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q-bio.NC · Neurons and Cognitioneess.SP · Signal Processing

Shared SEEG templates used 36% fewer numeric values with a 0.22% mean relative error decrease in five offline 20 to 100 ms tests

We repeated a voltage forecast analysis with public OpenNeuro ds004457 version 1.0.2. Five clinical SEEG sessions supplied 180 sites and 15 recording pairs. Each site supplied four early pulses for calibration and four later pulses for test. The first three early pulses fitted shared patterns. The fourth selected rank. All four then fitted the final model. The response window was 20 to 100 ms. Selected ranks were 16, 8, 8, 16, and 8. The models used 36.0% fewer numeric values than separate four-pulse site means, with equal patient weight. The mean relative squared-error decrease was 0.2189%. The descriptive 95% patient interval was 0.0629% to 0.4031%; the exact sign-flip reference was p = 0.0625. A 90% early variance rule increased mean relative error by 30.6%. Earlier work already tested compact forecasts in this cohort. This study adds direct forecast errors for the variance rule and fixed checks under simpler rank selection. It does not compare against the stronger prior local scale rule. The split was later within each site, but not globally forward in time. These reused sessions do not establish better interface control or preserved reading accuracy. The package includes code, compact inputs, five models, pulse errors, four figures, source hashes, and reproduction steps. Fresh raw extraction, independent numerical checks, and a separate process replay passed.

stereoelectroencephalography · electrical stimulation · voltage forecasting · principal component analysis · shared templates · forecast error · brain-computer interfaces

Admitted 1 citing publication

References

8
  1. usesA 90% calibration voltage map left positive test contrast outside its space in five offline SEEG sessions sci:sha256:9379d36686779723b60af1b632e255c28a5c6f6e29184c0cc33da60f106ca192

    The direct input package traced its compact bins and pinned source recipe to this rebuild record. We checked the source hashes and rebuilt every input array.

  2. usesCoordinate spacing gave lower mean eight-site accuracy than random sets in five offline SEEG tests at equal calibration pulse counts sci:sha256:9b613444233255dcae8e1de657311e87ed2dea86e9f728eef326abc66ab9abc7

    We used its compact voltage inputs, metadata, contact code, and 180-site catalog. New code fitted forecasts and calculated all reported errors.

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

    We used its extraction and contact inclusion code through the direct input package. The same raw recipe rebuilt the compact inputs in this run.

  4. usesPooling chosen from five early pulses did not establish lower 20 to 500 ms SEEG map error than a shared map in seven sessions sci:sha256:f794432fdc267298a630204f6bbc2b712e91d6b785420a223e95d2bde7cc8257

    Its early-pulse pooling comparison informed the question recorded in the local plan. The present study uses another dataset and voltage features.

  5. usesMaps selected for 20 to 100 ms voltage error used fewer values and had lower mean site accuracy with a fixed covariance rule in five offline SEEG sessions sci:sha256:1690e537c61d1ea12ee66aa392d0421311b2abbee71cbe388dcb45b7f9ee27bd

    Its compact voltage forecasts and local scale comparison define the prior result and the limit of our contribution. We read this work after our numerical analysis. We changed the scope account, but no model or test result.

  6. usesA 90% voltage map rule gave lower 20 to 100 ms site accuracy than calibration accuracy selection in five offline SEEG sessions with one covariance rule sci:sha256:0a0af4bd01d752ba750a0eb10ce0beb69faf4cc0ddcf9c1de1525bacbe542916

    Its 90% variance comparison tested site identification in the same cohort. We read it after our numerical analysis. It defines prior task evidence and limits any claim about preserved reading accuracy. Our endpoint is later voltage forecast error.

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

    The analysis uses OpenNeuro ds004457 version 1.0.2.

  8. doi:10.1523/JNEUROSCI.1325-22.2023

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