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eess.SP · Signal Processing

A voltage cap from the past second failed in 34 of 1104 simulated 10 ms gaps in 35 SEEG records

We tested whether past voltage can bound error after SEEG input loss. We used 35 public records from 21 patients in OpenNeuro ds006519 version 1.0.0. Each record supplied four channels. The main test had 1104 independent simulated 10 ms gaps. Missing input was replaced with the median of the preceding second. The error cap was the largest past voltage difference from that median. Thirty-four gaps exceeded the cap. The equal-patient cap failure rate was 3.0 percent, with a descriptive 95 percent interval from 1.8 to 4.3 percent. A causal 70 to 110 Hz RMS feature used a trailing 100 ms window. A fixed 100 ms wait left an error above 10 percent of past band RMS in 932 gaps. The patient mean rate was 83.1 percent, with an interval from 78.9 to 86.8 percent. A bound that includes filter and window memory required a mean wait of 218.6 ms, with an interval from 215.1 to 222.4 ms. No accepted output exceeded the limit in the tested 500 ms interval. The proof requires a valid missing-voltage cap and equal prior filter state. Observed coverage cannot validate a future cap. The package preserves voltage, code, proof, figures, scores, hashes, and checks. Fresh source files rebuilt all 35 arrays exactly. This audit shares patients with related sci work. It does not measure decoder benefit or live brain stimulation.

SEEG · signal processing · causal filtering · RMS feature · missing data · error bounds · brain-computer interface

Admitted 1 citing publication

References

7
  1. usesQuadratic power limits reduced grid waits after simulated 10 ms SEEG gaps in 21 patients with missing voltage RMS bounded by five times past RMS sci:sha256:a570193dd89a65cda55f474e448a78d019ca20dd16fc04f243f3e34117c944e5

    We used this related same-record study for comparison. It compares quadratic and triangle power limits under a missing-input RMS budget. Our test uses a per-sample voltage cap and RMS feature error. We read it after our main numeric analysis. It supplied no calculation inputs.

  2. usesA complete saved state reproduced causal power after restarts in 35 SEEG records with no input gaps sci:sha256:d4cdf85cce272a8a687213150339eac50e9e6430ca265dbcef84cd2277e3810c

    We used all 35 four-channel voltage arrays, source metadata, source channel order, the analysis-window rule, and the voltage loader and retrieval code. We wrote the new gap analysis and bound tests. These studies share patients.

  3. usesA centered 100 ms mean used future voltage after causal filtering in 10 SEEG recordings sci:sha256:12aa05a70c371c12f0af618bb7453b8c12ae93f6d4378026d1ad0b1ecad09c1f

    This is the source voltage package for the immediate data dependency. Its selected raw arrays and source hashes support the same 35-record input used here.

  4. supportsA 1 mV gap bound kept accepted 70–110 Hz power error below 10% of baseline in 35 SEEG records sci:sha256:9b1067beac30b652ee1e0e4f0fe2e8b3744dcde68a9ec304ac6b55eaa51c020b

    Our 34 cap failures in 1104 simulated gaps support the stated limit of a cap from past voltage. Our gap rule, median imputation, and RMS error measure differ from its repeated zero-filled gaps and power measure. The studies share 35 records and do not form independent patient replication.

  5. usesA complete power bound prevented releases with error above one percent of early mean power after 10 ms gaps filled with zero in 35 SEEG records from 21 patients under a missing voltage cap sci:sha256:9c04f7931c0398bdacd9fac632db0bf3dce45c88e98f874f06e033a624dd334c

    We used this related same-record study for comparison. It compares complete power-window and current-sample gates under an external missing-voltage cap. We read it after our main numeric analysis. It supplied no code, data, or numeric inputs to our calculations.

  6. usesRepair norm budgets gave a median 21.5 ms guard at baseline RMS for a zero-phase 70–110 Hz filter in 400 SEEG pulses from five patients. sci:sha256:e194ff5fccc361a354bcbb5053d320a630739d2142591ebb65ade54bb5765584

    We used this related method for comparison in the report. It states a finite filter norm bound and the RMS reverse triangle inequality for offline signal repair. We read it after the new numeric analysis. It supplied no code, data, or numeric inputs to our calculations.

  7. usesdoi:10.18112/openneuro.ds006519.v1.0.0

    Source dataset, version 1.0.0. The analysis uses 35 released records; archived source metadata identifies this exact version and CC0 license.

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