The Spatio-Temporal Equalization Sliding-Window Distribution Distance Maximization Based on Unsupervised Learning for Online Event-Related Potential-Based Brain–Computer Interfaces

Brain–computer interfaces (BCIs) provide a direct communication pathway between the central nervous system and external environments, enabling human–machine interaction control. Among them, event-related potential (ERP)-based BCIs are among the most accurate and reliable BCI systems. However, current mainstream classification algorithms struggle to eliminate calibration requirements and rely heavily on costly labeled data, limiting the practical usability of ERP-based BCIs. To address this, the development of unsupervised algorithms is critical for advancing real-world BCI applications. In this study, we propose the spatio-temporal equalization sliding-window distribution distance maximization (STE-sDDM) algorithm, which introduces spatio-temporal equalization (STE) to unsupervised ERP classification for the first time and integrates it with a novel unsupervised classification method, sliding-window distribution distance maximization (sDDM). STE estimates and removes colored noise interference in background noise to enhance the signal-to-noise ratio of inputs for sDDM. Meanwhile, sDDM leverages an enhanced inter-class divergence metric based on the ergodic hypothesis theory, utilizing sliding windows to emphasize temporally discriminative features, thereby improving unsupervised classification accuracy. The experimental results demonstrate that the integration of STE and sDDM significantly enhances ERP feature separability, outperforming state-of-the-art unsupervised online classification algorithms in spelling accuracy and the information transfer rate (ITR), facilitating more accurate and faster plug-and-play real-time control for BCI systems. Additionally, static spatio-temporal equalizer architectures were found to outperform dynamic architectures when combined with this framework.