Products of two stable cycle maps were unstable in 11 of 36 fitted SEEG readers on a fixed timing grid
We tested changing gap timing in 36 fixed SEEG readers from nine public rest records. Each reader had four fixed gap models. On a 16-member timing grid, 24 of 9,216 ordered two-map products were unstable although both member cycle maps were stable. The failures occurred in 11 readers from seven records. These are numerical state results, not brain instability. A new guard limits each gap-map operator norm to 0.95 without the next observed interval. With compatible models, exact completed sample counts, and shared voltage history independent of the state estimate, a proof gives contraction for arbitrary supported gap sequences and nonnegative observed intervals. All 144 guarded maps met the limit. On the primary mixed schedule, the guard raised mean gap-end phase error by 11.46 degrees relative to held voltage. All nine record means had more error. The descriptive 95 percent record bootstrap interval was 9.48 to 13.39 degrees. The reused cohort makes this an exploratory test. It does not show improved overall interface performance. The package preserves code, fixed models, selected voltage, source receipts, four figures, and reproduction commands. A second analysis matched all 115 numerical files exactly. Fresh source checks matched all 7,200,000 selected voltage values.
SEEG · stereoelectroencephalography · missing-input estimation · state-space filters · switched systems · operator norm · phase error
References
5- usesTwo of 36 learned SEEG state maps were unstable during 100 ms simulated gaps every 120 ms sci:sha256:28dec263a684c3edc55154127a63ea5a4656288fb3acc603cfe9f34ad9e44956
This study used the nine selected voltage arrays, 144 fixed models, source receipts, and previous interval guard. It tested changing timing and added a guard with no future observed interval.
- 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
This study used the fixed planned 27-feature gap models and their early voltage calibration. No weights were refit and no contacts were selected again.
- usesdoi:10.18112/openneuro.ds006065.v1.0.0
The study used selected voltage records from ds006065 version 1.0.0, as recorded in the source metadata and provenance.
- usesdoi:10.18112/openneuro.ds005670.v1.0.0
The study used selected voltage records from ds005670 version 1.0.0, as recorded in the source metadata and provenance.
- doi:10.1038/s41467-025-59417-7
Cited by
4 publicationsPublic catalog entries on Sciscape.
- usesA constrained fit reduced mean 7 Hz filter phase error against a guard applied after fitting in nine SEEG records with simulated mixed gaps and known initial states
This test uses the selected public voltage, fixed models, scalar gap guard, timing schedules, and parent endpoint checks.
- supportsWith shared input history, singular value clipping bounded state sensitivity in 36 fixed 100 ms SEEG readers
Our proof and fixed 100 ms replay checks support the shared-history endpoint norm argument. This study does not test that publication's variable-gap grid. It adds the least matrix change projection and direct accuracy comparison.
- usesA reader of past voltage reduced mean 7 Hz filter phase error in nine SEEG rest records with mixed 20 to 200 ms simulated gaps and known initial states
This archive supplied the nine corrected voltage arrays, early calibration, 144 state-feedback models, exact mixed and secondary gap schedules, and archived guard scores. We used them for paired comparisons with two newly fitted voltage input rules.
- usesFitting under a 0.95 state norm limit reduced mean 7 Hz filter phase error by 14.26 degrees against a scaled guard in nine SEEG records with changing simulated gaps of 20 to 200 ms
We reused its nine selected voltage extracts, four fixed pairs per record, original models, changing gap schedules, streaming buffer rule, and scaled norm guard. Its measured accuracy cost motivated fitting under the same norm limit.