EEG-based emotion classification using LSTM under new paradigm

Deep learning has gained much popularity in solving challenging machine learning problems related to image, speech classification, etc. Research has been conducted to apply deep learning models in emotion classification based on physiological signals such as EEG. Most of the research works have based their model on the spatial aspects of the EEG. However, the emotion features in EEG are spread across the time domain during an emotional episode. Therefore, in this work, the emotion classification problem is modelled as a sequence classification problem. The power band frequency based features of every time segment of EEG sequences generated from 32-channel EEG data are used to train three different models of Long Short-Term Memory (LSTM1, LSTM2, and LSTM3). Four class (HVHA, HVLA, LVHA, and LVLA) classification experiments were performed based on the valence and arousal emotion models. The LSTM3 model with 128 memory cells achieved the highest classification accuracy of 90%, whereas LSTM1 (32 cells) and LSTM2 (64 cells) yielded classification accuracies of 85% and 89% respectively. Further, the impact of segment size on classification accuracy was also investigated in this work. Results obtained indicate that a smaller segment size leads to higher classification accuracy using LSTM models.