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Bayesian Inference on Brain-Computer Interfaces via GLASS
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DOI：10.1080/01621459.2025.2498088.png)
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摘要
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Brain-computer interfaces (BCIs), particularly the P300 BCI, facilitate direct communication between the brain and computers. The fundamental statistical problem in P300 BCIs lies in classifying target and non-target stimuli based on electroencephalogram (EEG) signals. However, the low signal-to-noise ratio (SNR) and complex spatial/temporal correlations of EEG signals present challenges in modeling and computation, especially for individuals with severe physical disabilities—BCI’s primary users. To address these challenges, we introduce a novel Gaussian Latent channel model with Sparse time-varying effects (GLASS) under a Bayesian framework. GLASS is built upon a constrained multinomial logistic regression particularly designed for the imbalanced target and non-target stimuli. The novel latent channel decomposition efficiently alleviates strong spatial correlations between EEG channels, while the soft-thresholded Gaussian process (STGP) prior ensures sparse and smooth time-varying effects. We demonstrate GLASS substantially improves BCI’s performance in participants with amyotrophic lateral sclerosis (ALS) and identifies important EEG channels (PO8, Oz, PO7, and Pz) in parietal and occipital regions that align with existing literature. For broader accessibility, we develop an efficient gradient-based variational inference (GBVI) algorithm for posterior computation and provide a user-friendly Python module available athttps://github.com/BangyaoZhao/GLASS. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
Keyword:
Bayesian analysis
ERP
Gaussian process
Latent channel
P300
期刊
J
IF:
3
论文数:
5.2K
被引数:
4.8W
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引用论文
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