Learning DenseNet features from EEG based spectrograms for subject independent emotion recognition

Abstract

Subject-independent emotion recognition (SIER) using electroencephalogram (EEG) signals has always been a challenge among the biomedical research community. One of the major reasons behind this is the chaotic and non-stationary nature of EEG signals which may show varying signal characteristics for the same emotions for different individuals. Therefore, it is a daunting challenge for the researchers to enhance the emotion recognition performance of the existing SIER systems, which is still quite low. To address these challenges, in this work, we have proposed a novel deep learning-based approach to efficiently extract and classify emotion-related information from the 2D spectrograms obtained from the 1D EEG signals. To uncover the hidden deep features of EEG signals we proposed a Deep Convolution neural network for Emotion Recognition (DCERNet) with dense connections among layers. The proposed DCERNet is developed with a sequence of customizations over a pre-trained Densenet121 model integrated with softmax and Support Vector Machine (SVM) classifiers on top. The EEG signals are transformed to 2D spectrograms and fed to the proposed model for identifying emotions. To evaluate the performance of the proposed models, comprehensive simulations are conducted using two publicly available databases SJTU Emotion EEG Dataset (SEED) and Database for Emotion Analysis of Physiological Signals (DEAP). Experimental outcomes illustrate that the proposed DCERNet model boosts the emotion recognition accuracy by almost 8% compared to the state-of-the-art methods using DEAP database.