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Cross-Subject EEG-Based Emotion Recognition with Deep Domain Confusion by Weiwei Zhang; Fei Wang; Yang Jiang; Zongfeng Xu; Shichao Wu; Yahui Zhang is a book available to read on EtoBox.

What is Cross-Subject EEG-Based Emotion Recognition with Deep Domain Confusion about?

At present, the method of emotion recognition based on Electroencephalogram (EEG) signals has received extensive attention. EEG signals have the characteristics of non-linear, non-stationary and low spatial resolution. There are great differences between EEG signals collected from different subjects as well as the same subjects from different experimental sessions. Therefore, it's difficult for traditional emotion recognition methods to achieve high recognition accuracy. To tackle this problem, this paper proposes a cross-subject emotion recognition method based on convolutional neural network (CNN) and deep domain confusion (DDC). Firstly, the Electrodes-frequency Distribution Maps (EFDMs) is constructed from EEG signals, and the residual blocks based deep CNN is used to automatically extract the features related emotion recognition from the EFDMs. Then, the difference of the feature distribution between source and target domain are narrowed by the DDC. Finally, the EEG emotion recognition task is realized with EFDMs and CNN. On SEED, we set up two experiments, the proposed method achieved an average accuracy of 90.59% and 82.16%/4.43% for mean accuracy and standard deviation unde

Author

Weiwei Zhang; Fei Wang; Yang Jiang; Zongfeng Xu; Shichao Wu; Yahui Zhang

Publisher

Springer International Publishing : Imprint : Springer

Published

2019

Language

EN

ISBN

9783030275273

Subjects

Computer Science, Science, Mathematics