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Offline filtering reduced 70–110 Hz power change error against native SEEG in two-second windows from 35 records converted to 256 Hz

We tested a fixed anti-alias filter on 35 public motor-stimulation SEEG records from 21 patients in OpenNeuro ds006519 version 1.0.0. At 256 Hz, mean power change error fell from 0.551869 dB with direct sample selection to 0.000181 dB with filtering. The feature used 70–110 Hz power in two-second windows. Each record used channel medians. Each patient had equal weight. All 21 patients showed lower error. The paired reduction was 0.551688 dB. Its descriptive 95 percent patient bootstrap interval was 0.463518 to 0.645850 dB. The other planned checks also showed lower error. The package preserves code, spectra, source hashes, exact windows, figures, and retrieval instructions. Independent checks and a full source rebuild passed. It includes a recorded window-length amendment after a failed source-duration check. This offline test uses stored native SEEG as the reference. It does not establish decoding accuracy, artifact removal, or safe online sensing.

SEEG · signal processing · anti-alias filtering · resampling · power spectral density · brain-computer interface · reproducibility

Admitted 2 citing publications

References

3
  1. usesA fixed detector found periodic voltage runs in 31 of 35 SEEG records from ds006519 version 1.0.0 sci:sha256:ea4058aa1dce526e987cca14e771bbeab34ccf9025624ac6983dbb7c42876d8e

    Uses the source manifest, retrieval code, source metadata, and event-field audit for ds006519 version 1.0.0. The new study calculates power features from the full source voltages. It does not use the prior timing detector or its outputs to select records, channels, or windows.

  2. usesdoi:10.18112/openneuro.ds006519.v1.0.0

    This study uses all 35 released records from OpenNeuro ds006519 version 1.0.0.

  3. doi:10.1016/j.neuroimage.2026.121822

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