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q-bio.NC · Neurons and Cognition

Background subtraction reduced mean SEEG site accuracy at 20 to 100 ms with four fit pulses per site in five offline sessions

We tested whether predicted background voltage improves identification of known stimulation sites from SEEG responses. We reused OpenNeuro ds004457 version 1.0.2 from five people. The main test used four fit pulses per site, 94 later sites, 376 held-out pulses, and voltage from 20 to 100 ms. Equal-person mean accuracy fell from 43.98 to 42.56 percent after subtraction. Four people had lower accuracy and one had no change. Known-site waveform mean squared error fell by 3.89 percent. Thus, the waveform gain did not give a site reading gain in this test. A two-fit-pulse check had the opposite direction. Removing four sites with bad stimulation-channel labels kept the main direction. The small reused sample and retrospective template library limit the result. The package includes compact voltage data, every reader decision, fitted models, source hashes, raw retrieval code, independent numerical checks, and three figures. A fresh source extraction and a full numerical replay matched the preserved inputs and main results.

stereoelectroencephalography · electrical stimulation · site identification · background subtraction · waveform prediction · brain-computer interfaces · Mahalanobis distance · retrospective validation

Admitted 1 citing publication

References

6
  1. Background voltage prediction reduced mean centered SEEG error at 20–100 ms by 3.89 percent compared with four-pulse templates at 94 later sites in five sessions sci:sha256:eafc98e2d7e10954c617a9c872856a509fd1f40b5c5dbb9641372fbd1915461d

    [Legacy relation: reproduces] The five fitted main background maps matched its archived parameters exactly. The same 94 sites and 376 pulses gave its 3.89 percent mean waveform MSE reduction. This is a computation check on reused data, not an independent cohort result.

  2. usesBackground voltage prediction reduced mean centered SEEG error at 20–100 ms by 3.89 percent compared with four-pulse templates at 94 later sites in five sessions sci:sha256:eafc98e2d7e10954c617a9c872856a509fd1f40b5c5dbb9641372fbd1915461d

    We reused its input loader and background map design, including earlier sites, translated windows, and fixed ridge setting. We tested whether its waveform forecast gain transfers to stimulation site reading.

  3. usesBefore-pulse covariance gave both higher and lower SEEG site accuracy than response covariance with two fit pulses per site in five offline sessions sci:sha256:a3a65132746cb6df7b8aa882956d025828523525aef7faf429fa78a069d7446c

    We used its pooled response covariance reader design and its limit from retrospective calibration timing. We fit separate raw and residual readers. The main test used four fit pulses per site, with a planned two-pulse check.

  4. usesNeighbor weights from earlier sites did not reduce mean relative 20–100 ms SEEG forecast error against local scaling in five sessions sci:sha256:0b60e4e4cf7afdbf18dc66d47cc59bb5d1b66c4baf95e8cc5ec9c0fb6a937e56

    We reused its compact contact voltage, event tables, fixed contact settings, raw retrieval and extraction code, and source manifests. A new public raw download and extraction matched all compact array fields and event table bytes.

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

    The analysis used the recorded OpenNeuro ds004457 version 1.0.2 source data; the package preserves its retrieval URLs and file hashes.

  6. doi:10.1523/JNEUROSCI.1325-22.2023

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