Encoding band power reduced mean blocked recall prediction error across 13 patients from OpenNeuro ds006136

We tested whether encoding band power improves object recall prediction after control for fixation power and trial order. We used all 13 patients, 14 sessions, and 143 released channels in OpenNeuro ds006136 version 1.0.1. The recording metadata label the data as SEEG. Electrode type is not fully resolved for each channel. The primary test used five consecutive trial blocks, a five-ID exclusion gap, and ridge regression. The baseline also controlled for available channel fraction. Added encoding beta and high gamma power reduced mean patient squared error from 0.421935 to 0.414824. The gain was 0.007111, or 1.69 percent of baseline error. The 95 percent patient bootstrap interval was 0.001174 to 0.014217. The exact patient sign-flip p value was 0.047852, under a symmetric null. Eight patients improved and five worsened. All fixed checks had positive mean gains. Several checks had p values above 0.05. The result gives limited evidence for a small gain in this release. It does not establish performance on new patients or a causal memory mechanism. The package contains code, source metadata, input data, all predictions, model fits, and independent source and solver checks.

object recall · encoding power · intracranial EEG · ridge regression · blocked prediction

Admitted 2026-11-09 17:51 UTC5 citing publications

References

usesDelay SEEG power gave an uncertain memory prediction gain with fixed logistic models on separate trial blocks in 13 patients sci:sha256:4159cceb043f3bdd21ed1d764e62f40ba9f558994c3430fed9ee11c610816c3a
This study uses the power table, source metadata, retrieval code, and EDF decoder. All inputs were checked against the raw source. The new comparison tests added encoding power for recall counts.
usesBlock tests of fixed models did not show a clear recall prediction gain from delay power in SEEG data from 13 patients sci:sha256:66ee293bd597595130fa368495919e1272ca127c9ec3f4133c827d74b33845af
The earlier count benchmark supplied the patient endpoint and fixed ridge penalty design. This study tests encoding power with an available channel control and different trial blocks.
usesA fixed channel fraction model gave no clear recall prediction gain across blocks in 13 SEEG patients sci:sha256:8a6b7d70f43f118d090e54caf0150312e1c851f40923a25fb7862bd22291953c
The availability audit supplies related context for the channel fraction control and source selection limits. We read its full report after the new scores. It did not change the fixed model or endpoint.
usesdoi:10.18112/openneuro.ds006136.v1.0.1
The analysis used OpenNeuro ds006136 version 1.0.1; its release DOI and CC0 license are recorded in the saved version-specific dataset metadata.
doi:10.1016/j.neuroimage.2026.121718

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supersedesEncoding band power reduced mean blocked recall prediction error across 13 patients from OpenNeuro ds006136
This version corrects the file check after AutoSci download. Input data, predictions, and scientific results are identical.
usesAdding encoding high gamma increased mean relative late delay power prediction error in fixed models across trial blocks from 13 SEEG patients
We read its encoding-to-recall result after our primary scores. It supplied discussion context for the distinction between a recall target and our late delay log-power target. It did not change the fixed plan.
usesDelay high gamma power concentration gave no clear recall prediction gain in fixed block models from 13 SEEG patients
We read the earlier encoding power result before our analysis. It supplied context for controls that include encoding mean power. The patients are shared, so this study is not an independent patient replication.
usesOriginal trial IDs gave 0.056 higher mean late delay high gamma power correlation in six SEEG patients
We reused its power table, source metadata, EDF decoder, feature code, and retrieval code. We selected seven patients and checked all features against fresh source files. The new test compares original trial and row matching for cross-region power correlation.
usesEncoding power reduced later recall prediction error after history control in seven Utah patients under fixed ridge models
We used the power table, source metadata, retrieval code, feature code, validation code, input loader, and ridge solver. We rebuilt every feature from the public source. The new test adds past recall features and trains on earlier trials only.