Emotion Recognition from EEG Signals Using Principal Component Analysis and Random Forest Classifier

Emotion recognition using EEG signals is a critical task with wide-ranging applications in mental health monitoring, disorders of consciousness, and neurofeedback systems. However, EEG signals are inherently noisy and high-dimensional, posing persistent challenges to accurate emotion classification. Most existing approaches rely on limited preprocessing or employ Principal Component Analysis (PCA) solely for dimensionality reduction, often overlooking the residual artifacts that degrade classifier performance. This study introduces a novel PCA-RF framework that repurposes PCA for dual objectives: targeted noise suppression and feature dimensionality reduction. Uniquely, PCA is applied after frequency-specific filtering to more effectively eliminate residual ocular and cardiac artifacts, thus improving the quality of EEG feature representations. These compact and denoised features are then processed by a Random Forest (RF) classifier, which robustly captures the nonlinear dynamics of the EEG data. Evaluated on the DEAP dataset, the proposed PCA-RF approach achieved state-of-the-art performance, with 96.57% accuracy for arousal and 96.03% for valence classification. The key contribution of this work lies in its strategic integration of PCA for both artifact suppression and feature optimization, setting it apart from conventional pipelines and delivering a reliable and accurate solution for EEG-based emotion recognition.