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Early calibration reduced later error for removed beta voltage by 64 percent on average in 19 selected SEEG records

We tested whether early contact data could predict beta voltage that adjacent differences remove. Real linear models used one minute of calibration and only bipolar inputs during the next minute. The mean reduction in removed voltage error was 64.28 percent across 19 selected ds003876 records. All 19 gains were positive. The descriptive 95 percent patient bootstrap interval was 58.55 to 70.55 percent. Baseline error was 43.99 percent of total common-average beta energy. Model error was 16.04 percent. An exploratory independent phase benchmark gave a 30.82 percent mean gain from contact power geometry. Prediction does not uniquely recover removed voltage or identify neural sources. These short, reused clinical records do not establish later stability or clinical benefit. The package preserves source pins, all compact inputs, code, fitted models, patient results, proofs, and reproduction commands. All 4,222,800 beta coefficients matched a new public source rebuild. A fresh analysis matched every numerical output.

stereoelectroencephalography · bipolar montage · beta voltage · ridge regression · voltage reference · prediction

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References

7
  1. usesReference choice reduced mean 80 to 150 Hz rank agreement more than time change in 19 public SEEG records sci:sha256:16d6a5e5126b007f6f6be4f042d829df32ccd751f41bcfda4c9b9a3f5a80fce2

    The inherited EDF helper was adapted from this publication. We retain its credit and use the helper to check pinned source ranges and rebuild all beta inputs.

  2. usesAdjacent contact differences could not determine 44 percent of common-average beta power on average in 19 selected SEEG records sci:sha256:a638ba973d3bcf6c72e71eaa8884b79dddd19189e216fe719741da94fcaf8760

    We used its contact exclusions and the voltage modes removed by adjacent differences. We tested statistical prediction of those modes from earlier calibration. The result does not change its nonidentifiability proof.

  3. usesSelected covariance between named shafts reduced mean Gaussian beta loss for the next minute in 19 SEEG records sci:sha256:90aac3b00b3aaccfc26e317167e801e07b5f711f0c08a4977abaedee2b6b37a4

    We reused its 19 beta Fourier arrays, record selection, source metadata, raw examples, and pinned retrieval recipe. We added a new later-minute voltage prediction endpoint and rebuilt every input from public source ranges.

  4. usesdoi:10.18112/openneuro.ds003876.v1.0.2

    The analysis uses public SEEG data from OpenNeuro ds003876 release 1.0.2.

  5. doi:10.1038/s41593-021-00901-w
  6. doi:10.1101/2021.10.15.464594
  7. doi:10.1016/j.neuroimage.2018.08.020

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