EEG-Based Emotion Recognition with Combined Deep Neural Networks using Decomposed Feature Clustering Model

Much attention has been paid to the recognition of human emotions with the help of EEG signals based on machine learning technology. Recognizing emotions is a difficult task due to the non-linear nature of the EEG signal. This paper presents an advanced signal processing method that uses the depth function to extract features from all channels related to emotion. A decomposed feature clustering model is presented in this paper to decrease the computational cost of recognizing emotions and achieve better results. In the proposed method, we convert the signal into a two-dimensional wavelet spectrogram and calculate the characteristics of each subject. An EEG-based emotion classification model using a deep convolutional neural network (DNN) is presented on the SJTU SEED dataset. Combined feature model using AlexNet, VGGNet and ResNet-50 machine learning models are used for feature extraction. SVM and k-NN are used to classify data into positive/negative/neutral dimensions for SEED dataset. The results showed that models with images are more accurate than traditional models for emotion recognition. The proposed model achieves 91.3% accuracy in the SEED dataset, which is more accurate as compared to the other state-of-the-art human emotions recognition methods.