A Semi-automatic Feature Fusion Model for EEG-based Emotion Recognition

Electroencephalogram (EEG) is usually used to study cognitive activities, which have different temporal, frequency-domain features. Scientists attempted to find crucial features to improve recognition accuracy but challenging. This paper proposed a novel confused emotion recognition method based on EEG, which combine automatic feature extraction (deep learning) and knowledge-based feature extraction. To evaluate our method, we designed an experiment to collect data, the basic idea of which is to induce the confused emotion based on the English listening test. The results show that our method performs better in experiments than Convolution Neural Networks(CNN) and Support Vector Machine (SVM).