all AI news
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 …
arxiv bayesian bayesian inference brain computer glass inference interfaces stat.ap stat.ml type via
More from arxiv.org / stat.ML updates on arXiv.org
Dropout Regularization Versus $\ell_2$-Penalization in the Linear Model
2 days, 12 hours ago |
arxiv.org
Estimation Sample Complexity of a Class of Nonlinear Continuous-time Systems
4 days, 12 hours ago |
arxiv.org
Jobs in AI, ML, Big Data
Data Architect
@ University of Texas at Austin | Austin, TX
Data ETL Engineer
@ University of Texas at Austin | Austin, TX
Lead GNSS Data Scientist
@ Lurra Systems | Melbourne
Senior Machine Learning Engineer (MLOps)
@ Promaton | Remote, Europe
Research Scientist, Demography and Survey Science, University Grad
@ Meta | Menlo Park, CA | New York City
Computer Vision Engineer, XR
@ Meta | Burlingame, CA