Training rank maps gave no clear probability error gain in eight later-trial SEEG tests

We tested whether a fixed training rank map reduces probability error for a small SEEG reader. We used public OpenNeuro ds004473 version 1.0.2 data from eight people. Each reader used the same eight bipolar pairs and 24 power features. It learned from 1,332 earlier trials and was tested on 674 later trials. Mean Brier error was 0.19014 with standard scaling and 0.18914 with the rank map. The mean gain was 0.00100. Its descriptive 95 percent person-bootstrap interval was -0.00312 to +0.00542. The exact two-sided sign-flip reference p value was 0.6406 under independent people and a sign-symmetric null. Four people improved and four had higher error. The test gives no clear evidence of a mean benefit and does not show equal error. We reused a source cohort and did not test a live trigger, a separate day, or stimulation. A full public raw-range rebuild matched all 3,133,395 source feature values. A new process matched all numerical result files. The package keeps the fixed plan, source data and code, all maps and predictions, label controls, provenance, three figures, and instructions to repeat the work.

stereoelectroencephalography · brain-computer interface · probability error · rank map · logistic regression · Brier score · intracranial EEG