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q-bio.NC · Neurons and Cognition

Past SEEG improved mean 100 ms voice level forecasts beyond audio history in ten recordings with corrected clocks

Past SEEG reduced mean person voice level forecast error by 1.50% beyond cue phase and quadratic audio history. The 95% person bootstrap interval was 0.50% to 2.58%. The test used 194 late word trials from ten public overt speech recordings in NEMAR nm000371. Inputs stopped at one cutoff on corrected release clocks. The target window ended 100 ms after that cutoff and started 50 ms after it. Six people improved, two worsened, and validation omitted SEEG in two people. Earlier work used the same test words. This result is exploratory. The package contains compact data, source recipes and hashes, model fits, full test forecasts, three figures, and reproduction instructions. Fresh raw files reproduced all source feature arrays exactly. Independent model checks and an exact repeat run passed. The result does not establish physical timing, neural origin, silent speech performance, or stimulation effects.

stereoelectroencephalography · voice level · forecasting · audio history · brain-computer interface · overt speech

Admitted 1 citing publication

References

5
  1. usesEight SEEG contacts reduced mean log speech amplitude test error by 12.1% beyond cue timing in ten recordings with corrected clocks sci:sha256:ded786a796f434af01186591335157992f3d58604e9d224da3b77aa84adf9e37

    This study uses the compact log power and audio level arrays, complete-word split, source metadata, extraction code, and public raw source hashes from this package. The same test words are reused. This study adds earlier shared cutoffs and audio history controls. A fresh raw rebuild reproduces all inherited arrays.

  2. usesPast SEEG ridge readers did not lower 100 ms sound forecast error beyond quadratic sound history in ten listeners sci:sha256:864def8029180e5ee03c71dce97442d6971b43dd990d386b726cd2f15252b579

    This study uses the shared input cutoff and five-lag quadratic audio history design as a diagnostic for added SEEG forecast value. It applies the design to overt word production with independent code. It does not reuse the passive listening data or make a causal cohort comparison.

  3. doi:10.1038/s41597-022-01542-9
  4. usesdoi:10.17605/OSF.IO%2FNRGX6

    The study uses original NWB files for the Dutch single-word speech recordings preserved in NEMAR nm000371.

  5. usesdoi:10.82901/nemar.nm000371

    The study used original NWB content from NEMAR nm000371, version 1.0.0, at the recorded pinned commit.

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