REGEEG: A Regression-Based EEG Signal Processing in Emotion Recognition.

Abstract

Electroencephalograms provide a non-invasive and effective method for studying emotion recognition and developing Artificial Intelligence (AI) models to understand human behavior and decision-making processes. This study involved testing several machine learning classification kernels to develop an accurate emotion recognition model capable of classifying emotions stimuli such as "Boring," "Calm," "Happy," and "Fear" during gameplay. An emotion classifier was assessed using the publicly available database for an emotion recognition system based on EEG signals and various computer games (GAMEEMO). The signal processing method, referred to as Regression EEG (REGEEG), involves an efficient electrode pairing selector developed for EEG signal processing using a regression algorithm, rotation matrices, director vectors, and robust statistical and polynomial feature extraction. REGEEG and feature extraction methods were evaluated with 28 machine learning kernels, resulting in five kernels with classification performance above 80%, with the K-Nearest Neighbors (k-NN) based model outperforming the rest (achieving over 95% accuracy, F1-Score, and kappa-score). REGEEG performance was further validated using 30 Cross-Validation (CV) folders and 28 in the Leave-one Subject-out (LoSo) technique without impacting the average classification performance. The classification highlights revealed low variance in the CV, while the LoSo approach helped identify outliers in the GAMEEMO dataset. Furthermore, the EEG pair channels selector demonstrates superior performance in classification, indicating a correlation between features and each processed pair of channels.