EEG Signal Patterns Research Articles

Automatic detection of epileptic seizure based on one dimensional cascaded convolutional autoencoder with adaptive window-thresholding.

Identifying the Seizure Occurrence Period (SOP) in extended EEG recordings is crucial for neurologists to diagnose seizures effectively. However, many existing computer-aided diagnosis systems (CADs) for Epileptic Seizure Detection (ESD) primarily focus on distinguishing between ictal and interictal states in EEG recordings. This focus has limited their application in clinical settings, as these systems typically rely on super vised learning approaches that require labeled data. To address this, our study introduces an unsupervised learning framework for ESD using a 1D-Cascaded Convolutional Autoencoder (1D-CasCAE). In this approach, EEG recordings from selected patients in the CHB-MIT datasets are first segmented into 5-second epochs. Eight informative channels are chosen based on the correlation coefficient and Shannon entropy. The 1D-CasCAE is designed to autonomously learn the characteristic patterns of interictal (non-seizure) segments through downsampling and upsampling processes. The integration of adaptive thresholding and a moving window significantly enhances the model's robustness, enabling it to accurately identify ictal segments in long EEG recordings. Experimental results demonstrate that the proposed 1D-CasCAE effectively learns normal EEG signal patterns and efficiently detects anomalies (ictal segments) using reconstruction error. When compared with other leading methods in anomaly detection, our model exhibits superior performance, as evidenced by its average Gmean, sensitivity, specificity, preci sion, and false positive rate scores of 98.00±3.51%, 94.94±6.92%, 99.60±0.30%, 79.92±13.56% and 0.0044±0.0030/h of a typical patient in CHB-MIT datasets. Finally, the developed model framework can be employed in clinical settings, replacing the manual inspection process of EEG signals by neurologists. This automated system can adapt to each patient's SOP through the use of variable time windows for seizure detection.

Graph signal processing and graph learning approaches to Schizophrenia pattern identification in brain Electroencephalogram

The detection of Schizophrenia (SZ) directly from brain Electroencephalogram (EEG) signals has recently gained importance. Traditionally, EEG-based SZ detection is done by either manually extracting features from each EEG channel and using Machine Learning classifiers or by Deep Learning-based automated detection. These methods neglect the functional interactions between spatially distributed brain regions. This study proposes a graph signal processing and graph learning framework to detect SZ. Here, the individual channels’ features and the global interactions are combined into a unified graph signal (GS) representation. The proposed representation consists of the GS values and the underlying connectivity network. An SZ-specific feature, namely, the permutation entropies of each electrode’s signal, is considered as the GS values. The graph learning method learns the underlying network from a collection of GS observations. The learned network serves as the Fourier basis for the graph Fourier and wavelet transform. Utilizing these transforms, the graph spectral features are extracted from GSs. Lastly, the extracted features are classified using a set of Machine Learning models. The proposed graph signal processing and graph learning method is an effective approach to decode Schizophrenia patterns in brain EEG signals and outperform the existing approaches.

Latent Prototype-Based Clustering: A Novel Exploratory Electroencephalography Analysis Approach.

Electroencephalography (EEG)-based applications in brain-computer interfaces (BCIs), neurological disease diagnosis, rehabilitation, etc., rely on supervised approaches such as classification that requires given labels. However, with the ever-increasing amount of EEG data, incomplete or incorrectly labeled or unlabeled EEG data are increasing. It likely degrades the performance of supervised approaches. In this work, we put forward a novel unsupervised exploratory EEG analysis solution by clustering based on low-dimensional prototypes in latent space that are associated with the respective clusters. Having the prototype as a baseline of each cluster, a compositive similarity is defined to act as the critic function in clustering, which incorporates similarities on three levels. The approach is implemented with a Generative Adversarial Network (GAN), termed W-SLOGAN, by extending the Stein Latent Optimization for GANs (SLOGAN). The Gaussian Mixture Model (GMM) is utilized as the latent distribution to adapt to the diversity of EEG signal patterns. The W-SLOGAN ensures that images generated from each Gaussian component belong to the associated cluster. The adaptively learned Gaussian mixing coefficients make the model remain effective in dealing with an imbalanced dataset. By applying the proposed approach to two public EEG or intracranial EEG (iEEG) epilepsy datasets, our experiments demonstrate that the clustering results are close to the classification of the data. Moreover, we present several findings that were discovered by intra-class clustering and cross-analysis of clustering and classification. They show that the approach is attractive in practice in the diagnosis of the epileptic subtype, multiple labelling of EEG data, etc.

