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Bayesian Inference on Brain-Computer Interfaces via GLASS
Feb. 16, 2024, 5:45 a.m. | Bangyao Zhao, Jane E. Huggins, Jian Kang
stat.ML updates on arXiv.org arxiv.org
Abstract: 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 …
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