Multiclass EEG Classification Using Recurrent Neural Network and Feature Selection with PSO Algorithm for Emotion Recognition

Abstract

The recognition of emotion through Electroencephalogram data is essential for human computer interaction, mental health tracking, and emotion sensing computing. This paper integrates Recurrent Neural Networks (RNN) and a feature selection technique based on Particle Swarm Optimization (PSO) method within multiclass EEG classification paradigm. The method increases classification efficiency by feature selection from EEG signals which reduces the amount of computations needed in the first place. The span of emotion identification is aided by RNN model’s power in capturing the sequential dependencies of EEG signals. The experiments conducted demonstrated that the proposed solution performs better than traditional approaches in terms of accuracy and effectiveness. This opens avenues for the more precise and immediate applications of affective computing by providing a comprehensive solution for EEG emotion recognition.