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Correct trial matching did not show lower later probability error for conditional logistic readers in eight SEEG recordings

We tested whether correct trial matching reduced later probability error in public SEEG data. The study used eight people, eight selected bipolar pairs per person, and 674 later test trials. Weights and probability calibration used separate early trials. The primary Brier error was 0.1911 for correct matching and 0.1899 for the mean of 100 random-match readers. The mean difference was +0.00125, with a 95 percent person bootstrap interval [-0.00179, +0.00487]. The two-sided sign-flip value was 0.570. This test did not show a benefit. It did not establish equal performance. A paired fit cancels shared additive feature offsets when preprocessing stays fixed. This proof does not give a guarantee for separate window predictions. All features were rebuilt from pinned public source ranges. The package includes data, code, models, figures, provenance, and repeat instructions. This is an exploratory offline study of one recording per person. It does not test brain stimulation. This package revision excludes sci download metadata during file verification.

stereoelectroencephalography · conditional logistic regression · trial matching · probability error · brain-computer interfaces

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References

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

    We reused and verified all eight feature arrays, trial records, source metadata, raw range records, and the source extraction code. We tested a new conditional logistic fit and random trial matching control with separate early calibration.

  2. supersedesCorrect trial matching did not show lower later probability error for conditional logistic readers in eight SEEG recordings sci:sha256:a7f5f4db172ac9f8e7aac37ce25c70ba665f84b45dc0ebeb35f5b74750e5e055

    This revision excludes supplemental sci download metadata from file verification. All scientific data, models, outcomes, and analysis validation records are preserved from the first version.

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

    The analysis uses OpenNeuro ds004473 version 1.0.2.

  4. supportsdoi:10.1186/1471-2105-16-S6-S1

    This source describes the established conditional logistic matched-set method used in the analysis.

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

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