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Adding beta and high-frequency log power products did not show a clear Brier gain after a task change in eight people with eight SEEG pairs each

We tested whether beta and high-frequency log power products improved a stereoelectroencephalography (SEEG) reader of pre-response and rest windows after a task condition change. We used public OpenNeuro ds004473 version 1.0.2 data from eight people with epilepsy. All readers used eight pairs, three power bands, and 33–70 early calibration trials per person. Pair choice and penalty choice used training data. Mean Brier score was 0.2154 with separate squared band effects and 0.2156 with same-pair products. The mean gain was -0.0002; the descriptive 95% person bootstrap interval was -0.0019 to 0.0019. The exact sign reference was p=0.867, under independent symmetric person differences. The test did not establish a gain. In the planned earlier-rest test, products increased Brier error in all eight people. The Holm-adjusted sign reference was p=0.023. The package includes features, source versions and hashes, raw audit ranges, fitted states, predictions, figures, and replay code. This is an exploratory offline test in a reused cohort. It does not test live control, phase-amplitude coupling, or stimulation.

stereoelectroencephalography · SEEG · brain-computer interface · log power · Brier score · task transfer · logistic regression · exploratory study

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References

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  1. usesEight fixed SEEG decoders scored 65.8% mean balanced accuracy for response versus rest windows after task condition changes sci:sha256:27c3a162d5f23c03418feb1337bb50c3dbd6fcf78da3fdc3bdc12da973491084

    We used its source-order task-segment protocol to separate early calibration, later first-condition trials, and next-condition trials. We added nested penalty choice and product controls.

  2. usesAdding 70–110 Hz power gave an uncertain 0.9-point mean accuracy gain after task condition changes in eight records with eight fixed SEEG pairs sci:sha256:717027634e86912d787789584e80766fc7c5386a31c16a5120cc893cd7674e32

    We reused its fixed feature arrays, source files, and task split. Its split and pair-rank equations supported the new controlled test of log power products. All 119 reused input files matched its manifest.

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

    We reused its periodogram feature data and bounded EDF retrieval code through the direct parent. These data support the response-versus-rest tests without a full EDF download.

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

    Dataset analyzed in this publication; the source records identify OpenNeuro ds004473 version 1.0.2.

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

    Source study for the SEEG task data and background claims described in the publication.

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