EEG-Based Cross-Subject Emotion Recognition Using Sparse Bayesian Learning With Enhanced Covariance Alignment

Abstract

EEG (Electroencephalography)-based emotion recognition has emerged as a crucial area of research due to its potential applications in mental health, brain-computer interfaces (BCIs), and affective computing. However, the inherent variability in EEG signals across individuals, coupled with limited dataset sizes, significantly hinders the development of robust and generalizable emotion recognition models. To overcome these challenges, we propose the Sparse Bayesian Learning with Enhanced Covariance Alignment (SBLECA) algorithm. SBLECA formulates cross-subject emotion recognition as an end-to-end decoding problem, integrating spatiotemporal filtering and classification within a sparse Bayesian learning (SBL) framework. Crucially, SBLECA incorporates a novel covariance alignment technique to mitigate inter-subject variability in EEG patterns. Rigorous evaluations on two publicly available emotion datasets demonstrate that SBLECA consistently outperforms state-of-the-art methods. Furthermore, SBLECA offers valuable insights into the neural correlates of emotion through interpretable visualizations of learned spatial and temporal filters. SBLECA holds promise as a valuable EEG decoding tool to advance the development and translation of neurotechnologies and biomarkers for brain disorders.