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eess.SP · Signal Processingq-bio.NC · Neurons and Cognition

A learned state with the last voltage sample reduced mean 7 Hz filter phase error against held voltage after 20 ms simulated gaps in 36 SEEG patients with exact pre-gap states

We tested a gap recovery model in 36 selected SEEG patients from OpenNeuro ds004100 version 1.1.3. The model added the last voltage sample to 27 past features. File A supplied all fits and choices. File B supplied the test. After nominal 20 ms gaps, mean 7 Hz filter phase error was 5.38 degrees with 28 features and 5.92 degrees with held voltage. The mean reduction was 0.54 degrees. The descriptive 95% patient interval was 0.40 to 0.70 degrees. 31 of 36 patients improved. The reduction against 27 features was 1.67 degrees, with all 36 patients improved. Gaussian controls showed that a gain over held voltage alone does not prove temporal prediction. Missing-input state error was also below zero fill in all 144 SEEG pairs at 20 ms. All 72 fresh source checks passed. Independent numerical checks and a full replay passed. The package preserves inputs, fits, predictions, code, source records, and four figures. The result requires exact pre-gap states and isolated simulated gaps. It does not establish neural phase truth, measured artifact recovery, or clinical benefit.

SEEG · intracranial EEG · filter recovery · phase error · missing samples · causal filter · ridge regression · OpenNeuro ds004100

Admitted 1 citing publication

References

5
  1. supportsA learned state reduced mean 7 Hz filter phase error after 100 ms simulated gaps in nine SEEG rest records with known initial states sci:sha256:101556a678d7a1131cc51640655ad1744d73bf01ea5d5882f68258510d434efd

    The exploratory last-voltage feature had lower mean 20 ms gap recovery error than held voltage and the 27-feature model in 36 selected ds004100 patients. The held-voltage reduction was 0.54 degrees, and 31 of 36 patients improved. This supports the feature in this new dataset under exact pre-gap states. It does not reproduce the same cohort or establish live stimulation performance.

  2. usesA learned state reduced mean 7 Hz filter phase error after 100 ms simulated gaps in nine SEEG rest records with known initial states sci:sha256:101556a678d7a1131cc51640655ad1744d73bf01ea5d5882f68258510d434efd

    We adapted the causal gap reader, missing-input regression target, 27-feature model, exploratory last-voltage feature, and recovery identity. We tested both feature sets in a different public SEEG dataset with file A fitting and file B tests.

  3. usesOther shafts increased mean 70–110 Hz forecast error after removal of a 60 Hz fit in ridge models transferred between files from 36 SEEG patients sci:sha256:1c253197c3da76fad6c67aa954cbc5cfc87533a0045fd578dc1683888ff54373

    The inherited source audit script credits this publication. We used that script to read all 72 pinned EDF prefixes again and compare every selected digital sample and calibration with the archived input.

  4. usesPhase and future signal scores chose different SEEG pairs in 17 of 36 patients with a 50 ms phase target sci:sha256:939487ab19ef8b2fa938b85b4be0b2c4febc393393a4654ef5ab5fa049792697

    We reused all 72 exact native digital input arrays, the 36-patient and four-pair selection, and inherited source records. The new study made no exclusions. We also adapted its metadata check and carried its retrieval and audit scripts into the new package.

  5. usesdoi:10.18112/openneuro.ds004100.v1.1.3

    The study analyzes selected SEEG samples from the HUP iEEG Epilepsy Dataset, OpenNeuro ds004100 version 1.1.3. The saved dataset metadata identifies this exact version and its CC0 license.

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