In recent years, driver fatigue has become one of the main causes of road accidents. As a result, fatigue detection systems have been developed to warn drivers, and, among the available methods, EEG signal analysis is recognized as the most reliable method for detecting driver fatigue. This study presents an automated system for a two-stage classification of driver fatigue, using a combination of compressed sensing (CS) theory and deep neural networks (DNNs), that is based on EEG signals. First, CS theory is used to compress the recorded EEG data in order to reduce the computational load. Then, the compressed EEG data is fed into the proposed deep convolutional neural network for automatic feature extraction/selection and classification purposes. The proposed network architecture includes seven convolutional layers together with three long short-term memory (LSTM) layers. For compression rates of 40, 50, 60, 70, 80, and 90, the simulation results for a single-channel recording show accuracies of 95, 94.8, 94.6, 94.4, 94.4, and 92%, respectively. Furthermore, by comparing the results to previous methods, the accuracy of the proposed method for the two-stage classification of driver fatigue has been improved and can be used to effectively detect driver fatigue.

Due to the effect of emotions on interactions, interpretations, and decisions, automatic detection and analysis of human emotions based on EEG signals has an important role in the treatment of psychiatric diseases. However, the low spatial resolution of EEG recorders poses a challenge. In order to overcome this problem, in this paper we model each emotion by mapping from scalp sensors to brain sources using Bernoulli–Laplace-based Bayesian model. The standard low-resolution electromagnetic tomography (sLORETA) method is used to initialize the source signals in this algorithm. Finally, a dynamic graph convolutional neural network (DGCNN) is used to classify emotional EEG in which the sources of the proposed localization model are considered as the underlying graph nodes. In the proposed method, the relationships between the EEG source signals are encoded in the DGCNN adjacency matrix. Experiments on our EEG dataset recorded at the Brain-Computer Interface Research Laboratory, University of Tabriz as well as publicly available SEED and DEAP datasets show that brain source modeling by the proposed algorithm significantly improves the accuracy of emotion recognition, such that it achieve a classification accuracy of 99.25% during the classification of the two classes of positive and negative emotions. These results represent an absolute 1–2% improvement in terms of classification accuracy over subject-dependent and subject-independent scenarios over the existing approaches.

In recent years, detecting driver fatigue has been a significant practical necessity and issue. Even though several investigations have been undertaken to examine driver fatigue, there are relatively few standard datasets on identifying driver fatigue. For earlier investigations, conventional methods relying on manual characteristics were utilized to assess driver fatigue. In any case study, such approaches need previous information for feature extraction, which could raise computing complexity. The current work proposes a driver fatigue detection system, which is a fundamental necessity to minimize road accidents. Data from 11 people are gathered for this purpose, resulting in a comprehensive dataset. The dataset is prepared in accordance with previously published criteria. A deep convolutional neural network–long short-time memory (CNN–LSTM) network is conceived and evolved to extract characteristics from raw EEG data corresponding to the six active areas A, B, C, D, E (based on a single channel), and F. The study’s findings reveal that the suggested deep CNN–LSTM network could learn features hierarchically from raw EEG data and attain a greater precision rate than previous comparative approaches for two-stage driver fatigue categorization. The suggested approach may be utilized to construct automatic fatigue detection systems because of their precision and high speed.

Reaching out the function of the brain in perceiving input data from the outside world is one of the great targets of neuroscience. Neural decoding helps us to model the connection between brain activities and the visual stimulation. The reconstruction of images from brain activity can be achieved through this modelling. Recent studies have shown that brain activity is impressed by visual saliency, the important parts of an image stimuli. In this paper, a deep model is proposed to reconstruct the image stimuli from electroencephalogram (EEG) recordings via visual saliency. To this end, the proposed geometric deep network-based generative adversarial network (GDN-GAN) is trained to map the EEG signals to the visual saliency maps corresponding to each image. The first part of the proposed GDN-GAN consists of Chebyshev graph convolutional layers. The input of the GDN part of the proposed network is the functional connectivity-based graph representation of the EEG channels. The output of the GDN is imposed to the GAN part of the proposed network to reconstruct the image saliency. The proposed GDN-GAN is trained using the Google Colaboratory Pro platform. The saliency metrics validate the viability and efficiency of the proposed saliency reconstruction network. The weights of the trained network are used as initial weights to reconstruct the grayscale image stimuli. The proposed network realizes the image reconstruction from EEG signals.

Common spatial pattern (CSP) is shown to be an effective pre-processing algorithm in order to discriminate different classes of motor-based EEG signals by obtaining suitable spatial filters. The performance of these filters can be improved by regularized CSP, in which available prior information is added in terms of regularization terms into the objective function of conventional CSP. Variety of prior information can be used in this way. In this paper, we used time correlation between different classes of EEG signal as the prior information, which is clarified similarity between different classes of signal for regularizing CSP. Furthermore, the proposed objective function can be easily extended to more than two-class problems. We used three different standard datasets to evaluate the performance of the proposed method. Correlation-based CSP (CCSP) outperformed original CSP as well as the existing regularized CSP, Principle Component Cnalysis (PCA) and Fisher Discriminate Analysis (FDA) in both two-class and multi-class scenarios. The simulation results showed that the proposed method outperformed conventional CSP by 6.9% in 2-class and 2.23% in multi-class problem in term of mean classification accuracy.