Emotion Recognition from EEG Using All-Convolution Residual Neural Network

Emotion recognition has become a research hotspot due to the rapid development of machine learning and neuroscience. One of the most challenging tasks in the Brain Computer Interface (BCI) is to recognize human emotions by electroencephalography (EEG) signals. Motivated by the excellent performance of deep learning approaches in recognition tasks, we proposed an All-Convolution Residual Neural Network (ACRNN), which is a hybrid neural network that combines convolution neural network (CNN) and residual network (ResNet). The ACRNN solves the problem of information loss between convolution layer and full connection layer to some extent, and the time hardly increase. Meanwhile, instead of pooling layer, we increased the convolution step to reduce the size of the feature map, so there was no pooling layer in ACRNN. We conducted extensive experiments on the DEAP dataset to demonstrate the performance of the emotional recognition of the ACRNN. The experimental results demonstrate that the proposed method achieved an excellent performance with a recognition accuracy of 92.46% and 91.68% on arousal and valence classification task. It was verified that the ACRNN for emotion recognition is effective.