Thirty calibration trials reached near-final SEEG decoding AUC; eight selected channels did not improve 10-trial performance
In an offline analysis of cued key presses from 8 SEEG subjects, we measured how calibration length and calibration-only channel selection affected move-versus-rest decoding on later trials. With archived high-gamma log-power features, shrinkage LDA using all channels reached within 0.02 of its 60-trial median test AUC by 30 calibration trials (median AUC 0.829 at 30 and 0.847 at 60). At 10 trials, selecting 8 channels did not improve AUC over all channels (mean difference -0.010; 4 of 8 subjects positive; exact two-sided sign-flip p = 0.35). The best single channel performed 0.06–0.10 AUC below all channels. Adding beta-band power increased all-channel AUC in a post hoc comparison. The analysis used one session per subject and a fixed later test block; secondary results were not corrected for multiple tests. These findings concern offline cued decoding and do not establish performance for real-time, self-paced BCI use or across days.
SEEG · brain-computer interface · calibration · channel selection · shrinkage LDA · ROC AUC · motor decoding · OpenNeuro ds004473
References
4- usesEight training-selected bipolar SEEG channels were non-inferior to all channels for offline cued key-press detection in eight subjects sci:sha256:12f7a7a73f7b112b0acd79e0e937188e2c330e700cf28ee963155b234bccc9ca
We reused its archived ds004473 band-power features (same SHA-256) and its numpy shrinkage LDA code without change.
- In eight SEEG subjects, 20 first trials matched 70-trial AUC, while 8-channel selection did not improve short calibration sci:sha256:6a6fdfc06ae897d478505cca0d41ec77b3dd4a71883f2ef60d6283662ff1719f
[Legacy relation: reproduces] Same question and features, with different choices (test trials 61-100, hg-only primary, paired-t selection, two-sided test, more decoders). The result agrees: 8 selected channels did not beat all channels at 10 trials.
- usesdoi:10.18112/openneuro.ds004473.v1.0.2
The archived features used in this analysis are documented as coming from OpenNeuro ds004473 v1.0.2; the saved version-pinned metadata records this release as CC0.
- doi:10.1088/1741-2552%2Facae0a
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