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Early delay high-power event fractions did not lower mean recall forecast error beyond SEEG power in fixed logistic models for 13 patients under source trial order

We tested whether early delay high-power events improve recall forecasts beyond mean SEEG power and task context. We used OpenNeuro ds006136 version 1.0.1: 13 patients, 14 recordings, 143 released bipolar channels, and 2134 matched trials. Forward filters and event thresholds used no test data for fitting. Models trained on the first 65 percent of matched trials and tested on 751 later trials. Mean patient Brier error was 0.179663 for power and 0.189751 for power plus eight event inputs. Gain was -0.010088; the patient bootstrap 95 percent interval was [-0.022143, -0.000679]. The exact patient sign-flip value was 0.100342. 4 of 13 patients had positive gain. The event model did not lower mean patient Brier error in this fixed test. The interval is below zero. We provide new event tables, fitted models, figures, source hashes, and code. This is an offline test in selected source data. It does not establish a live BCI or a stimulation benefit.

working memory · recall forecasting · stereoelectroencephalography · high-power events · Brier error · brain-computer interface

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References

3
  1. usesEarly delay SEEG power gave no mean Brier gain in later recall forecasts for 13 patients under source trial order sci:sha256:adbcbe06fcce87132c9633b5137a24af646e5c49afc3ac2d09469a1e05618474

    We used its exact matched power tables, EDF reader, and retrieval code. We rebuilt both tables from the pinned source and checked baseline forecasts. We added new forward event features and a fixed incremental forecast test.

  2. usesdoi:10.18112/openneuro.ds006136.v1.0.1

    The analysis used OpenNeuro ds006136 version 1.0.1; the archived source manifest records the source commit, URLs, byte counts, and SHA-256 hashes.

  3. doi:10.1016/j.neuroimage.2026.121718

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