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BrainSense: A Sequential Emotion Classification Model

This paper explores the task of Emotion Classification in Humans through a Deep Learning model by analysing Electroencephalogram (EEG) waves captured by a 32-electrodes and 16-channel BCI device which measures these signal amplitudes. Our purpose is two-fold - firstly, to provide an annotated dataset: BrainSense for furthering research in this domain, and secondly, to propose a novel sequential deep learning approach for creating a binary classifier that would classify a continuous EEG data stream as “Joyous” or “Sad”. We call our model - Continuous Sequential Emotion (CSEM) Classifier. We model the EEG data in such a manner, that future and past EEG waves are inculcated in deciding the emotion of a particular tuple of signal amplitudes. Keeping in mind the latter, our approach uses a many-to-one LSTM and also Bidirectional LSTM to encode the EEG signals in order to match the continuous nature of these signals. Classification is achieved through a dense network based on the EEG encodings. We get a maximum accuracy of 96.95% on the Bidirectional-CSEM. We train and test the model on our data, and discuss the detailed results and methodology.