Relationships between Electroencephalogram and Thermal Perception of Passenger in Winter Vehicle Compartments

The development of electric vehicles (EVs) has prompted a critical examination of the trade-off between range and human thermal comfort. Therefore, an accurate, real-time assessment of human thermal perception inside vehicles is important. This study investigates an electroencephalogram- (EEG-) based method for evaluating human thermal comfort in the vehicle passenger compartment. Under transient winter heating conditions, the study experimentally investigates the correlation between objective physiological parameters (skin temperature and electroencephalogram) and subjective human thermal perception. The results reveal distinct patterns in EEG signals corresponding to changes in thermal perception. Specifically, the δ rhythm exhibits a U-shape variation with increasing thermal perception, while the θ, α, β, and γ rhythms display an inverted U-shape variation. Differences in each frequency band across thermal comfort states in humans are greater than differences in the frequency band across thermal sensation states. Furthermore, the relative power of the θ rhythm emerges as the most effective in discerning the thermal perception state of the human body. The EEG signal characteristics of the T7 and T8 channels align more closely with human thermal sensation, whereas the AF4 channel excels at discriminating the state of human thermal comfort. The insights gained from this study serve as a foundation for evaluating human thermal perception in vehicles, enhancing human-vehicle interaction, and addressing challenges related to human thermal comfort and vehicle range.

Online continual decoding of streaming EEG signal with a balanced and informative memory buffer

Electroencephalography (EEG) based Brain Computer Interface (BCI) systems play a significant role in facilitating how individuals with neurological impairments effectively interact with their environment. In real world applications of BCI system for clinical assistance and rehabilitation training, the EEG classifier often needs to learn on sequentially arriving subjects in an online manner. As patterns of EEG signals can be significantly different for different subjects, the EEG classifier can easily erase knowledge of learnt subjects after learning on later ones as it performs decoding in online streaming scenario, namely catastrophic forgetting. In this work, we tackle this problem with a memory-based approach, which considers the following conditions: (1) subjects arrive sequentially in an online manner, with no large scale dataset available for joint training beforehand, (2) data volume from the different subjects could be imbalanced, (3) decoding difficulty of the sequential streaming signal vary, (4) continual classification for a long time is required. This online sequential EEG decoding problem is more challenging than classic cross subject EEG decoding as there is no large-scale training data from the different subjects available beforehand. The proposed model keeps a small balanced memory buffer during sequential learning, with memory data dynamically selected based on joint consideration of data volume and informativeness. Furthermore, for the more general scenarios where subject identity is unknown to the EEG decoder, aka. subject agnostic scenario, we propose a kernel based subject shift detection method that identifies underlying subject changes on the fly in a computationally efficient manner. We develop challenging benchmarks of streaming EEG data from sequentially arriving subjects with both balanced and imbalanced data volumes, and performed extensive experiments with a detailed ablation study on the proposed model. The results show the effectiveness of our proposed approach, enabling the decoder to maintain performance on all previously seen subjects over a long period of sequential decoding. The model demonstrates the potential for real-world applications.

