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A BAYESIAN REINFORCEMENT LEARNING FRAMEWORK FOR OPTIMIZING THE BCI-UTILITY OF P300 BRAIN-COMPUTER INTERFACES
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AI
DOI：10.1214/25-AOAS2080.png)
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Abstract
En 中文
Brain-computer interfaces (BCIs) enable direct communication between the brain and computers, providing critical tools for people with disabilities to communicate with the world. The performance of BCIs is often evaluated using BCI-utility, a comprehensive metric that balances both accuracy and speed in communication. This paper introduces a Bayesian reinforcement learning framework to optimize the BCI-utility of the P300 BCI, a BCI system that identifies a user's intended character on a virtual keyboard by analyzing EEG responses to stimuli. We construct confidence scores for each character based on EEG responses and then propose a unified learning framework that explicitly maximizes BCI utility. It integrates two key components: an early stopping policy and a dynamic stimulus selection policy. The early stopping policy is optimized using an actor-critic algorithm, while a Gaussian process-based Bayesian model is developed to learn transition dynamics to guide the selection of the next stimulus. The proposed framework effectively addresses critical implementation challenges, including pauses between characters, double-target issues, and delays caused by the time required for EEG responses. Extensive simulations under varying signalto-noise ratios (SNRs) and evaluations on recorded human EEG data demonstrate that our method significantly improves BCI-utility compared to existing approaches. This work highlights the potential of reinforcement learning to improve the performance and usability of P300 BCI systems.
Keywords:
Model-based reinforcement learning
brain-computer interface
dynamic stimulus pattern
early stopping
Gaussian process
Journal
A
IF:
1.4
Papers:
88
Citations:
5.1K