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arXiv · 2602.15955

Adaptive Semi-Supervised Training of P300 ERP-BCI Speller System with Minimum Calibration Effort

Shumeng Chen·Jane E. Huggins·Tianwen Ma
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Abstract

A P300 ERP-based Brain-Computer Interface (BCI) speller is an assistive communication tool. It searches for the P300 event-related potential (ERP) elicited by target stimuli, distinguishing it from the neural responses to non-target stimuli embedded in electroencephalogram (EEG) signals. Conventional methods require a lengthy calibration procedure to construct the binary classifier, which reduced overall efficiency. Thus, we proposed a unified framework with minimum calibration effort such that, given a small amount of labeled calibration data, we employed an adaptive semi-supervised EM-GMM algorithm to update the binary classifier. We evaluated our method based on character-level prediction accuracy, information transfer rate (ITR), and BCI utility. We applied calibration on training data and reported results on testing data. Our results indicate that, out of 15 participants, 9 participants exceed the minimum character-level accuracy of 0.7 using either on our adaptive method or the benchmark, and 7 out of these 9 participants showed that our adaptive method performed better than the benchmark. The proposed semi-supervised learning framework provides a practical and efficient alternative to improve the overall spelling efficiency in the real-time BCI speller system, particularly in contexts with limited labeled data.

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Shumeng Chen, Jane E. Huggins, Tianwen Ma. 2026-02-17. Adaptive Semi-Supervised Training of P300 ERP-BCI Speller System with Minimum Calibration Effort. https://arxiv.org/abs/2602.15955

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