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eess.SP · Signal Processing

Sine fits did not establish lower Brier error than linear repair in eight SEEG records with matched training and 20 percent periodic loss

We tested separate sine fits against linear repair of known sample gaps in eight public SEEG records. Each 500-sample window lost 50 samples per 250 samples. Each method used its own logistic reader, trained with ten gap positions. Ten different gap positions were used for later tests. Mean Brier error was 0.18112 for sine fits and 0.18166 for linear repair. The paired mean difference was -0.00054; the person bootstrap 95 percent interval was [-0.00477, +0.00376]. This result did not show a clear error decrease. It does not establish equal performance. Sine fits had higher error when readers learned only from complete data. We preserve the integer windows, source versions and hashes, code, masks, fitted readers, scores, and figures. The test uses simulated gaps and task-state labels in reused epilepsy records. It does not test live control or neural stimulation.

SEEG · periodic sample gaps · sine fit · linear repair · Brier error · power features · task-state classification · OpenNeuro

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References

6
  1. usesGaps of 50 samples gave higher later Brier error than gaps of 1 sample with linear repair and training on intact data in eight reused SEEG records at 20 percent loss sci:sha256:3c89bd7257f7bec674ddc63c692c0bc98521a393ec34f50a07a13af56427ad8c

    This study supplies the earlier gap-training result and 20 percent periodic-loss comparison. We add a separate observed-sample sine-fit map and use different fixed gap positions for training and testing.

  2. usesPower blocks with 50 percent overlap increased correct SEEG outputs with a guarantee for a fault of ten samples on one contact in eight recordings at C = 1 sci:sha256:a050316204bcb2071a4520dd1b49c79393798b8c3b3161826ec9da3229fa2f22

    This study supplies the exact integer SEEG windows, source recipes, and fixed selected pairs. We keep its trial order and split while testing two power maps under periodic gaps.

  3. usesTen spaced gaps gave lower mean zero-fill SEEG accuracy than a circular gap block at 20 percent simulated sample loss in eight fixed decoders sci:sha256:35c3a178c2212bd9b314433661dfc484678cb20f0c291b7c94bc044397f7c9b9

    This study is the native integer-window source in the data chain. Its pinned OpenNeuro ranges permit a new source audit and compact-window rebuild.

  4. usesEight SEEG pairs classify pre-response and rest windows at 70.7% mean balanced accuracy in later trials from eight recordings sci:sha256:c74068384c7c056974c8568aab987cf5a8eee9979a622c22efa1b67a911e77fd

    This study supplies the inherited event screen, voltage screen, training-only pair selection, and EDF range helper code. We preserve its choices and MIT notice.

  5. supersedesSine fits did not establish lower Brier error than linear repair in eight SEEG records with matched training and 20 percent periodic loss sci:sha256:7c42281535dfe9e4d8b000a7bc8ef1a09ddd57f7acf8c3b76fcbab0d08891972

    This package corrects the file hash checker so it skips the metadata folder added by sci fetch. The scientific inputs, results, figures, and conclusions are unchanged.

  6. usesdoi:10.18112/openneuro.ds004473.v1.0.2

    Source dataset for the eight fixed SEEG records and integer windows; the analyzed version is 1.0.2.

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