A Fully Unsupervised Online Classification Algorithm for Event-Related Potential based Brain-Computer Interfaces

Objective: Brain-computer interfaces (BCIs) based on event-related potentials (ERPs) are among the most accurate and reliable BCIs. However, current mainstream classification algorithms struggle to eliminate the need for calibration and rely on expensive labeled data, limiting the practical usability of ERP based BCIs. The development of fully unsupervised algorithms is essential for the advancement of practical applications of BCI systems. Methods: In this study, we propose a novel unsupervised classification method called sliding-window distribution distance maximization (sDDM). This algorithm utilizes sliding windows to highlight important temporal features and transforms the metric of inter-class differences from absolute distances to relative distribution distances in Mahalanobis space, while incorporating information on target event similarity from the BCI paradigm. Additionally, our proposed spatial dimensionality reduction strategy ensures smaller spatial dimensions and more prominent spatial features. Results: We compare our proposed method to other state of-the-art unsupervised classification methods and evaluate it offline on our self-collected dataset, a public dataset recorded during the use of a P300 Speller by patients with ALS, and the BCI Competition III Dataset II. Our results demonstrate that our proposed method achieves the best spelling accuracy across all datasets, surpassing other unsupervised algorithms. We further explore its improvement effectiveness through ablation experiments. Conclusion: Our proposed method enhances the performance of unsupervised classification in ERP-based BCIs.