EEG-based Emotion Recognition using Transfer Learning Based Feature Extraction and Convolutional Neural Network

In this paper, a novel method for EEG(Electroencephalography) based emotion recognition is introduced. This method uses transfer learning to extract features from multichannel EEG signals, these features are then arranged in an 8×9 map to represent their spatial location on scalp and then we introduce a CNN model which takes in the spatial feature map and extracts spatial relations between EEG channel and finally classify the emotions. First, EEG signals are converted to spectrogram and passed through a pre-trained image classification model to get a feature vector from spectrogram of EEG. Then, feature vectors of different channels are rearranged and are presented as input to a CNN model which extracts spatial features or dependencies of channels as part of training. Finally, CNN outputs are flattened and passed through dense layer to classify between emotion classes. In this study, SEED, SEED-IV and SEED-V EEG emotion data-sets are used for classification and our method achieves best classification accuracy of 97.09% on SEED, 89.81% on SEED-IV and 88.23% on SEED-V data-set with fivefold cross validation.