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Time pooling reduced SEEG prediction band width by a mean of 61.7% and mean four-pulse coverage fell to 86.3% in five patients

We tested prediction bands for completed 20–100 ms stimulation responses in public SEEG data from OpenNeuro ds004457 version 1.0.2. Each site used four fit pulses and four later pulses. Earlier sites set a maximum-error threshold for all pulses, recording pairs, and time bins. Later sites tested it. Pooling variance across time reduced normalized band width by a mean of 61.7% across five patients. Mean complete-batch coverage fell from 93.2% to 86.3%. The two methods covered 52 and 48 of 56 tests. Pointwise Student t limits covered 90.4% of coordinates but only 8.8% of complete batches on the equal-patient mean. The result shows a width and coverage tradeoff in this selected cohort. It does not establish equal coverage, a future coverage guarantee, or clinical benefit. The package preserves source bins, pinned retrieval code, all results, four figures, and replay instructions. A fresh raw-data rebuild exactly matched all 30 input arrays. A clean replay reproduced all scientific tables and 95 prediction arrays.

stereoelectroencephalography · electrical stimulation · prediction bands · conformal prediction · coverage · brain-computer interface

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References

4
  1. usesStudent t limits scored worse than normal limits for centered SEEG voltage at 86 selected sites after five fit pulses sci:sha256:012478d084fb27a8d92d12089b83be731e42bdd06cd15828d578bf1698fc0e58

    We read its pointwise and complete-waveform coverage results to define the coverage units in this investigation. Its dataset is ds004696. No data or code from that study entered this ds004457 analysis.

  2. usesA two-pulse site rank did not show lower 20–100 ms SEEG forecast error than a spread budget in five sessions sci:sha256:1992325c3f451df1d04c3ddb1db9e235b387fdd80733c220eacfe8b81353a15c

    We used its fixed recording contacts, saved contact bins and event tables, BIDS metadata, pinned source manifest, and retrieval and extraction code. We checked input archive hashes and exactly rebuilt the input arrays from public MEF files. Its response ranking results are not new results in this study.

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

    This version of OpenNeuro ds004457 supplied the SEEG recordings used in the analysis.

  4. supportsdoi:10.1523/JNEUROSCI.1325-22.2023

    This source study describes the data collection and stimulation context for the analyzed SEEG dataset.

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