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A 20-trial delay in correct feedback changed mean absolute coverage error by 0.23 percentage points in eight SEEG replays at a 90% target and a 0.05 step

We tested delayed feedback for a fixed SEEG reader. Feedback labels were correct. The test used eight recordings from OpenNeuro ds004473, version 1.0.2. It used 674 later trials. Each trial had rest and pre-response windows. The whole-trial coverage target was 90%. The update step was 0.05. A 20-trial delay changed mean absolute coverage error by +0.23 percentage points relative to no added delay. The 95% paired person bootstrap interval was -0.12 to +0.62 points. This primary result was uncertain. Mean single-label output fell from 34.31% to 28.67% of windows. Mean error among these outputs was 17.59% and 19.60%, respectively. The package gives a delayed feedback benchmark, exact state arithmetic, and an audit of pending feedback. It includes code, feature arrays, fixed reader inputs, every trial decision, four figures, source hashes, and retrieval instructions. A fresh source rebuild reproduced all retained features exactly. The cohort and reader had prior use. Response alignment and later eligibility make this a retrospective test. The result does not establish live BCI performance or a brain stimulation effect.

stereoelectroencephalography · adaptive conformal inference · delayed feedback · brain-computer interface · coverage · retrospective replay

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References

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  1. usesCalibration trial reuse for reader fitting reduced mean trial coverage by 5.5 percentage points on later trials in eight SEEG recordings at a 90% target sci:sha256:af8ec2210cc0b14ba6673f31b10d34e08671d013a9bb64c2b44926c2d1a1fe77

    Used the separate C=1 reader, exact trial splits, double precision probabilities, saved model parameters, and source provenance. The new analysis adds delayed correct-label feedback. The fixed-set counts matched the parent.

  2. usesFour-bit error feedback reduced mean state log loss at 999 Hz and increased it at 250 Hz in eight SEEG recordings with fixed readers and limits of six times training RMS sci:sha256:91e54b754ed955d5fa0916d3e8b3aa5659aeb3b71f70125655aa74a6b185a587

    Used the MIT numerical helper to reconstruct and refit the fixed logistic readers. The helper license and source record are preserved.

  3. usesEight SEEG pairs classify pre-response and rest windows at 70.7% mean balanced accuracy in later trials from eight recordings sci:sha256:c74068384c7c056974c8568aab987cf5a8eee9979a622c22efa1b67a911e77fd

    Used the candidate-pair feature arrays, eligibility rules, BIDS sidecars, version-pinned byte recipes, and feature extraction code. These inputs support the new feedback replay.

  4. usesdoi:10.18112/openneuro.ds004473.v1.0.2

    The analysis used OpenNeuro ds004473 version 1.0.2.

  5. doi:10.1088/1741-2552%2Facae0a

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