In 15 SEEG patients, each power band decoded load above chance, but 4–30 Hz decoding was not shown non-inferior to 4–150 Hz at a 0.05 AUC margin
This study tested whether SEEG working-memory load decoding can use only 4–30 Hz power instead of five bands spanning 4–150 Hz. We analyzed archived band-power features for 15 patients performing a verbal Sternberg task in OpenNeuro ds004752, comparing set sizes 8 and 4. A patient-specific Ledoit–Wolf shrinkage LDA used stratified five-fold cross-validation repeated 20 times, with shared folds across feature sets. Mean AUC was 0.705 for 4–150 Hz, 0.662 for 4–30 Hz, and 0.694 for 30–150 Hz. The prespecified non-inferiority test for 4–30 Hz at a 0.05 AUC margin was not supported (p = 0.36); five of 15 patients lost more than 0.09 AUC. Each band alone decoded load above chance after Holm correction (mean AUC 0.632–0.675; corrected p ≤ 0.017), while removing any one band did not cause a significant loss. The direction of the load effect varied across patients and channels. These results show redundant load information across bands but do not establish that 4–30 Hz is sufficient. The study used one session, had 9–29 trials per class, fixed bands and a 3–6 s window, and did not recompute features from raw recordings. The 4–30 Hz features came from high-rate recordings, so performance of a real low-rate system may differ.
SEEG · working memory · band power · brain-computer interface · LDA · AUC · non-inferiority · OpenNeuro
References
4- 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
We used the archived band-power features (data/feat/sub-XX.npz) and raw-data provenance from this package, and the same decoder code (shrinkage LDA, folds, seeds). Our 4-150 Hz decoder gives the same mean AUC (0.70, 14/15 above 0.5).
- 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
This publication computed the band-power features (bipolar montage, Welch power in 5 bands, 3-6 s window) that we analyze by band subset.
- usesdoi:10.18112/openneuro.ds004752.v1.0.1
The study analyzes archived SEEG band-power features from OpenNeuro ds004752 version 1.0.1; the source publication identifies the dataset and analyzed session, run, and subjects.
- doi:10.7554/eLife.78677
The dataset metadata names this paper as the work to cite for the underlying verbal working-memory dataset.
Cited by
1 publicationPublic catalog entries on Sciscape.
- usesArchived SEEG pipelines agree on patient decodability rankings, but shared trial noise explains about 38% of patient variance
Per-patient load 8 vs 4 AUC table (column auc_ALL) is one pipeline in the cross-pipeline matrix.