Brain-Computer Interactions (BCIs) have been an emerging trend in the last few decades. Typically, facial expressions and gesture recognition are used to identify emotions, but these techniques can be invasive and need for quick user responses. Recently, Electroencephalogram (EEG) technology has emerged as a highly efficient method for emotion recognition. Hence, in this project we develop a classification model for human emotion classification based on EEG signals.
Main objective is to identify EEG signals and classify emotions into 3 categories/labels i.e. Positive, Negative, Neutral by implementing Machine Learning techniques using various packages, training and testing our model with a subject’s data-set and identifying its accuracy using a classification report.