EEG Emotion Recognition Using PSO-Based Feature Selection and Convolutional Neural Networks

EEG signals have become a promising source for emotion recognition due to their ability to capture the brain's electrical activity connected with different emotional conditions. In this work, a novel approach is proposed that integrates Particle Swarm Optimization (PSO)-based feature selection with Convolutional Neural Networks (CNNs) for improved EEG emotion classification. The method with the preprocessing of a notch filter to eliminate noise and enhance the quality of the EEG signals. Key features, including Magnitude Squared Coherence Estimate (MSCE) and Power Spectral Density (PSD), are extracted to capture essential frequency-domain information. PSO is employed to optimize the selection of features, reducing dimensionality while preserving the most relevant and informative attributes for emotion recognition. The optimized feature was subsequently passed to a CNN classifier, which improves the model's capability to accurately differentiate between different emotional states. This study is implemented using Python software to analyze emotion, and the effectiveness of the proposed approach is assessed using the EEG Brainwave dataset. Experimental results demonstrate that the proposed approach delivers an accuracy of 92.6% and a precision of 91%, highlighting its effectiveness in real-time, high-precision emotion recognition from EEG data.