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arXiv Machine Learning By Ethan Davis

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

Read the original on arXiv Machine Learning →

arXiv:2607. 22980v1 Announce Type: new Abstract: Brain-computer interfaces (BCIs) have long sought calibration-free operation, but classifiers are typically benchmarked by discrimination alone, blind to whether predicted probabilities are well calibrated - a meaningful gap given nonstationary electroencephalogram (EEG) signals and the risk of overconfident point-estimate classifiers under distribution shift.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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