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Zero-calibration SEEG key-press decoding was near chance in 8 subjects, while 10 labeled within-subject trials reached median AUC 0.83

We tested whether a key-press-versus-rest SEEG decoder trained on other people could work in a new person without labeled trials. Using archived band-power features for 8 subjects from OpenNeuro ds004473 v1.0.2, we summarized high-gamma (70–150 Hz) and beta (13–30 Hz) channel z-values with their mean and seven quantiles, then trained shrinkage LDA in leave-one-subject-out tests. The prespecified primary test did not show reliable above-chance decoding: median AUC was 0.528, with 6/8 subjects above 0.5 (exact one-sided sign-flip p = 0.145). With normalization from trials 1–20, median AUC on trials 61–100 was 0.519. A within-subject decoder using all channels and 10 labeled trials reached median AUC 0.827 (8/8 subjects above 0.5; p = 0.0039); the same calibration using channel-agnostic summaries reached 0.597. These results suggest that, in this dataset, removing channel identity accounted for more performance loss than cross-subject transfer, and that these whole-implant summaries removed useful information from specific channels. The study used 8 subjects, one session, and offline windows at known event times; cue or visual activity could contribute, primary normalization used unlabeled windows from the full test set, only one channel-agnostic representation was tested, and secondary tests were not corrected for multiple comparisons.

SEEG · brain-computer interface · zero-calibration decoding · cross-subject transfer · shrinkage LDA · band power · channel-agnostic features · negative result

Admitted 2 citing publications

References

5
  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

    Uses the archived ds004473 band-power features (same SHA-256) and the numpy shrinkage LDA code.

  2. supportsIn eight SEEG subjects, 20 first trials matched 70-trial AUC, while 8-channel selection did not improve short calibration sci:sha256:6a6fdfc06ae897d478505cca0d41ec77b3dd4a71883f2ef60d6283662ff1719f

    Supports that a short labeled within-subject calibration gives high AUC on ds004473; 10 trials gave median AUC 0.83 here, and zero-calibration transfer failed.

  3. supportsCross-patient SEEG recall decoding was above chance but weaker than a three-list within-patient decoder sci:sha256:d52ba56c23a69304d837e4ef5a9f3f20bcee59ebf2cd093faea38a0334a9fa4a

    Also finds that cross-subject SEEG decoding without channel alignment is weak and not better than within-subject decoding, here for a motor task.

  4. usesdoi:10.18112/openneuro.ds004473.v1.0.2

    The archived band-power features used in this analysis are attributed to OpenNeuro ds004473 v1.0.2.

  5. doi:10.1088/1741-2552%2Facae0a

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