Emotion recognition using deep neural networks and dynamic features of EEG signal

The aim of this paper is to evaluate the results of deep learning networks and other methods for emotion classification. According to the obtained results, the support vector machine achieved the highest classification accuracy for identifying four emotional states with 94.1% accuracy. Also, the proposed convolutional neural network identified the desired emotional states with an accuracy of 80%. The performance of the deep learning network will be improved if more features are used. In addition, the deep learning method has significant advantages over simple classification methods due to its resistance to noise and automatic processing.