Electroencephalograph (EEG) research of emotion, as an important task in the advanced stage of artificial intelligence, has received more and more attention in recent years. Emotional EEG classification is widely used in human-computer interaction, medical research and other fields. This study presents the design of an EEG classification system on a lightweight convolutional neural network (CNN). DEAP (dataset for emotion analysis using physiological signals) provides EEG data of two kinds of emotion: arousal and valence. In order to obtain frequency domain information, the power spectral density features of theta, alpha, beta and gamma bands are extracted for evaluation, and each power spectral density matrix is expressed as a two-dimensional gray-scale image. The images were input into the convolutional neural network to train the classification model and complete the task of two classification. Experimental results show that compared with traditional machine learning, CNN has better classification effect. The accuracy of the two classification is 82.33% (Arousal) and 75.46% (Valence) respectively.
HAO Yan, SHI Huiyu, HUO Shoujun, HAN Dan, CAO Rui
. Emotion Classification Based on EEG Deep Learning[J]. Journal of Applied Sciences, 2021
, 39(3)
: 347
-346
.
DOI: 10.3969/j.issn.0255-8297.2021.03.001
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