Du, Xiaobing, Ma, Cuixia, Zhang, Guanhua, Li, Jinyao, Lai, Yu-Kun  ORCID: https://orcid.org/0000-0002-2094-5680, Zhao, Guozhen, Deng, Xiaoming, Liu, Yong-Jin and Wang, Hongan
2022.
An efficient LSTM network for emotion recognition from multichannel EEG signals.
IEEE Transactions on Affective Computing
13
(3)
, pp. 1528-1540.
10.1109/TAFFC.2020.3013711
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Abstract
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.
| Item Type: | Article |
|---|---|
| Date Type: | Publication |
| Status: | Published |
| Schools: | Schools > Computer Science & Informatics |
| Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
| ISSN: | 1949-3045 |
| Date of First Compliant Deposit: | 11 August 2020 |
| Date of Acceptance: | 22 July 2020 |
| Last Modified: | 30 Nov 2024 22:00 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/134147 |
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