A 20–100 ms response update did not establish lower 100–500 ms SEEG forecast error with five calibration pulses in eight sessions
We tested whether the 20–100 ms voltage response to a stimulation pulse improves a forecast of centered SEEG voltage at 100–500 ms. We used public OpenNeuro ds004696 version 1.0.1. The test had eight patient sessions, 86 stimulation sites, and 886 stimulation-to-readout pairs. At each site, the first five good pulses fit the model and the final five tested it. Calibration selected the update size. Mean error reduction versus a noise-scaled late mean was 0.0816%. The descriptive 95% patient bootstrap interval was −0.0030% to 0.1727%. The exact patient sign-flip p value was 0.1875 under independence and sign-symmetry assumptions. The test did not establish lower error or equal performance. A fixed half update increased mean error by 2.24%, with higher error in seven patients. This control result is descriptive. A clean run reproduced all tables and 88 model arrays. Three complete public trial files rebuilt exactly, with 1,430 direct waveform loss checks. The package preserves code, exact data products, every test score and prediction weight, four figures, source versions and hashes, and reproduction instructions. The test uses a reused benchmark and offline quality labels. Calibration is chronological within sites but retrospective across sites. It does not establish an online or clinical benefit.
stereoelectroencephalography · single-pulse stimulation · voltage forecast · forecast error · calibration · intracranial EEG · signal processing
References
4- usesFive-pulse noise scaling reduced later single-pulse error for 20–500 ms centered bipolar SEEG in eight patient sessions sci:sha256:fbc06ab469d48eac7a6f54dd97cd24ccd9c7c5ca4b0db4d64340536b3e81428a
We used its clipped five-pulse noise-scaled late waveform as the reference forecast. We also used its source extractor lineage through the exact-product parent. The new comparison tests the added value of an earlier same-pulse measurement.
- usesA three-part test did not establish lower 20–500 ms SEEG forecast error than one scale in eight sessions sci:sha256:3750642d11ed4ab80768651b3daf5b561fbea1c78c7dd75cb9dcef493b4736e6
We used its 25 exact-product data files, the chronological first-five and final-five pulse roles, the 886 spatially defined pairs, source records, and extraction code. All inherited data hashes matched. New source rebuilds for three sessions matched all derived arrays and tables.
- usesdoi:10.18112/openneuro.ds004696.v1.0.1
The analyzed source is OpenNeuro ds004696 version 1.0.1; this exact release supplied the trial voltage and metadata.
- usesdoi:10.1523/JNEUROSCI.2201-22.2023
This is the data paper named by the source dataset metadata for the OpenNeuro recordings used in the analysis.
Cited by
4 publicationsPublic catalog entries on Sciscape.
- usesPre-pulse ridge updates had lower mean relative forecast error than early updates in five SEEG replays with earlier fits from other shafts
Its early-to-late update question in another dataset motivated this test. The present fits also restrict all rows to earlier completed responses.
- usesEarly SEEG error did not establish better late error ranking than a matched before-pulse score after five calibration pulses in eight people with epilepsy
Its early voltage forecast update result informed the separate question of early error ranking. Both studies use the same people.
- usesA shared early response update gave no clear 100–500 ms SEEG forecast gain after five calibration pulses in eight sessions
The separate waveform update test informed the early-to-late question and the scaled-template baseline. The new rule restricts the update to the local late mean and shares one scalar coefficient across only sites whose calibration responses ended before each test cutoff.
- usesA five-pulse derivative update gave a 0.155% mean SEEG forecast gain at 86 selected sites in eight patients
This study used its separate early and later windows, noise-corrected mean reference, and calibration-only shrink approach. It tested a constrained derivative update beyond the same amplitude update.