A novel convolutional neural networks for emotion recognition based on EEG signal

Emotion recognition based on electroencephalogram (EEG) signal is attracting more and more attention. Many feature engineering based models have been investigated. However, these models require a lot of effort for manually designing feature set. And these features can be hardly transformed among different problems. To reduce the manual effort on features used in EEG-based recognition and improve the performance, we propose an end-to-end model which is based on Convolutional Neural Networks (CNNs). In order to represent the EEG signals better, the original channels of EEG are firstly rearranged by Pearson Correlation Coefficient and the rearranged EEGs are fed into CNN. experiments were carried on DEAP dataset. The experimental results on the DEAP dataset show that the proposed method achieves 77.98% accuracy on the Valence recognition and 72.98% on the Arousal recognition.

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