A data augmentation and channel selection technique for grading human emotions on DEAP dataset

Emotion Recognition is one of the most important research area in the domain of Brain Computer Interactions. Human activities are influenced by emotions. Emotion recognition is carried out using gesture recognition, facial expressions etc. These methods are inconvenient and require quick feedback from users. Recently, Electroencephalogram technology has been found to be very efficient for emotion recognition task. Multi-Channel EEG headset is found to be an effective technology for BCI. However, it generates huge channel data and data obtained from many channels do not play effective role in identification of emotional state. In this paper, we use publicly available DEAP dataset as a source of EEG signals. Two major issues regrading the EEG data analysis is being addressed in this research. The first issue is the availability of the small number of samples. To address this issue, we exploit signal processing techniques. Multi-channel EEG with a large sampling frequency produces huge data per channel. All the channels are not important for emotion analysis and hence to address this second issue we explore metaheuristic algorithms to obtain an optimal subset of channels for emotion classification. Obtained results are very promising with 92.5% and 81.25% for two class classification in valence and arousal emotions and our proposed work can be used for practical applications of emotion classification.