Grey Level Differences Matrix for Alcoholic EEG Signal Classification

Electroencephalogram (EEG) signals can provide information on abnormalities in a person's brain and characterize brain activity. Brain injury or diseases can manifest as brain disorders. Trauma or the use of specific chemicals or medications, such as alcohol, can result in brain damage. Previous research has demonstrated variations in the patterns of EEG signals between alcohol-using and non-drinking people. Various techniques, including wavelet and entropy, have been developed to detect alcoholic EEG using event-related potential (ERP) testing. This work proposes a feature extraction technique based on texture analysis for the classification of alcohol EEG signals because ERP-measured EEG often involves many channels. An NxM image is thought to be equivalent to an EEG signal with N channels and a recording duration of M samples. The NxM matrix is formed by channelizing the N-channel EEG signal in this investigation. Normalization is then used to get a matrix value of 0-255 or an 8-bit image in the following step. Five features are measured in four directions, and the Grey Level Difference Matrix (GLDM) approach is utilized for feature extraction. Using five grey-level difference matrix (GLDM) features and linear discriminant analysis as a classifier, the maximum accuracy was achieved at 73.3%. Image processing can still be used to increase accuracy even though the final product is less accurate than the earlier technique. The suggested approach can still be adjusted to work with biomedical signals or image processing techniques like the Grey Level Co-occurrence Matrix (GLCM).

EEG-based emotion recognition using MobileNet Recurrent Neural Network with time-frequency features

Despite the developments in deep learning, extracting different features from brain signals remains a crucial challenge in EEG-based emotion recognition. This study introduces a novel methodology to overcome the challenge of capturing temporal and channel-related features. The basic idea of the proposed method is to use scalograms to capture subtle emotional patterns in EEG signals, followed by MobileNet Recurrent Neural Network (MRNN). Unlike conventional methods, MRNN utilizes recurrent connections to learn sequential patterns and recognize complex temporal dependencies in the data, enhancing EEG-based emotion recognition. The model is trained and tested on two publicly available datasets, DEAP and DREAMER, and achieved better results at 85.95 % for 15 classes and 96.29 % for 9 classes accuracy on average. Our approach extends beyond binary or limited multi-class emotion classification and significantly improves accuracy compared to the state-of-the-art methods. This research can have potential applications in developing emotion recognition systems for real-world scenarios, such as mental health monitoring or human-computer interaction.

Dynamic topological data analysis: a novel fractal dimension-based testing framework with application to brain signals.

Topological data analysis (TDA) is increasingly recognized as a promising tool in the field of neuroscience, unveiling the underlying topological patterns within brain signals. However, most TDA related methods treat brain signals as if they were static, i.e., they ignore potential non-stationarities and irregularities in the statistical properties of the signals. In this study, we develop a novel fractal dimension-based testing approach that takes into account the dynamic topological properties of brain signals. By representing EEG brain signals as a sequence of Vietoris-Rips filtrations, our approach accommodates the inherent non-stationarities and irregularities of the signals. The application of our novel fractal dimension-based testing approach in analyzing dynamic topological patterns in EEG signals during an epileptic seizure episode exposes noteworthy alterations in total persistence across 0, 1, and 2-dimensional homology. These findings imply a more intricate influence of seizures on brain signals, extending beyond mere amplitude changes.

EEG microstates are associated with music training experience.

Music training facilitates the development of individual cognitive functions and influences brain plasticity. A comprehensive understanding of the pathways and processes through which music affects the human brain, as well as the neurobiological mechanisms underlying human brain perception of music, is necessary to fully harness the plasticity that music offers for brain development. To investigate the resting-state electroencephalogram (EEG) activity of individuals with and without music training experience, and explore the microstate patterns of EEG signals. In this study, an analysis of electroencephalogram (EEG) microstates from 57 participants yielded temporal parameters(mean duration, time coverage, occurrence, and transition probability)of four classic microstate categories (Categories A, B, C, and D) for two groups: those with music training experience and those without. Statistical analysis was conducted on these parameters between groups. The results indicate that compared to individuals without music training experience, participants with music training experience exhibit significantly longer mean durations of microstate A, which is associated with speech processing. Additionally, they show a greater time coverage of microstate B, which is associated with visual processing. Transition probabilities from microstate A to microstate B were greater in participants with music training experience compared to those without. Conversely, transition probabilities from microstate A to microstate C and from microstate C to microstate D were greater in participants without music training experience. Our study found differences in characteristic parameters of certain microstates between individuals with and without music training experience. This suggests distinct brain activity patterns during tasks related to speech, vision, and attention regulation among individuals with varying levels of music training experience. These findings support an association between music training experience and specific neural activities. Furthermore, they endorse the hypothesis of music training experience influencing brain activity during resting states. Additionally, they imply a facilitative role of music training in tasks related to speech, vision, and attention regulation, providing initial evidence for further empirical investigation into the cognitive processes influenced by music training.

