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Removing a 4–150 Hz fitted background reduced mean later-trial SEEG state accuracy by 7.1 percentage points in eight patients with eight fixed pairs each

We tested whether removal of a fitted spectral background improves a small SEEG task-state decoder. We used public OpenNeuro ds004473 version 1.0.2, eight patients, and eight fixed bipolar pairs per patient. Each trial supplied rest and pre-response voltage buffers. Models trained on 1,332 early trials and predicted 674 later trials. A masked 4–150 Hz line fit supplied residual band powers. With 24 inputs in both models, mean balanced accuracy fell from 70.7% for absolute power to 63.6% for residual power. The paired change was −7.1 percentage points; its descriptive 95% patient-bootstrap interval was −10.5 to −3.6 points. Seven patients had lower accuracy. Background intercept and slope alone gave 65.1% accuracy. Two fixed fit-range checks also gave lower mean accuracy. Independent calculations and a full repeat passed. The package preserves exact voltage buffers, source versions and hashes, code, models, predictions, and three figures. Contacts were selected with absolute power, and the cohort had been tested before. This exploratory result applies to the fixed offline pipeline. It does not establish a brain mechanism, live control, or a stimulation benefit.

stereoelectroencephalography · SEEG · spectral background · band power · residual power · balanced accuracy · task-state decoding · logistic regression · patient bootstrap · OpenNeuro

Admitted 7 citing publications

References

6
  1. 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 use its early-selected eight contact pairs, retained trials, fixed later-trial split, native absolute features, and frozen models. These inputs define the tested pipeline and allow exact baseline checks.

  2. usesA stationary Gaussian variance correction with 5 percent shrinkage reduced SEEG decoding accuracy for 20 percent simulated sample loss in eight recordings sci:sha256:495cd9ff22cc042e1f138d100dbf95110efd76545b22997f0bb2f49092bdf3ae

    We use its exact complete 500-sample bipolar voltage buffers, archived source metadata, version-pinned byte-range recipe, and four source retrieval and audit modules. No simulated missing-sample result enters the new comparison.

  3. usesA 4–90 Hz spectral background fit reduces beta channel rank agreement in 2.5 s windows from eight SEEG patients sci:sha256:9a2e4f733164e9e53cd839991951b4fa4c93b46f74e8a1596c3d0c6bfc99d262

    Its lower beta rank agreement after a fitted background motivated the task-prediction question. The present endpoint is later-trial state accuracy in a different dataset. We do not use its voltage data or code.

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

    Source dataset used for the eight SEEG recordings; the recorded version is 1.0.2 and its source metadata lists CC0.

  5. doi:10.1088/1741-2552%2Facae0a
  6. doi:10.1038/s41593-020-00744-x

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