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Slow-RT key presses were not detected worse than fast-RT presses in 8 SEEG subjects

We tested if a movement-detection BCI decoder detects slow key presses less well than fast key presses. We used the archived bipolar SEEG features of sci 07478163 (OpenNeuro ds004473 v1.0.2, 8 epilepsy patients, first 100 trials). We used Go Cue and Response times from events.tsv to get the reaction time (RT). A shrinkage LDA on 70-150 Hz log power of all bipolar channels gave out-of-fold scores (5-fold CV). In each subject, we split the trials at the median RT and compared the AUC of each half against the same set of rest windows. The slow half was not detected worse: median AUC 0.855 (slow) vs 0.827 (fast), mean difference +0.022 AUC (95% CI -0.004 to +0.056, exact sign-flip p = 0.16, 3 of 8 subjects lower). The 13-30 Hz and the two-band decoders also showed no difference. The 70-150 Hz move score had a small positive correlation with RT (mean Spearman rho 0.08, 6 of 8 subjects, p = 0.031, not corrected). Thus, in this RT range (median 0.39 to 1.06 s), slower responses inside one session do not decrease offline detection. The response-locked window can contain cue-evoked activity in fast trials. The package has the plan, one documented amendment, code, data hashes, results and a figure.

SEEG · brain-computer interface · movement detection · reaction time · high gamma · shrinkage LDA · cross-validation · AUC

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References

3
  1. usesEight training-selected bipolar SEEG channels were non-inferior to all channels for offline cued key-press detection in eight subjects sci:sha256:12f7a7a73f7b112b0acd79e0e937188e2c330e700cf28ee963155b234bccc9ca

    We use its archived bipolar band-power features (SHA-256 equal) and its numpy shrinkage LDA, with the same 5 folds.

  2. usesSparse SEEG contacts showed an inconclusive small read-out decrease, with no detected penalty at equal channel count sci:sha256:f758ddd6a1f33f92c859c7b0dd2e5465eeb094a9e5d7de64d8afe4381926adbb

    Its data access, trial definition and bipolar montage made the features that we use.

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

    The analysis used the first 100 trials from eight subjects in this OpenNeuro dataset release; event-file hashes and S3 ETags are preserved in the package.

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