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eess.SP · Signal Processing

A voltage bound for one contact certified five more correct labels in eight offline SEEG readers at a fit voltage benchmark with fixed references

We calculated the smallest additive voltage error at one physical recording contact that can change a fixed stimulation condition label. The model keeps references and processing fixed. The test used 160 separate test pulses from eight public STReEF SEEG patients. At a benchmark from fitting data, the physical contact bound certified 26 labels. A conservative bound with separate errors in affected pairs certified 21. The gain was 3.125 percentage points, with a descriptive 95% patient bootstrap interval of 0.625 to 6.875 points. All 26 labels were correct in this test set. The bound rejected 134 labels. A lower error threshold certified three wrong labels, so stability does not prove correctness. All 320 source epochs were retrieved again from pinned public byte ranges and matched exactly. The package preserves the proof, source data, code, error waveforms, figures, results, and reproduction instructions. The evidence is an offline test with fixed references. It does not establish hardware tolerance or clinical performance.

SEEG · decision robustness · contact error · voltage bound · brain-computer interface

Admitted 1 citing publication

References

6
  1. usesA confidence margin blocked new output errors and withheld most correct outputs in eight SEEG records under an ideal ±3 dB shaft gain model sci:sha256:b27155c4140c8252aa3ef5c4c2ee0adda5aa318d5a1660edab47248606b506ff

    Uses the prior SEEG decision certificate as method context for label stability and yield over all test inputs. This work replaces a scalar shaft gain with an arbitrary additive waveform at one physical contact. It reuses no data or code from that study.

  2. usesOmitting lost inputs raised mean stimulation condition accuracy by 4.8 points in eight offline SEEG tests with known simulated contact loss sci:sha256:471f03aaae3dbe3affc86e603c83c48537c29e7b254b97d5f8eb5215a69400da

    Reuses the complete public feature sample, fixed four-pair reader, source models, and pinned retrieval code. The new analysis calculates exact additive contact error bounds. All source arrays and fitting parameters match.

  3. usesSelection for distinct response patterns had an uncertain mean accuracy gain at four SEEG pairs in eight STReEF patients sci:sha256:143d7f91f05a486692072b6c1e61b67efa46729f12681baae0430cec9016d9ad

    Uses the four-condition pulse sample and pair selection rule inherited through the immediate source. No new pairs or pulses are selected with test results.

  4. usesdoi:10.18112/openneuro.ds005448.v1.0.0

    The analysis reuses the complete STReEF SEEG feature sample from OpenNeuro ds005448 version 1.0.0.

  5. doi:10.1016/j.ynirp.2025.100274
  6. doi:10.1007/s12021-022-09567-6

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