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Early loss tests did not show better contact priorities than coefficient size under simulated loss in eight SEEG recordings

An early accuracy loss rank did not show better contact priorities than coefficient size in this test. The study used eight public SEEG recordings from OpenNeuro ds004473 v1.0.2. A fixed decoder used eight bipolar pairs to classify windows before a response against rest windows. Training, early ranking, and later testing used separate time blocks. Each known simulated contact loss disabled every pair that used that contact. An early accuracy loss rank selected a mean later loss of 2.33 percentage points. A coefficient size rank selected 3.75 points. The paired difference was -1.42 [-3.92, 1.23] points, with a descriptive 95 percent person bootstrap interval. The exact sign-flip reference was p = 0.375. Planned checks gave no clear gain. The package preserves features, raw audits, contact masks, models, predictions, figures, source versions, and reproducible code. This is an offline task-state test in a small reused cohort. It does not test actual faults, online control, or stimulation.

stereoelectroencephalography · brain-computer interfaces · contact loss · feature ablation · decoder robustness

Admitted 1 citing publication

References

5
  1. usesLoss-specific fits did not establish a mean accuracy gain at threshold 0.5 under known single-contact loss in eight SEEG sessions sci:sha256:e4f3b11d4be4acc9877938d259b2586721cc56650b5c292c875eab422cd5b4f3

    This study uses its known single-contact loss setting. A missing contact disables every selected bipolar pair that uses it. The new question concerns prediction of later loss, with no model refit.

  2. usesRefitting with saved data gave a small and uncertain gain after simulated loss of one contact in eight SEEG decoders sci:sha256:1f270c885459d2fb124ecc2c62c7754d784fca31de12e57fd812ddb127fcdae7

    This study uses its contact loss and refit result as research context. The new test concerns an early contact priority rank rather than recovery through refitting.

  3. 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 uses its exact SEEG power features, trial eligibility, source metadata, raw audit ranges, and extraction code. It trains new decoders and tests contact priorities in separate time blocks.

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

    The analyzed SEEG recordings and source metadata are from OpenNeuro ds004473 version 1.0.2.

  5. doi:10.1088/1741-2552%2Facae0a

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