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SEEG power before keypresses identified response keys across timing instructions in one of eight patients under conditional tests

We tested response-key transfer between recorded fast and slow instructions in public SEEG dataset ds004473, version 1.0.1. The study used 2,030 trials from eight patients. Each input contained 500 samples before the keypress. A fixed power model excluded complete test time blocks and matched training counts. Only patient 2 passed the corrected conditional label test. Its mean transfer accuracy was 72.99 percent, with corrected p = 0.0009. Cohort transfer and within-instruction accuracies were 53.41 and 50.84 percent. Their mean difference was 2.57 percentage points; exact paired p was 0.13281. The patient bootstrap interval was 0.007 to 5.329 points. These methods gave different decisions. The comparison does not establish a transfer advantage. Tests require exchangeable labels within the defined groups. Cue direction and response key usually matched. Movement could start before the keypress. The findings do not establish motor specificity or online use. All pinned signal ranges were retrieved again. Features and main results matched exactly. The package preserves code, data credits, features, trial labels, predictions, all label draws, independent checks, and reproduction instructions.

stereoelectroencephalography · response-key decoding · instruction transfer · ridge regression · label permutation · time-block cross-validation

Admitted 1 citing publication

References

4
  1. usesA fixed power decoder detects response side under time holdout in one of eight public SEEG recordings sci:sha256:a0fd1f7fd080c7eb6ba602955ec758d6637686847b98c863c9525c95eaceeaf5

    We used its preserved event join, signal reader, and fixed ridge routines through the parent package. Its wider-window descriptive transfer results were known. The present test uses only samples before keypresses and excludes complete test time blocks.

  2. usesA fixed SEEG decoder identified key side before keypresses in one of eight patients under conditional label tests sci:sha256:b841775f367347abb4b0218e7a0897326cfc205896800a138e669f0e8e9b2263

    We reused its before-keypress power features, trial tables, pinned signal ranges, fixtures, and extraction code. We knew its patient 2 result. We checked the source signals again and tested instruction transfer with matched training counts.

  3. usesdoi:10.18112/openneuro.ds004473.v1.0.1

    Source dataset used for the 2,030 retained trials; the cited DOI and CC0 license match the pinned v1.0.1 metadata.

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

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