计算机科学
自编码
脑电图
人工智能
适应(眼睛)
编码(社会科学)
交叉验证
模式识别(心理学)
语音识别
机器学习
人工神经网络
统计
数学
心理学
精神科
神经科学
作者
Lei Zhu,Wangpan Ding,Jieping Zhu,Ping Xu,Yian Liu,Ming Yan,Jianhai Zhang
标识
DOI:10.1016/j.bspc.2022.103687
摘要
Emotion recognition has an important application in human–computer interaction (HCI). Electroencephalogram (EEG) is a reliable method in emotion recognition and is widely studied. However, since the individual variability of EEG, it is difficult to build a generic model between different subjects. In addition, the EEG signals will change in different periods, which has a great impact on the model. Therefore, building an effective model for cross-sessions, cross-subjects, cross- subjects and sessions has become challenging. In order to solve this problem, we propose a new emotion recognition method called Multisource Wasserstein Adaptation Coding Network (MWACN). MWACN can simplify output data and retain important information of input data by Autoencoder. It also uses Wasserstein distance and Association Reinforcement to adapt marginal distribution and conditional distribution. We validated the effectiveness of the model on SEED dataset and SEED-IV dataset. In cross-sessions experiment, the accuracy of our model achieves 92.08% in SEED and 76.04% in SEED-IV. In cross-subjects experiment, the accuracy of our model achieves 87.59% in SEED and 74.38% in SEED-IV. In cross-subjects and sessions experiment, the accuracy of the MWACN is improved by 3.72% in SEED and 5.88% in SEED-IV. The results show that our proposed MWACN outperforms recent domain adaptation algorithms.
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