Brain Topology Modeling With EEG-Graphs for Auditory Spatial Attention Detection.

Despite recent advances, the decoding of auditory attention from brain signals remains a challenge. A key solution is the extraction of discriminative features from high-dimensional data, such as multi-channel electroencephalography (EEG). However, to our knowledge, topological relationships between individual channels have not yet been considered in any study. In this work, we introduced a novel architecture that exploits the topology of the human brain to perform auditory spatial attention detection (ASAD) from EEG signals. We propose EEG-Graph Net, an EEG-graph convolutional network, which employs a neural attention mechanism. This mechanism models the topology of the human brain in terms of the spatial pattern of EEG signals as a graph. In the EEG-Graph, each EEG channel is represented by a node, while the relationship between two EEG channels is represented by an edge between the respective nodes. The convolutional network takes the multi-channel EEG signals as a time series of EEG-graphs and learns the node and edge weights from the contribution of the EEG signals to the ASAD task. The proposed architecture supports the interpretation of the experimental results by data visualization. We conducted experiments on two publicly available databases. The experimental results showed that EEG-Graph Net significantly outperforms the state-of-the-art methods in terms of decoding performance. In addition, the analysis of the learned weight patterns provides insights into the processing of continuous speech in the brain and confirms findings from neuroscientific studies. We showed that modeling brain topology with EEG-graphs yields highly competitive results for auditory spatial attention detection. The proposed EEG-Graph Net is more lightweight and accurate than competing baselines and provides explanations for the results. Also, the architecture can be easily transferred to other brain-computer interface (BCI) tasks.

Study on different brain activation rearrangement during cognitive workload from ERD/ERS and coherence analysis.

The functional activities of the brain during any task like imaginary, motor, or cognitive are different in pattern as well as their area of activation in the brain is also different. This variation in pattern is also found in the brain's electrical variations that can be measured from the scalp of the brain using an electroencephalogram (EEG). This work exclusively studied a group of subjects' EEG data (available at: https://archive.physionet.org/physiobank/database/eegmat/) to unravel the activation pattern of the human brain during a mental arithmetic task. Since any cognitive task creates variations in EEG signal pattern, the relative changes in the signal power also occur which is also known as event-related desynchronization/synchronization (ERD/ERS). In this work, ERD/ERS have calculated the band-wise power spectral density (PSD) using Welch's method from the EEG signals. Besides, the coherence analysis was also performed to verify the results of ERD/ERS analysis from several randomly chosen subjects' EEG data. Here, subjects performing mental arithmetic tasks were grouped based on their performances: good (subtraction solved > 10 on average) and bad (subtraction solved ≤ 10 on average) to conduct group-specific ERD/ERS analysis regarding their performance in cognitive tasks. It was found that when the brain is on count condition, the variations found in the band power of theta and beta. The amounts of ERS in the left hemisphere are increased. When the task complexity increases, it contributes to an increase in relative ERD/ERS amounts and durations. The left and right hemispheres were asymmetrically distributed by both the pre-stimulus stages of the corresponding band power and relative ERD/ERS.

Tensor-CSPNet: A Novel Geometric Deep Learning Framework for Motor Imagery Classification.

