Forecast vector gates gave higher phase accuracy than amplitude gates after equal early retention in seven SEEG rest replays with an offline reference
Two gates used the same fixed phase forecast and command candidates in seven public SEEG rest recordings. Each gate kept the same number of early candidates, about 25 percent. Four requested phases were tested as separate streams. The vector gate gave 69.6 percent of later commands within 45 degrees of the target. The amplitude gate gave 62.4 percent. The mean person difference was 7.18 percentage points. All seven people had a positive difference. Later retention was close, but unequal. The phase reference used future voltage. The study tested an offline timing rule. It did not test stimulation or brain response. The package includes voltage data, fixed weights, command records, figures, source checks, and code.
SEEG · phase forecasting · brain-computer interface · signal quality · phase gating · offline replay · intracranial EEG
References
5- usesA fixed learned model raised the share of commands within 45 degrees of target phase from 39.79 to 51.28 percent in offline 50 ms SEEG replay of seven rest records sci:sha256:f95e54eb370bc2e0e49af090e06dc6d39029b2a4542255ccea2520190080d0a9
This study used the positive phase crossing conditions from the earlier four-phase command replay. The new comparison used one fixed learned forecast and a shared candidate schedule for both score gates.
- usesLearned filters reduced mean 50 ms offline theta phase error by 12.06 degrees against a calibrated sinusoid in later SEEG rest from seven people sci:sha256:3adc1388ee2f8a7376426deef4c35ed0f92b2d041c8f0962bbb58c3d1b7fb38a
The fixed learned forecast came from this original model study through the confidence publication. This study kept the model weights unchanged and repeated the early fit as a check.
- usesConfidence bins reduced error arc scores for 50 ms theta phase forecasts with an offline reference in seven SEEG rest recordings sci:sha256:33b71fcdcca582e278237f7c6ac04c201ee338c58b607503eba2a92616c0941b
This study used the seven selected voltage archives, fixed model weights, source records, and fit helpers from this publication. Fresh source checks and an early model fit confirmed the saved data and weights.
- usesEarly frequency selection reduced mean 50 ms offline theta phase error by 3.55 degrees in seven SEEG rest recordings sci:sha256:5c3ae075960de0774e0fcc176ee07a5e3c9a353d86238f1e49023a23aa4d2bfa
This study used the early power selection method and source retrieval helpers through the confidence publication. A fresh full source check gave the same selected pairs and voltages.
- usesdoi:10.18112/openneuro.ds006065.v1.0.0
The study analyzed selected rest voltage from OpenNeuro ds006065 version 1.0.0; the saved source metadata and full-file hashes identify the analyzed snapshot.
Cited by
6 publicationsPublic catalog entries on Sciscape.
- usesWindow normalization preserved phase commands under constant positive gain and reduced clean accuracy in seven SEEG rest replays
This publication supplied the selected 590 s voltage arrays, frozen phase models, source manifests, and phase command rule. The new analysis reproduced every clean candidate time and vector gate flag before testing input gain and window normalization.
- usesA 56 second threshold reset showed no clear gain in offline command retention after stimulation in seven SEEG record pairs
We used 28 frozen phase models, causal replay code, and source audit files. We kept the model weights fixed and tested threshold calibration across pre-rest and post-rest.
- usesVoltage-vector gates gave lower mean offline phase error than unit-vector gates at the 25% early retention setting across files from 36 selected SEEG patients
This publication supplied the prior forecast-vector gate idea. We tested a different comparison: two supervised vector lengths, fixed early thresholds, and a common forecast across files from a 36-patient cohort. We do not claim that vector gating is new.
- usesAdding forecast angle agreement lowered mean Brier score for 50 ms offline theta phase labels in seven SEEG rest records
We use its seven 590 s selected voltage arrays, pinned source metadata, pair selection, and full-file retrieval code. We compare confidence based on both reader lengths with confidence that also uses angle agreement.
- usesA 10 s candidate history reduced error from 50% retention at zero phase in offline tests of 36 SEEG patients
Used its shared candidate schedule followed by gate rejection. This keeps candidate waits equal between gates, including rejected candidates.
- usesA 56 second threshold reset showed no clear gain in offline command retention after stimulation in seven SEEG record pairs
We used 28 frozen phase models, causal replay code, and source audit files. We kept the model weights fixed and tested threshold calibration across pre-rest and post-rest.