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Since EEG signal acquisition is non-invasive and portable, it is convenient to be used for different applications. Recognizing emotions based on Brain-Computer Interface (BCI) is an important active BCI paradigm for recognizing the inner state of persons. There are extensive studies about emotion recognition, most of which heavily rely on staged complex handcrafted EEG feature extraction and classifier design. In this paper, we propose a hybrid multi-input deep model with convolution neural networks (CNNs) and bidirectional Long Short-term Memory (Bi-LSTM). CNNs extract time-invariant features from raw EEG data, and Bi-LSTM allows long-range lateral interactions between features. First, we propose a novel hybrid multi-input deep learning approach for emotion recognition from raw EEG signals. Second, in the first layers, we use two CNNs with small and large filter sizes to extract temporal and frequency features from each raw EEG epoch of 62-channel 2-s and merge with differential entropy of EEG band. Third, we apply the adaptive regularization method over each parallel CNN’s layer to consider the spatial information of EEG acquisition electrodes. The proposed method is evaluated on two public datasets, SEED and DEAP. Our results show that our technique can significantly improve the accuracy in comparison with the baseline where no adaptive regularization techniques are used.


This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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IEEE Access





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