脑电图
计算机科学
人工智能
特征提取
模式识别(心理学)
情绪分类
过程(计算)
连续小波变换
小波
语音识别
小波变换
特征(语言学)
离散小波变换
心理学
哲学
精神科
操作系统
语言学
作者
Sali Issa,Qinmu Peng,Xinge You
出处
期刊:IEEE transactions on systems, man, and cybernetics
[Institute of Electrical and Electronics Engineers]
日期:2020-02-13
卷期号:51 (12): 7382-7391
被引量:58
标识
DOI:10.1109/tsmc.2020.2969686
摘要
This article presents a new user-independent emotion classification method that classifies four distinct emotions using electroencephalograph (EEG) signals and the broad learning system (BLS). The public DEAP and MAHNOB-HCI databases are used. Just one EEG electrode channel is selected for the feature extraction process. Continuous wavelet transform (CWT) is then utilized to extract the proposed gray-scale image (GSI) feature which describes the EEG brain activation in both time and frequency domains. Finally, the new BLS is constructed for the emotion classification process, which successfully upgrades the efficiency of emotion classification based on EEG brain signals. The experiment results show that the proposed work produces a robust system with high accuracy of approximately 93.1% and training process time of approximately 0.7 s for the DEAP database, as well as, the high average accuracy of approximately 94.4% and training process time of approximately 0.6 s for MAHNOB-HCI database.
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