifier, containing two sets of adversarial processes for domain-wise alignment and class-wise alignment. In the adversarial domain adaptation, the feature extractor is forced to learn the common domain features deliberately. The Bi-Classifier also fosters the feature extractor to retain the category discrepancies of the unlabeled domain, so that its classification boundary is consistent with the labeled domain. Furthermore, different adversarial distance functions of the Bi-Classifier are adopted and evaluated in this model. We conduct classification experiments on a publicly available BCI competition dataset for recognizing low, medium, and high cognitive workload levels. The experimental results demonstrate that our proposed BCJDA model based on cross-gradient difference maximization achieves the best performance.