Classifying Emotional States Through EEG-Derived Spectrograms

Abstract

Identifying emotional state of a person plays an important role in a multitude of applications such as affective computing, human-computer interaction and most importantly healthcare. Understanding and correctly identifying human emotions can improve mental health assessments, making it possible to obtain personalized treatment plans and increase the user experience across a variety of digital applications. Our work explores the feasibility of emotion classification using EEG signals recorded from multiple users while experiencing various emotions. Working with time series data of the brain activity in various participants, who are experiencing particular emotions over an interval of 15 seconds. To train and evaluate the classification model several kinds of machine learning and deep learning models such as CNN, RNN and LSTM, are employed. We compare general sequential models with image processing models in the task of classifying signal data. Observing the model's ability to generalize across the population. © 2024 IEEE.