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Early phase calibration reduced 50 ms disagreement with an offline theta filter in seven SEEG rest records

Early phase calibration reduced later mean absolute phase disagreement at a simulated 50 ms delay in seven public SEEG rest records. A sinusoid forecast used the last 500 ms of stored samples. Early offline phase targets selected a frequency before the later test. At 50 ms, person mean disagreement was 76.49 degrees for calibration, 84.45 degrees for nominal theta power selection, and 83.33 degrees for fixed 7 Hz. Calibration improved all seven people against power selection and six against fixed 7 Hz. Both planned person sign tests gave Holm p = 0.03125. The power-band boundary changed the result: the calibration gain was only 1.11 degrees against a strict power-bin rule. Also, 65.4 percent of calibrated forecasts differed from the offline target by more than 45 degrees. Constant training windows exposed numerical phase sensitivity in one person. The package includes selected source signals, pinned retrieval instructions, code, results, five figures, and source and repeat checks. This reused sample gives an exploratory offline result. It does not establish biological phase accuracy, stimulation benefit, or live BCI performance.

stereoelectroencephalography · phase forecasting · theta oscillations · frequency calibration · offline validation · neural engineering

Admitted 1 citing publication

References

4
  1. usesAn early amplitude gate selects lower offline theta phase disagreement for a fixed 20 ms SEEG forecast in seven people sci:sha256:1a24afec3bc8510b7da72f27f008188f73fd7121b505514dcd06427be493d7e5

    Reuse its seven source contact pairs, source arrays, metadata, range retrieval code, and sinusoid forecast. Test early phase-target frequency selection at 50 ms with two planned comparisons and numerical sensitivity checks.

  2. usesdoi:10.18112/openneuro.ds006065.v1.0.0

    Source dataset, version 1.0.0; the publication analyzes selected rest records from this release.

  3. doi:10.1038/s41467-025-59417-7
  4. doi:10.1016/j.neuroimage.2020.116761

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