Name
Abstract | ||
|---|---|---|
Most previous EEG-based emotion recognition methods studied hand-crafted EEG features extracted from different electrodes. In this article, 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
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tention-based
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with
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omain
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iscriminator (ATDD-LSTM), a model based on Long Short-Term Memory (LSTM) for emotion recognition 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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TAFFC.2020.3013711 | IEEE Transactions on Affective Computing |
Keywords | DocType | Volume |
Emotion recognition,multichannel EEG,LSTM,attention mechanism,domain adaptation | Journal | 13 |
Issue | ISSN | Citations |
3 | 1949-3045 | 1 |
PageRank | References | Authors |
0.36 | 24 | 9 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaobing Du | 1 | 1 | 0.70 |
| Cuixia Ma | 2 | 154 | 15.79 |
| Guanhua Zhang | 3 | 1 | 0.36 |
| Jinyao Li | 4 | 1 | 0.36 |
| Yu-Kun Lai | 5 | 1025 | 80.48 |
| Guozhen Zhao | 6 | 1 | 0.70 |
| Xiaoming Deng | 7 | 68 | 7.59 |
| Yong-Jin Liu | 8 | 837 | 72.83 |
| Hongan Wang | 9 | 642 | 79.77 |