Title: Classification of electroencephalography signals using three-dimensions convolution neural network with long short-term memory architecture

Authors: Viet Quoc Huynh; Hoang-Thuy-Tien Vo; Thu Anh Nguyen; Tuan Van Huynh

Addresses: Department of Physics and Computer Science, Faculty of Physics and Engineering Physics, University of Science, Ho Chi Minh City, Vietnam; Vietnam National University, Ho Chi Minh City, Vietnam ' Department of Physics and Computer Science, Faculty of Physics and Engineering Physics, University of Science, Ho Chi Minh City, Vietnam; Vietnam National University, Ho Chi Minh City, Vietnam ' Department of Physics and Computer Science, Faculty of Physics and Engineering Physics, University of Science, Ho Chi Minh City, Vietnam; Vietnam National University, Ho Chi Minh City, Vietnam ' Department of Physics and Computer Science, Faculty of Physics and Engineering Physics, University of Science, Ho Chi Minh City, Vietnam; Vietnam National University, Ho Chi Minh City, Vietnam

Abstract: This research attempts to use the electroencephalography signals based on physiological signals extracted from the database for emotion analysis to classify the emotion of the subjects by using classifier neural network algorithms. In this work, two types of neural network including 3D convolution neural network and hybrid network (3D convolution neural network model combined with long short-term memory architecture) were applied to train and test its ability of emotion states classification. As a result, the hybrid network gave the most efficient classification with an accuracy of around 80%, which was better than other algorithms such as support vector machine, random forest, convolution neural network. Furthermore, the results also showed that the accuracy achieved differently at various frequency bands, in which delta frequency band gave the highest accuracy. Combining signals of different frequencies helped to improve the classification efficiency.

Keywords: electroencephalography; EEG; convolution neural network; LSTM; hybrid network.

DOI: 10.1504/IJMEI.2023.130732

International Journal of Medical Engineering and Informatics, 2023 Vol.15 No.3, pp.270 - 281

Received: 04 Dec 2020
Accepted: 01 May 2021
Published online: 04 May 2023 *

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