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Neighboring SEEG site responses had estimated shape separation of 0.16 at 20–100 ms in five sessions under stable means and independent group errors

We tested whether neighboring stimulation sites had repeated response shapes beyond one signed size change. We used public OpenNeuro ds004457 version 1.0.2. Five patient sessions provided 183 eligible sites and 1,464 pulses. The primary test used 122 neighboring comparisons at 6 mA. Four separate pulse groups estimated signal energy and collinearity moments. The component estimates are unbiased under stable site means and independent zero-mean group errors. The ratio is biased. At 20 to 100 ms, the equal-patient mean shape estimate was 0.159, with a patient range of 0.025 to 0.341. Other same-current comparisons had a mean of 0.460. A few strong comparisons carried most of the estimated weight. Before-pulse controls had unstable denominators. Simulations showed that shared group errors can produce false shape separation. An independent calculation matched all 4,034 saved pair results. Replay reproduced eleven output files and 120 arrays. A fresh one-patient raw reconstruction gave byte-identical inputs. The package includes all five-session analysis inputs, source hashes, code, three figures, results, proof, checks, and reproduction instructions. The result is an offline calibration benchmark. It does not show safe stimulation, distinct percepts, or better online control.

SEEG · electrical stimulation · response shape · collinearity · squared angle · noise correction · repeated pulses · brain interfaces

Admitted 1 citing publication

References

7
  1. usesCovariance angles kept 37.9 percent mean site accuracy under nine simulated positive common voltage gains in five offline SEEG sessions sci:sha256:0dbd4e1ba615799ff752cce0c46663f54b7af5ebdf8dd5a55d3ee4cebeb486e4

    Its angle-decoding result provided scientific context for positive-gain stability. The present study estimates signed scalar collinearity with squared angles, a fixed Euclidean metric, and no trained classifier.

  2. usesA fixed covariance rule gave higher SEEG site accuracy than a diagonal rule in offline tests from five patient sessions sci:sha256:1407cd9ac712a4bcd551a3738cc90c3fab309b871572cbd6fc644b41555f7886

    The study reused the exact hash-checked voltage bins, event tables, source metadata, source hashes, and retrieval and extraction code from this package. An independent inclusion implementation matched its physical pulse allocation.

  3. usesSeparate pulse groups showed larger 20–100 ms SEEG site distances than pre-pulse distances in five sessions under a fixed noise scale sci:sha256:9f1e90015b0bdafdea3d9064d667fecee53bf4a1f7b6784f4b237b4b812d4c1b

    Its repeated site-distance result defined the scientific gap: voltage distance does not separate response size from shape. The present study did not import its fitted noise scale or result arrays.

  4. usesMean estimated SEEG site map alignment was 0.28 for 20 to 100 ms and 100 to 500 ms in five sessions under stable means and independent group errors sci:sha256:3da61344038cc338985933849b56f820fd6f4dd48b236c6f72c5aa873d6fbe28

    Its use of four separate pulse groups informed the product estimator and the ratio-bias caution. The present study wrote new pair-level collinearity and validation code. It did not reuse the site-map result arrays.

  5. usesdoi:10.18112/openneuro.ds004457.v1.0.2

    The analysis used SEEG stimulation data and acquisition metadata from this exact OpenNeuro release.

  6. usesdoi:10.1523/JNEUROSCI.1325-22.2023

    This is the original acquisition study for the source dataset.

  7. doi:10.1371/journal.pcbi.1005508

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