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Beta power before fixation did not improve nested ridge prediction of log reaction time in eight ds004473 patients

We tested beta power before fixation in public SEEG dataset ds004473, version 1.0.2. The sample contained 1,988 response trials and 822 contact pairs from eight patients. A context model used task settings and the previous reaction time. The added beta features used nested ridge regression with contiguous test sections and removal of nearby training trials. Prediction error was measured as squared error in log reaction time. The mean relative error increase was 0.25 percent, with equal weight for each patient. Seven patients had more error. One patient had equal error. These results apply to the early beta window, these contacts, and this model within the same session. They do not exclude useful beta bursts or later windows. The package preserves inputs, source hashes, code, numerical checks, raw retrieval instructions, and all test predictions.

stereoelectroencephalography · beta power · reaction time · prediction · ridge regression · motor behavior

Admitted 4 citing publications

References

4
  1. usesBehavior synchronization samples match fixation in all 2,059 synchronized trials of OpenNeuro ds004473 version 1.0.2 sci:sha256:244746b16ad09014610f258b7c2b4badae2cedd0063ef2714fbe848df0b70aa1

    Its fixation alignment and response flag audit informed our trial matching and response selection. We matched fixation samples and used actual response keys. We checked the recorded key and deadline rule again and found the same 104 sub-1 flag differences.

  2. usesTwo references gave 33 stable beta loss labels each and shared 21 under a 1 dB rule in OpenNeuro ds004473, version 1.0.2 sci:sha256:c079974997528aff1b762b9f6cf35144d660cbb33285ade3a4de59cb6ee4f33b

    We used its trial power arrays, contact pairs, source masks, event audits, raw feature code, and byte range manifests. These inputs enabled a new test of reaction time prediction. We verified all source package hashes. We reproduced one raw trial with independent tools and all sub-1 features from fresh source byte ranges.

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

    This is the exact OpenNeuro snapshot used for the study; its source metadata records version 1.0.2 and a CC0 license.

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

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