Publications

Abstract : In recent years, there were many attempts to classify human emotions based on corporeal signals including ECG, EEG, EMG. EEG based emotion classification is more accurate because it cannot be tainted by subjects' will. The recent development in CNNs has made it easier to systematically extract features from EEG easily. But again, the traditional CNNs fail to comprehend the multi-channel aspect of EEG. In this work, a simple and efficient pre-processing method by considering baseline signals is proposed to enhance the accuracy of recognition and we proposed a hybrid neural network which combines Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) to identify human emotions by extracting spatial and temporal features from raw EEG stream effectively. In CNN, the 1D EEG sequence is then efficiently converted into a 2D frame structure. In order to extract the inter-channel connection between physically adjacent EEG signals, the CNN module is used, and to extract the contextual information, the LSTM module is used. Using this logic, we were able to create a deep learning model which predicts arousal and valence emotions with 86.98% and 85.82% accuracy respectively.