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A causal SEEG switch detected 10.6% of cued key presses at 0.28 false detections/min in an 8-subject, 240-s-per-subject analysis

We evaluated a causal asynchronous (self-paced) SEEG switch on one 240-s continuous block from each of all 8 subjects in OpenNeuro ds004473 v1.0.2. The task used visually cued key presses; the negative class included fixation, inter-stimulus intervals, and go-cue periods. Forward-only 13–30 Hz and 70–150 Hz band power was computed over 100–112 bipolar pairs, with one frame every 50 ms, and classified by shrinkage LDA in four contiguous folds. The threshold was selected by inner cross-validation on training folds for a target of 1 false detection/min; a hit was a detection within 0.5 s of a press. The switch detected 26 of 246 presses (10.6%; 95% Wilson interval 7.3–15.0%) at a pooled 0.28 false detections/min. Per-subject hit rates ranged from 0 to 34.6% (median 7.9%); four of eight subjects were above chance, and the one-sided Wilcoxon test gave p = 0.031. Median frame AUC was 0.77 (range 0.52–0.93) and correlated with hit rate (Spearman rho 0.88, p = 0.004). Even when the threshold was swept on test data, the median hit rate at no more than 1 false detection/min was 16.5%. Thus, in this offline cued task and the tested 240-s blocks, frame-level discrimination did not yield a reliable self-paced switch at a low false-detection rate. The analysis used about 45 s of training data per fold and one feature design; per-subject estimates are imprecise because each subject contributed 25–38 presses. It does not establish performance in an uncued online task or with other feature designs.

SEEG · brain-computer interface · asynchronous detection · self-paced switch · key press · shrinkage LDA · OpenNeuro · event detection

Admitted 6 citing publications

References

5
  1. supportsAsynchronous SEEG key-press detection varied across two patients despite moderate frame AUC sci:sha256:7a4a65c51973efad61c1df2a3030065bf8c1e30d38cd37559def4f2ea5854c43

    This work extends the causal SEEG key-press switch of 6729928f from 2 to all 8 ds004473 subjects. The result agrees with it: frame AUC can be high, but at 1 or fewer false detections per minute the switch finds few presses (pooled 10.6%; sub-2 had 24% here and 50% there).

  2. usesIn two SEEG patients, a band-power LDA separated key presses from rest but also detected fixation before the go cue sci:sha256:308bb75164322d9f4b37dee63f6b867db5a609a5a09aa305e088d69f62cb4ec7

    We used the design that 7324159f recommends: the negative class includes fixation, ISI and go-cue periods, not only rest.

  3. usesIn eight SEEG subjects, 70–150 Hz decoding declined above 4 µV steps, while 13–30 Hz decoding remained stable through 16 µV sci:sha256:4798ae98a792c8b2ec8fe52fa59ed047650bafc5786cbf69b0fe1c8562b4c59a

    The EDF header and byte-range read functions in code/fetch_block.py are adapted from this package.

  4. usesRe-referencing improved high-frequency SEEG detection over the recorded reference, but no montage was best across eight subjects sci:sha256:fde123191b6f52b597384fb536814e8db9f51ec1854910522f07a26b58c04704

    code/lda_np.py (numpy shrinkage LDA) and the EDF byte-range reader pattern come from this package.

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

    This analysis used one 240-s SEEG block and event tables per subject from OpenNeuro ds004473 v1.0.2.

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