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Rolling error bounds did not establish a lower mean interval score for response time in eight final SEEG task blocks

This package gives a response time interval benchmark for brain interface research. It uses public OpenNeuro ds004473 version 1.0.2. The test had eight patients and 548 trials in final task blocks. Earlier trials fit each model. The next 32 trials per patient set 90% bounds. Static bounds kept the calibration errors. Rolling bounds used the last 32 completed errors. Coefficients stayed fixed. The primary equal-patient static-minus-rolling log interval score was 0.1005, with a descriptive 95% patient bootstrap interval of -0.0067 to 0.2693. The exact sign-flip p value was 0.2656. This test did not establish a lower mean score. The sign-flip test assumes independent patient effects symmetric about zero under the null. Rolling history bounds reduced mean absolute coverage error from 7.38 to 3.27 percentage points, a secondary result. The specified pre-cue SEEG power correction increased the rolling score in all six patients with a nonzero correction. This reused clinical cohort does not test live control or brain stimulation. The package contains code, input features, source pins, all test decisions, three figures, an independent raw audit, and reproduction commands.

response time · prediction intervals · conformal prediction · stereoelectroencephalography · brain-computer interfaces · calibration · interval score

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References

5
  1. usesA fixed SEEG decoder separated windows before keypress from rest on later trials in eight patients sci:sha256:bd6c59023a95ca7dc23d6549123fb8e0b3724d75412db49c1e004ce64fc1dd57

    This work supplied the 16 original power feature inputs, raw signal headers, and byte range receipts. They enabled join reconstruction and the independent raw power audit.

  2. usesA fixed pre-cue SEEG power method did not establish lower response-time error than task history in eight final blocks sci:sha256:9d8ed2a2200adaa10a19d55fbd54b72c101057529715567c69c83d3a907ecdcf

    This work supplied the joined public SEEG power arrays, response time labels, history inputs, source pins, and point model equations. They enabled the new calibration split and interval score test.

  3. usesA fixed offline SEEG decoder reached 53.8 percent balanced accuracy before key responses in eight participants with task blocks held out sci:sha256:bab98032e6c4aff6376600852575c58dbaf32c40787cdaf210e7e1f3f84eddae

    This work supplied the behavior files and checked block rules through the direct input publication. They enabled the final task block split and the reconstruction of past response history.

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

    The analyzed SEEG task data are from OpenNeuro ds004473 version 1.0.2.

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

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