计算机科学
脑电图
人工智能
情绪识别
卷积神经网络
图形
脑-机接口
模式识别(心理学)
生成语法
任务(项目管理)
情绪分类
机器学习
语音识别
心理学
理论计算机科学
神经科学
经济
管理
作者
Yun Gu,Xinyue Zhong,Cheng Qu,Chuanjun Liu,Bin Chen
出处
期刊:IEEE Journal of Biomedical and Health Informatics
[Institute of Electrical and Electronics Engineers]
日期:2023-02-03
卷期号:27 (5): 2377-2386
被引量:15
标识
DOI:10.1109/jbhi.2023.3242090
摘要
Emotion is a human attitude experience and corresponding behavioral response to objective things. Effective emotion recognition is important for the intelligence and humanization of brain-computer interface (BCI). Although deep learning has been widely used in emotion recognition in recent years, emotion recognition based on electroencephalography (EEG) is still a challenging task in practical applications. Herein, we proposed a novel hybrid model that employs generative adversarial networks to generate potential representations of EEG signals while combining graph convolutional neural networks and long short-term memory networks to recognize emotions from EEG signals. Experimental results on DEAP and SEED datasets show that the proposed model achieved the promising emotion classification performance compared with the state-of-the-art methods.
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