Deep learning (DL) has been widely investigated in a vast majority of applications in electroencephalography (EEG)-based brain-computer interfaces (BCIs), especially for motor imagery (MI) classification in the past five years. The mainstream DL methodology for the MI-EEG classification exploits the temporospatial patterns of EEG signals using convolutional neural networks (CNNs), which have been particularly successful in visual images. However, since the statistical characteristics of visual images depart radically from EEG signals, a natural question arises whether an alternative network architecture exists apart from CNNs. To address this question, we propose a novel geometric DL (GDL) framework called Tensor-CSPNet, which characterizes spatial covariance matrices derived from EEG signals on symmetric positive definite (SPD) manifolds and fully captures the temporospatiofrequency patterns using existing deep neural networks on SPD manifolds, integrating with experiences from many successful MI-EEG classifiers to optimize the framework. In the experiments, Tensor-CSPNet attains or slightly outperforms the current state-of-the-art performance on the cross-validation and holdout scenarios in two commonly used MI-EEG datasets. Moreover, the visualization and interpretability analyses also exhibit the validity of Tensor-CSPNet for the MI-EEG classification. To conclude, in this study, we provide a feasible answer to the question by generalizing the DL methodologies on SPD manifolds, which indicates the start of a specific GDL methodology for the MI-EEG classification.

Temporal-frequency-phase feature classification using 3D-convolutional neural networks for motor imagery and movement.

Recently, convolutional neural networks (CNNs) have been widely applied in brain-computer interface (BCI) based on electroencephalogram (EEG) signals. Due to the subject-specific nature of EEG signal patterns and the multi-dimensionality of EEG features, it is necessary to employ appropriate feature representation methods to enhance the decoding accuracy of EEG. In this study, we proposed a method for representing EEG temporal, frequency, and phase features, aiming to preserve the multi-domain information of EEG signals. Specifically, we generated EEG temporal segments using a sliding window strategy. Then, temporal, frequency, and phase features were extracted from different temporal segments and stacked into 3D feature maps, namely temporal-frequency-phase features (TFPF). Furthermore, we designed a compact 3D-CNN model to extract these multi-domain features efficiently. Considering the inter-individual variability in EEG data, we conducted individual testing for each subject. The proposed model achieved an average accuracy of 89.86, 78.85, and 63.55% for 2-class, 3-class, and 4-class motor imagery (MI) classification tasks, respectively, on the PhysioNet dataset. On the GigaDB dataset, the average accuracy for 2-class MI classification was 91.91%. For the comparison between MI and real movement (ME) tasks, the average accuracy for the 2-class were 87.66 and 80.13% on the PhysioNet and GigaDB datasets, respectively. Overall, the method presented in this paper have obtained good results in MI/ME tasks and have a good application prospect in the development of BCI systems based on MI/ME.

Efficient novel network and index for alcoholism detection from EEGs.

Alcoholism is a catastrophic condition that causes brain damage as well as neurological, social, and behavioral difficulties. This illness is often assessed using the Cut down, Annoyed, Guilty, and Eye-opener examination technique, which assesses the intensity of an alcohol problem. This technique is protracted, arduous, error-prone, and errant. As a result, the intention of this paper is to design a cutting-edge system for automatically identifying alcoholism utilizing electroencephalography (EEG) signals, that can alleviate these problems and aid practitioners and investigators. First, we investigate the feasibility of using the Fast Walsh-Hadamard transform of EEG signals to explore the unpredictable essence and variability of EEG indicators in the suggested framework. Second, thirty-six linear and nonlinear features for deciphering the dynamic pattern of healthy and alcoholic EEG signals are discovered. Subsequently, we suggested a strategy for selecting powerful features. Finally, nineteen machine learning algorithms and five neural network classifiers are used to assess the overall performance of selected attributes. The extensive experiments show that the suggested method provides the best classification efficiency, with 97.5% accuracy, 96.7% sensitivity, and 98.3% specificity for the features chosen using the correlation-based FS approach with Recurrent Neural Networks. With recently introduced matrix determinant features, a classification accuracy of 93.3% is also attained. Moreover, we developed a novel index that uses clinically meaningful features to differentiate between healthy and alcoholic categories with a unique integer. This index can assist health care workers, commercial companies, and design engineers in developing a real-time system with 100% classification results for the computerized framework.

