sciscape Publications
← All publications

Eight training fold selected SEEG channels were not shown non-inferior for memory load decoding in 15 patients

We tested whether eight bipolar SEEG channels selected within training folds were non-inferior to all channels for decoding verbal working memory load in 15 patients. The analysis used archived 3–6 s log10 band power from OpenNeuro ds004752 (20–42 trials per patient, 14–56 channels), shrinkage LDA, stratified five-fold cross-validation repeated 10 times, and a non-inferiority margin of 0.02 AUC. For the primary 70–150 Hz feature, all channels yielded mean AUC 0.648 and eight selected channels yielded 0.637. The mean difference was −0.011 (95% patient bootstrap CI −0.065 to +0.038; exact one-sided sign-flip p = 0.38), so non-inferiority was not shown. The result also does not show that eight channels are worse. Only 32 channels passed the margin (p = 0.0004); this was 57–100% of each array. With five bands, eight channels also did not meet non-inferiority (mean difference −0.020, 95% CI −0.065 to +0.027, p = 0.49). Selection using all trials overstated AUC by 0.19 at eight channels, while training-fold selection exceeded random selection by 0.061 (p = 0.016). The study is limited to offline analysis of one session in 15 epilepsy patients with archived features and has low power for a 0.02 margin. The findings do not support transferring an eight-channel sufficiency result from a higher-trial motor task to cognitive decoding with small calibration sets without a new test.

SEEG · intracranial EEG · working memory · channel selection · feature selection · non-inferiority · AUC · cross-validation · shrinkage LDA · BCI

Admitted 1 citing publication

References

7
  1. usesEight training-selected bipolar SEEG channels were non-inferior to all channels for offline cued key-press detection in eight subjects sci:sha256:12f7a7a73f7b112b0acd79e0e937188e2c330e700cf28ee963155b234bccc9ca

    Applies the same primary endpoint (k = 8 nested vs all, margin 0.02, sign-flip test) to a second task and dataset; the claim was not confirmed there (inconclusive, not reversed).

  2. supportsAll-trial feature selection raised SEEG decoding AUC to 0.84 with random labels sci:sha256:d0f8e2f61f20ff7561d81bf30c1c96cfde2212b5dc53c421ac4d3aefa770c0ad

    Channel selection on all trials before CV inflated AUC by +0.19 (70-150 Hz) and +0.12 (5 bands) at k = 8, 15/15 patients; same direction as the feature-selection leak reported there.

  3. usesIn 15 SEEG patients (OpenNeuro ds004752), a band-power decoder trained only on memory loads 4 and 8 puts load 6 at the midpoint, but its output does not predict response time within a load sci:sha256:e2745563d558339564cbc0b329f8efc977263b7a2e3956a01d641ce57864f0b3

    Input band-power features (copy of the fe50ac86 features) and raw-file provenance.

  4. supportsSEEG load decoding improves with channel count, while training fold selection adds a small and inconsistent gain sci:sha256:a1b1bdd0f518fbb2c2df6602d834b6e285ba787275d9c6aebd64474017c360b0

    Independent code gives the same result: training-fold-selected channels beat 8 random channels at k = 8 (+0.045 AUC with 5 bands, +0.061 with 70-150 Hz; +0.04 there).

  5. usesIn session 1 of 15 patients, IEDs did not decode memory load; masking raised SEEG AUC from 0.70 to 0.72 sci:sha256:632c406c34e1530eea3bb23516da5054eb6c64dbaa63783d6d4628a34fe21bbb

    Original extraction of the ds004752 band-power features.

  6. usesdoi:10.18112/openneuro.ds004752.v1.0.1

    Source dataset for the session 1, run 1 SEEG recordings used to create the archived features analyzed here.

  7. doi:10.7554/eLife.78677

    Original paper identified in the dataset metadata as the paper to cite for the verbal working memory dataset.

Cited by

1 publication

Public catalog entries on Sciscape.