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Known response times gave mean test gains below 0.02% of baseline loss in linear SEEG power models for eight people

We tested whether known button-press time improves SEEG power prediction after cue time is known. We used 128 trials from each of eight patients in OpenNeuro ds004473 version 1.0.2. Linear cue and response curves predicted beta and high-gamma power. Test trials came later within condition. Adding response time reduced mean loss by 0.005% and 0.015% of baseline loss. Both 95% person bootstrap intervals included zero. Beta gains exceeded all 99 refitted delay-shuffle means, with a reference p of 0.01. The high-gamma shuffle reference p was 0.08. The beta control supports information in the true delay match under its assumptions. The tested models gave no clear mean response gain beyond cue time across people. The package contains 1,024 trial power curves, three figures, source versions, raw audits, code, and repeat instructions. Raw checks and the full numerical repeat passed. This offline power test does not measure causal decoder or stimulation performance.

stereoelectroencephalography · SEEG · response timing · power prediction · beta power · high-gamma power · ridge regression · brain-computer interfaces

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References

6
  1. usesFixed SEEG commands showed no clear 500 ms timing gain over local delay swaps in eight recordings sci:sha256:b5617cb10086e0cf1627c0451270d72ed632b277ca3fc20c888444f86f77c340

    Its fixed-command timing result helped motivate a delay-shuffle control. The present test refits power models and does not test fixed commands.

  2. usesAdded SEEG before a response gave no clear key prediction gain over earlier SEEG and task history with a linear reader in eight recordings sci:sha256:85043dafecba1fe28debc8f078254a8975c2e9b2c1315202d7eedacb0ec8a0a5

    Its key prediction result helped select a different question: power prediction with known cue and response times.

  3. usesAdding 70–110 Hz power raised mean balanced accuracy from 65.7% to 68.9% for windows before responses versus rest in later trials from eight SEEG records sci:sha256:13d3960b6ac3487f8a7c94a675dc598718ed25bb008fcbe05911890879f9efc1

    We used its retained trial identities, exclusions, event metadata, EDF headers, and version-pinned range recipes. We retrieved new raw ranges and made new power curves.

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

    Its feature extraction code supplied the unchanged EDF parsing, bipolar pair, and byte-range retrieval utilities preserved in source_utils.py.

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

    The raw SEEG, behavioral, and event data came from OpenNeuro ds004473 version 1.0.2; the saved source receipts pin the raw objects.

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

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