The effects of layer-wise relevance propagation-based feature selection for EEG classification: a comparative study on multiple datasets.

The brain-computer interface (BCI) allows individuals to control external devices using their neural signals. One popular BCI paradigm is motor imagery (MI), which involves imagining movements to induce neural signals that can be decoded to control devices according to the user's intention. Electroencephalography (EEG) is frequently used for acquiring neural signals from the brain in the fields of MI-BCI due to its non-invasiveness and high temporal resolution. However, EEG signals can be affected by noise and artifacts, and patterns of EEG signals vary across different subjects. Therefore, selecting the most informative features is one of the essential processes to enhance classification performance in MI-BCI. In this study, we design a layer-wise relevance propagation (LRP)-based feature selection method which can be easily integrated into deep learning (DL)-based models. We assess its effectiveness for reliable class-discriminative EEG feature selection on two different publicly available EEG datasets with various DL-based backbone models in the subject-dependent scenario. The results show that LRP-based feature selection enhances the performance for MI classification on both datasets for all DL-based backbone models. Based on our analysis, we believe that it can broad its capability to different research domains.

Improving EEG major depression disorder classification using FBSE coupled with domain adaptation method based machine learning algorithms

Major depression disorder (MDD) has become the leading mental disorder worldwide. Medical reports have shown that people with depression exhibit abnormal wave patterns in EEG signals compared with the healthy subjects when they are exposed to positive and negative stimuli. In this paper, we proposed an intelligent MDD detection model based on Fourier-Bessel series expansion (FBSE) coupled with domain adaptation (DA). First, EEG signals are segmented into intervals and each segment is passed through FBSE. Two types of features, including statistical and nonlinear features are investigated and extracted from each FBSE coefficient to detect MDD. Student t-test and Wilcoxon test are employed to remove noisy and bad features that can compromise the performance of data-driven learners. Then, DA method named Independence Domain Adaptation was applied to reduce the difference of feature distributions among subjects. The selected features are sent to a least square support vector machine (LS-SVM), and other classifiers named SVM, k-nearest (KNN), ransom forest, Bagged ensemble, boosted ensemble, decision tree, gradient boosting and stacked ensemble for the comparison purpose. Our experiments are simulated by using publicly available dataset. The performance of the proposed model is evaluated in both subject dependence experiment by 10-fold cross validation, subject independence experiment by leave-one-subject-out cross-validation, and Confidence interval respectively. Results showed that the features reduction method can significantly improve the mean accuracy by 4.20. The proposed model is compared with previous studies and the results show that the proposed model outperforms the other methods.

Exploring deep residual network based features for automatic schizophrenia detection from EEG

Schizophrenia is a severe mental illness which can cause lifelong disability. Most recent studies on the Electroencephalogram (EEG)-based diagnosis of schizophrenia rely on bespoke/hand-crafted feature extraction techniques. Traditional manual feature extraction methods are time-consuming, imprecise, and have a limited ability to balance accuracy and efficiency. Addressing this issue, this study introduces a deep residual network (deep ResNet) based feature extraction design that can automatically extract representative features from EEG signal data for identifying schizophrenia. This proposed method consists of three stages: signal pre-processing by average filtering method, extraction of hidden patterns of EEG signals by deep ResNet, and classification of schizophrenia by softmax layer. To assess the performance of the obtained deep features, ResNet softmax classifier and also several machine learning (ML) techniques are applied on the same feature set. The experimental results for a Kaggle schizophrenia EEG dataset show that the deep features with support vector machine classifier could achieve the highest performances (99.23% accuracy) compared to the ResNet classifier. Furthermore, the proposed model performs better than the existing approaches. The findings suggest that our proposed strategy has capability to discover important biomarkers for automatic diagnosis of schizophrenia from EEG, which will aid in the development of a computer assisted diagnostic system by specialists.

