Bayesian Complete-Pooling in Cross-Subject Classification for Motor Imagery Electroencephalogram

arXiv:2607.22980 · cs.LG · Submitted 2026-07-25 · Read on arXiv

cs.LG

Submitted: 2026-07-25

Updated: 2026-09-25

Project page: http://probml.github.io/book1

Terminology

• Calibration — 16× in this paper · explained in 7 episodes
• Accuracy — 7× in this paper · explained in 1 episode
• Benchmark — 7× in this paper · explained in 2 episodes
• Verification — 7× in this paper · explained in 2 episodes
• Feature Engineering — 5× in this paper · explained in 3 episodes
• Distribution Shift — 3× in this paper · explained in 5 episodes
• Transfer Learning — 3× in this paper · explained in 10 episodes
• Uncertainty Quantification — 3× in this paper · explained in 8 episodes
• Benchmarking — 2× in this paper · explained in 3 episodes
• Loss Function — 2× in this paper · explained in 2 episodes
• Mother of All BCI Benchmarks — 2× in this paper
• Bayesian Framework — 1× in this paper · explained in 3 episodes
• Exchangeability — 1× in this paper · explained in 2 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Gaussian Mixture Model (GMM) — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• No-U-Turn Sampling — 1× in this paper
• Pairwise Comparisons — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Signal-to-noise ratio (SNR) — 1× in this paper · explained in 2 episodes
• Singular Value Decomposition (SVD) — 1× in this paper · explained in 2 episodes
• fNIRS — 1× in this paper · explained in 2 episodes

Sources

• OptunaHub: A Platform for Black-Box Optimization

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