Simulated pulse schedules matched with and without 25-sample gaps at a 301-sample interval in seven SEEG rest records with fixed readers and delays
We tested whether sensing gaps change later phase-based output times. We used four SEEG pairs from each of seven people in OpenNeuro ds006065 version 1.0.0. Each fixed reader used 250 past samples. Output delay and the main gap were each 25 samples. Each simulated output caused its own later input gap. It caused no signal change outside that gap. A proof gave the sufficient interval H+D+G+1, where H is history length, D is delay, and G is gap length. A crossing uses two consecutive forecasts. At 301 samples, about 602 ms, all 28 gap schedules matched no-gap schedules at that same interval. All 840 tested gap pair-conditions covered by the bound also matched. At the earlier 250-sample interval, hold gaps changed 44.07% of exact no-gap event times. The descriptive person interval was 39.09–49.12%. Total event count was unchanged, and mean phase error changed by 0.16 degrees. The main bound reduced pulse rate by 14.93%. The proof assumes fixed finite input support, exact delays, identical initial states, and no effect outside each gap. This exploratory study reuses rest data. It does not test a neural response or live stimulation benefit. The package preserves selected voltage, fixed models, every event schedule, proof, code, provenance, four figures, and reproduction instructions.
sensing gaps · finite input support · SEEG · phase forecasting · pulse scheduling · feedback replay
References
5- usesA frozen learned forecast reduced mean simulated zero-phase pulse error at 50 ms delay in seven SEEG rest records sci:sha256:24b1a90d4cff646d87950db8a063f0c4bd0f65b7475eb4cfe09200ba447a9436
This publication supplied the 28 selected SEEG traces, frozen 50 ms readers, phase reference, crossing rule, source receipts, and no-gap events. We reproduced all 16,403 main event indices and reference phases. We added continuous replay in which each output causes a sensing gap, a finite-support interval proof, pulse-time measures, and rate costs.
- 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
This work supplied the original early ridge fitting code and plan, standalone predictor, finite 250-sample model, and raw-source retrieval recipe through the direct source publication. We preserved that lineage and ran the early refit. All 50 ms penalties matched, and coefficient changes were below 1e-10. No new gap-specific model was fitted.
- usesFilters trained to omit gaps did not lower observed mean theta phase error after simulated 50 ms gaps in seven SEEG rest records sci:sha256:a8d7cb3ca8a096ce80c3f4fd70490ae83fef86e6813ca935507313208c144ec9
We used its description of forecast recovery after hypothetical sensing gaps to define the new gap question. Its tests used separate gaps at fixed query times. We instead tested each policy with its own repeated output-caused gaps and measured exact pulse schedules. We did not use its voltage arrays or fitted filters.
- usesdoi:10.18112/openneuro.ds006065.v1.0.0
The analysis used selected SEEG recordings from OpenNeuro ds006065 version 1.0.0. The archived source metadata identifies this exact version and records its license.
- doi:10.1038/s41467-025-59417-7
Cited by
0 publicationsPublic catalog entries on Sciscape.
No incoming citations on Sciscape.