Exploring the Intrinsic Features of EEG Signals via Empirical Mode Decomposition for Depression Recognition.

Depression is a severe psychiatric illness that causes emotional and cognitive impairment and has a considerable impact on patients' thoughts, behaviors, feelings and well-being. Moreover, methods for recognizing and treating depression are lacking in clinical practice. Electroencephalogram (EEG) signals, which objectively reflect the internal workings of the brain, is a promising and objective tool for recognizing and diagnosing of depression and enhancing clinical effects. However, previous EEG feature extraction methods have not performed well when exploring the intrinsic characteristics of highly complex and nonstationary EEG signals. To address this issue, we propose a regularization parameter-based improved intrinsic feature extraction method of EEG signals via empirical mode decomposition (EMD), which mines the intrinsic patterns in EEG signals, for depression recognition. Furthermore, our method can effectively solve the problem that EMD fails to extract intrinsic features. In this method, we first select an appropriate regularization parameter to generate the regularization matrix. Next, we calculate the sum of the matrix products of the IMFs and the regularization matrix and leverage the inverse of this matrix to extract the intrinsic features. The classification results of our method on four EEG datasets reached 0.8750, 0.8850, 0.8485 and 0.7768, respectively. In addition, compared with the iEMD method, our method requires less computational costs. These results support our claim that our method can effectively strengthen the depression recognition performance, and our method outperforms state-of-the-art feature extraction approaches.

EEG-Based Person Identification during Escalating Cognitive Load.

With the development of human society, there is an increasing importance for reliable person identification and authentication to protect a person’s material and intellectual property. Person identification based on brain signals has captured substantial attention in recent years. These signals are characterized by original patterns for a specific person and are capable of providing security and privacy of an individual in biometric identification. This study presents a biometric identification method based on a novel paradigm with accrual cognitive brain load from relaxing with eyes closed to the end of a serious game, which includes three levels with increasing difficulty. The used database contains EEG data from 21 different subjects. Specific patterns of EEG signals are recognized in the time domain and classified using a 1D Convolutional Neural Network proposed in the MATLAB environment. The ability of person identification based on individual tasks corresponding to a given degree of load and their fusion are examined by 5-fold cross-validation. Final accuracies of more than 99% and 98% were achieved for individual tasks and task fusion, respectively. The reduction of EEG channels is also investigated. The results imply that this approach is suitable to real applications.

Search for EEG signal patterns in simulating phobic anxiety disorder situations in a VR environment

Introduction: Effective and prompt formulation of diagnostic conclusions about the presence of anxiety-phobic disorders requires the improvement of existing and the development of new methods for diagnosing and treating patients, including the use of virtual reality technology. Purpose: To analyze a reaction of an individual to a stimulus that triggers a fear response to virtual reality scenes (height exposure). To identify electroencephalographic (EEG) signal markers related to the level of anxiety and virtual reality environment susceptibility of an individual. Methods: A group of nine conditionally healthy males aged 23 to 26 years old who reported neither history of somatic symptoms nor organic brain disorders was formed to conduct the research. The immersion into virtual reality was accompanied by the registration of EEG signals and subsequent completion of a self-assessment questionnaire by the subjects. Results: The state of rest (a reference value) and the state of high emotional stress experience (at the height of a skyscraper) in the virtual reality environment were compared. The results obtained allow to make a conclusion that the simulated situation of being at a height causes a decrease in the indices of alpha, theta, beta rhythms, and an increase in the delta rhythm index of the EEG signal relative to the state of rest in various subjects, regardless of the intensity of fear manifestation. Practical relevance: The conducted research is among the pioneering studies in assessing the effect of virtual reality technologies on human phobic anxiety state. Some objective electrophysiological markers related to the level of anxiety were determined to confirm the presence of patterns in the functional state of the cerebral cortex with a sense of anxiety in individuals immersed in a virtual reality environment.