A 500 ms future buffer reduced theta phase label change compared with 50 ms in seven patients' rest SEEG with fixed reflection
We tested how future data change offline theta phase labels. We used 17 public rest SEEG files from seven patients. The labels used 4 to 10 Hz filtering, a Hilbert transform, and fixed raw reflection. Against a matched four second future reference, mean label change was 35.93 degrees with 50 ms of future data. It was 3.39 degrees with 500 ms. The mean decrease was 32.54 degrees, with equal weight for each patient. A descriptive patient bootstrap interval was 30.67 to 34.39 degrees. The decrease occurred in 7 of seven patient means. The exact sign flip probability was 0.015625, under an independent patient and symmetric null assumption. Fixed changes to reflection, past length, and extension length gave the same direction of group change. At 50 ms, 24.60% of success labels for a frozen forecast changed against the four second reference. At 500 ms, 2.35% changed. The reference defines processing stability. It does not establish biological phase truth. The package preserves native inputs, pinned sources, all query labels, code, tables, a figure, and complete numerical checks.
SEEG · theta phase · future buffer · offline phase labels · signal processing · phase sensitivity
References
6- usesForecast amplitude ranked 50 ms accuracy better than sine amplitude against offline theta phase in seven patients' SEEG rest data sci:sha256:3d6d4640ca14b2213e0b7b65f38363af780b8416fe2b4ccb0c74a8d8e55188c8
This publication supplied the 17 native SEEG inputs, source version pins, contact mapping, forecast code, and saved forecast arrays. We checked these dependencies and held the forecasts fixed for the new future buffer comparison.
- usesCalibrated linear forecasts had lower mean 50 ms error against offline theta phase than sine fits in seven patients' later SEEG rest data sci:sha256:1cd3c1c0512262d93ab30e567f06de8055b966e60e34fe87bac572b3e24ce770
This publication supplied the causal ridge benchmark and bounded source retrieval code, through the source publication. We used those methods to rebuild the fixed forecasts and to check all 17 raw source ranges.
- usesdoi:10.18112/openneuro.ds006065.v1.0.0
The analysis used 17 rest SEEG files from OpenNeuro ds006065 v1.0.0.
- doi:10.1038/s41467-025-59417-7
- doi:10.1016/j.neuroimage.2020.116761
- usesdoi:10.5281/zenodo.14735080
The source study software is cited and linked; its GPL-licensed archive is not redistributed.
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