An efficient LSTM network for emotion recognition from multichannel EEG signals

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:	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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