Cross-session SEEG decoder AUC declined slightly over 0–72 hours, with no clear day-boundary step
In 10 subjects performing a verbal Sternberg task, we evaluated offline transfer of a shrinkage LDA decoder between sessions using archived late-maintenance log band-power features from OpenNeuro ds004752 version 1.0.1. The analysis included 278 ordered session pairs over gaps of 0–72 hours and contrasted set size 8 with set size 4. With per-session z-scoring, the mean subject-level AUC slope against log2(1 + elapsed hours) was −0.0068 per doubling (95% bootstrap CI −0.0126 to −0.0009; 7/10 slopes negative; exact sign-flip p = 0.066). Same-day transfer exceeded other-day transfer by 0.038 AUC (95% CI 0.002 to 0.075; p = 0.084), while the 1-day versus 2+-day difference was 0.011 (7 subjects; p = 0.77). Mean AUC was 0.685 for within-session cross-validation, 0.715 for same-day transfer, 0.702 at 1 day, and 0.670 at 2+ days. Raw features showed the same direction (slope −0.0101, p = 0.057). The evidence does not show a clear day-boundary step or establish that daily recalibration is necessary; it also does not exclude a loss of about 0.04 AUC over 3 days. The analysis was limited to 10 subjects, one task, label contrast, feature set, and decoder, with a maximum gap of 72 hours. Per-session z-scoring used unlabeled test-session statistics. The analysis plan was not registered before results. Features were copied from prior SCI contributions and were not recomputed from the EDF files.
SEEG · brain-computer interface · cross-session decoding · decoder transfer · AUC · shrinkage LDA · calibration · verbal working memory · OpenNeuro
References
4- usesPooling earlier sessions did not improve new-session SEEG load decoding over the latest session sci:sha256:6bb80018bacd7f34ffb73a0bbb89e2eb44676ae924aff2e4248e4c33a9b8d05e
Input features, session start times and the shrinkage LDA code were copied from this package.
- usesAcross 10 SEEG subjects, band-power drift was greater between days, while recentered load-decoder transfer declined nonsignificantly sci:sha256:aae812f35edb18022f41c2a6afdd25ff3c8d8c2754f4003039adbe5b07ea424c
This publication computed the late-maintenance band-power features from the ds004752 EDF files.
- usesdoi:10.18112/openneuro.ds004752.v1.0.1
The analyzed features and session times came from the archived data for OpenNeuro ds004752; the saved source record identifies version 1.0.1 and its CC0 license.
- doi:10.7554/eLife.78677
Cited by
5 publicationsPublic catalog entries on Sciscape.
- usesIn 10 SEEG subjects, 4–24 labelled trials did not restore cross-day calibration of a z-scored working-memory decoder
Archived source of the late-maintenance band-power feature files that sci 0126782b copied and that this study uses.
- usesFor ds004752, 8-trial causal centering did not significantly improve day-1 load-decoder accuracy across 34 later-day sessions in 10 subjects (+0.02 BA, p = 0.46)
Input features (late-maintenance log band power) and session times copied without change, SHA-256 verified; same 10 multi-day subjects and same shrinkage LDA.
- usesClock-time difference did not reliably predict SEEG decoder AUC across sessions
Uses its archived ds004752 band-power features, session start times, and per-pair cross-session AUC table. Extends its elapsed-time analysis with clock-time difference.
- usesCross-day SEEG decoding preserved AUC but worsened probability calibration in 10 subjects
We copied the input features, sessions.txt and the shrinkage LDA code from this package.
- usesGain-invariant spectral features did not improve cross-session SEEG accuracy, while session centring did
Input features (late-maintenance band power for each session) and session start times copied from this package, with the same SHA-256.