Emotion is considered to be critical for the actual interpretation of actions and relationships. Recognizing emotions from EEG signals is also becoming an important computer-aided method for diagnosing emotional disorders in neurology and psychiatry. Another advantage of this approach is recognizing emotions without clinical and medical examination, which plays a major role in completing the Brain-Computer Interface (BCI) structure. Emotions recognition ability, without traditional utilization strategies such as self-assessment tests, is of paramount importance. EEG signals are considered the most reliable technique for emotions recognition because of the non-invasive nature. Manual analysis of EEG signals is impossible for emotions recognition, so an automatic method of EEG signals should be provided for emotions recognition. One problem with automatic emotions recognition is the extraction and selection of discriminative features that generally lead to high computational complexity. This paper was design to prepare a new approach to automatic two-stage classification (negative and positive) and three-stage classification (negative, positive, and neutral) of emotions from EEG signals. In the proposed method, directly apply the raw EEG signal to the convolutional neural network and long short-term memory network (CNN-LSTM), without involving feature extraction/selection. In prior literature, this is a challenging method. The suggested deep neural network architecture includes 10-convolutional layers with 3-LSTM layers followed by 2-fully connected layers. The LSTM network in a fusion of the CNN network has been used to increase stability and reduce oscillation. In the present research, we also recorded the EEG signals of 14 subjects with music stimulation for the process. The simulation results of the proposed algorithm for two-stage classification (negative and positive) and three-stage classification (negative, neutral and positive) of emotion for 12 active channels showed 97.42% and 96.78% accuracy and Kappa coefficient of 0.94 and 0.93 respectively. We also compared our proposed LSTM-CNN network (end-toend) with other hand-crafted methods based on MLP and DBM classifiers and achieved promising results in comparison with similar approaches. According to the high accuracy of the proposed method, it can be used to develop the human-computer interface system.
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.
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.
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