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[2602.15955] Adaptive Semi-Supervised Training of P300 ERP-BCI Speller System with Minimum Calibration Effort
Summary
This article presents a novel adaptive semi-supervised training method for a P300 ERP-based Brain-Computer Interface (BCI) speller system, aiming to reduce calibration effort while enhancing accuracy and efficiency.
Why It Matters
The development of efficient BCI systems is crucial for improving communication for individuals with disabilities. This research addresses the challenge of lengthy calibration processes, proposing a method that enhances usability and performance with minimal labeled data, making it more accessible for practical applications.
Key Takeaways
- Introduces a semi-supervised EM-GMM algorithm to improve BCI speller systems.
- Reduces the need for extensive calibration, enhancing user experience.
- Demonstrates improved character-level accuracy in trials with participants.
- Offers a practical approach for real-time applications in assistive technology.
- Highlights the potential for better performance in scenarios with limited labeled data.
Computer Science > Machine Learning arXiv:2602.15955 (cs) [Submitted on 17 Feb 2026] Title:Adaptive Semi-Supervised Training of P300 ERP-BCI Speller System with Minimum Calibration Effort Authors:Shumeng Chen, Jane E. Huggins, Tianwen Ma View a PDF of the paper titled Adaptive Semi-Supervised Training of P300 ERP-BCI Speller System with Minimum Calibration Effort, by Shumeng Chen and 2 other authors View PDF HTML (experimental) 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 ...
