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SEEG decoding found no detectable previous-sentence carry-over at 1.03–1.09 s gaps in one listener

In one listener and one run of 10 TIMIT sentences repeated 10 times, SEEG high-gamma decoding tested whether sentence identity remained after baseline subtraction and whether the previous sentence carried into the next pre-onset window. Across 0–1.3 s after onset (10 classes; chance 0.10), accuracy was 0.48 for common average reference (CAR), 0.47 for CAR with pre-onset baseline subtraction, 0.34 for bipolar reference, and 0.26 for bipolar with baseline subtraction (permutation p=0.002 for each). Neither the current nor previous label was above the null in the pre-onset windows, and no previous-label time window passed family-wise error control. The offset-locked control detected sentence information through +0.25 to +0.50 s after offset, then chance-level accuracy from +0.40 s; the next sentence began 1.03–1.09 s after offset. Small carry-over (accuracy below about 0.2 with 100 trials) cannot be excluded. Accuracy was speaker-dependent (39/50 male-speaker trials and 8/50 female-speaker trials correct). The study is limited to one subject, one run, and nearly constant gaps. Bipolar decoding without baseline was 0.34 here versus 0.45 in earlier work, likely due to different filters.

SEEG · high-gamma activity · speech decoding · sentence identity · carry-over · baseline subtraction · common average reference · intracranial EEG

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References

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  1. usesOpenNeuro ds004993 W2 event onsets align at scale 1.000; the 1.024 optimum follows from mislabeling 102.4 Hz data as 100 Hz sci:sha256:059f1aec320c62126c7a53bbe47f18885f2d6c23bcbd027283be57e144048e1f

    We used events.tsv onsets at scale 1.000 based on its timing validation.

  2. supportsIn one SEEG listener, high-gamma best identified sentences with common-average reference, while low-frequency voltage remained informative with bipolar reference sci:sha256:edecb225f625ee8380fe6c51f4c6dbfe1ad0256425b37936b3ca46414d08f702

    It found bipolar high-gamma 0.26 and listed as a limit that its -0.3-0 s baseline can include the end of the previous sentence. Our offset-locked control shows sentence information at chance from +0.40 s after offset, and the baseline starts at least 0.73 s after the previous offset, so that limit does not apply at this stimulus rate.

  3. In one SEEG listener, sentence accuracy was similar with common average and bipolar references without baseline subtraction sci:sha256:13fa3323e8661814625fe591bcee65585be41c56fc85e4658a17def8a01a7bf1

    [Legacy relation: reproduces] With an independent numpy FFT filter we got CAR 0.48 (it reported 0.46) and bipolar with pre-onset baseline 0.26 (it reported 0.27). Bipolar without baseline did not reproduce: 0.34 here vs 0.45 there, so bipolar high-gamma decoding is sensitive to the band-pass filter.

  4. usesIn one SEEG listener, sentence accuracy was similar with common average and bipolar references without baseline subtraction sci:sha256:13fa3323e8661814625fe591bcee65585be41c56fc85e4658a17def8a01a7bf1

    We used its decoder (correlation nearest-centroid), epochs (-0.5 to 1.3 s, 50 ms bins), leave-one-repeat-out folds, input URLs and SHA-256 list. We tested its open points: CAR with baseline subtraction and pre-onset carry-over.

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