Abstract
Using deep learning architectures to recognize human emotions from electroencephalography (EEG) signals, thereby deploying practical applications, is a problem that is currently of great interest to many scientists. In which, depending on the application, the EEG signal can be received from devices with many electrodes (32, 64) or small (14, 2). For each application, to ensure good recognition efficiency, determining the appropriate deep learning architecture as well as the corresponding input features is an important issue that needs to be solved. In this paper, based on the DEAP emotion database, we evaluate the performance of several different deep learning architectures (CNN, BiLSTM, a combination of CNN and BiLSTM) that input is different features (FFT, Welch) extracted from 32-electrode to 14-electrode EEG signals. The experimental results obtained show that the CNN network with FFT features has the best performance with the highest average recognition accuracy and the lowest average loss value. However, the CNN network architecture with Welch features gives the best stability when switching from 32 electrodes to 14 electrodes.
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Mai, T.D.T., Phung, TN. (2023). Evaluating the Performance of Some Deep Learning Model for the Problem of Emotion Recognition Based on EEG Signal. In: Nghia, P.T., Thai, V.D., Thuy, N.T., Son, L.H., Huynh, VN. (eds) Advances in Information and Communication Technology. ICTA 2023. Lecture Notes in Networks and Systems, vol 847. Springer, Cham. https://doi.org/10.1007/978-3-031-49529-8_19
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