An Efficient LSTM Network for Emotion Recognition from Multichannel EEG Signals
Xiaobing Du,
Cuixia Ma,
Guanhua Zhang,
Jinyao Li,
Yu-Kun Lai,
Guozhen Zhao,
Xiaoming Deng,
Yong-Jin Liu,
Hongan Wang
IEEE Transactions on Affective Computing (TAFFC), ,
pp. 1-12,
2020.
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Most previous EEG-based emotion recognition methods studied hand-crafted EEG features extracted from different electrodes. In this paper, we study the relation among different EEG electrodes and propose a deep learning method to automatically extract the spatial features that characterize the functional relation between EEG signals at different electrodes. Our proposed deep model is called ATtention-based LSTM with Domain Discriminator (ATDD-LSTM) that can characterize nonlinear relations among EEG signals of different electrodes. To achieve state-of-the-art emotion recognition performance, the architecture of ATDD-LSTM has two distinguishing characteristics: (1) By applying the attention mechanism to the feature vectors produced by LSTM, ATDD-LSTM automatically selects suitable EEG channels for emotion recognition, which makes the learned model concentrate on the emotion related channels in response to a given emotion; (2) To minimize the significant feature distribution shift between different sessions and/or subjects, ATDD-LSTM uses a domain discriminator to modify the data representation space and generate domain-invariant features. We evaluate the proposed ATDD-LSTM model on three public EEG emotional databases (DEAP, SEED and CMEED) for emotion recognition. The experimental results demonstrate that our ATDD-LSTM model achieves superior performance on subject-dependent (for the same subject), subject-independent (for different subjects) and cross-session (for the same subject) evaluation.
@article{du20_taffc,
title = {An Efficient LSTM Network for Emotion Recognition from Multichannel EEG Signals},
author = {Du, Xiaobing and Ma, Cuixia and Zhang, Guanhua and Li, Jinyao and Lai, Yu-Kun and Zhao, Guozhen and Deng, Xiaoming and Liu, Yong-Jin and Wang, Hongan},
year = {2020},
journal = {IEEE Transactions on Affective Computing (TAFFC)},
doi = {10.1109/TAFFC.2020.3013711},
pages = {1-12}
}