Emotion Recognition from Brain EEG Signals

Abstract: The recognition of emotions plays a vital role in various fields such as neuroscience, cognitive sciences, and biomedical engineering. This particular project is centered on the development of a system for recognizing emotions through EEG signals. The main goal is to accurately classify different emotional states like valence and arousal by analyzing EEG brain wave patterns. The study is based on the DEAP dataset, which contains EEG and peripheral physiological signals recorded as participants interacted with video clips and music. The main objective is to explore and compare the efficacy of two classification techniques: Long Short Term Memory (LSTM) and Convolutional Neural Networks (CNN). A total of 20 electrodes are used to identify and differentiate among 12 emotional states. The principal focus is on arousal and valence-related trends utilizing Russell's Circumplex Model, which depicts emotions on a two-dimensional plane determined by these two factors. This model allows for the visualization of emotions within this framework based on their levels of arousal and valence. By conducting extensive training and testing on the DEAP dataset, the accuracy of each classifier in predicting emotional states, including valence and arousal, is evaluated. This comparative assessment helps in understanding the strengths and weaknesses of each method for emotion recognition using EEG signals.