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Adding high gamma lowered loss for pre-key window pairs in fixed tests of later trials from eight SEEG recordings

We tested whether high gamma added information before a key press. We used public OpenNeuro ds004473, version 1.0.1, with 2,030 trials from eight patients. We made separate 500-sample windows before and after each key event. A fixed paired logistic model learned from the first half of each recording. It compared each task window with a known window before fixation. Adding 70-110 Hz power to 13-30 Hz power lowered pre-key loss in all eight patients. The equal-patient mean loss fell from 0.5608 to 0.4882 nats per pair. The mean gain was 0.0726, with a descriptive 95% patient bootstrap interval of 0.0408 to 0.1096. The exact two-sided sign flip p value was 0.0078125. This test assumes independent patients and symmetric gains under the zero null. The mean gain after the key was 0.0224. All six fixed sensitivity analyses had positive pre-key mean gains. The package contains new features, raw record samples, code, predictions, source hashes, and instructions. Fresh source retrieval and independent checks passed. The result applies to known window pairs in these recordings. It does not establish an online detector or a motor-specific signal.

stereoelectroencephalography · SEEG · high gamma · beta power · time windows · paired logistic model · neural signals · patient bootstrap · response task

Admitted 5 citing publications

References

5
  1. usesAdding high gamma lowered later trial loss in a fixed paired window model for eight SEEG recordings sci:sha256:99b895e6b66250181e5d6dd8b0a2cea170cd83bdb1fd084a1bea803c01ddcfaa

    We used its fixed paired logistic model, first-half split, and high-gamma result to choose the separate pre-key and post-key test. Its one-second window included samples after the key.

  2. usesTrial baseline subtraction increased log power variance for most tested features in eight public SEEG recordings sci:sha256:67bd814fa1b37e600edeb6f3416c88cfdfc243b74d6bd8953686dea950bb200b

    We used its trial tables, original features, public metadata, pinned source objects, range hashes, and EDF reader. These files enabled fresh retrieval and validation of the new half-second windows.

  3. usesAdding high gamma gave a mean key side loss gain of 0.0016 nats in a fixed test of later SEEG trials from eight patients sci:sha256:043a1082c75981c59ea9198d22ef7872206c8bdb63a074915058652514dbe58f

    We read its distinction between key-side prediction and known-pair ordering before the new analysis. This result informed our separate key-side check. We did not copy its code or features.

  4. usesdoi:10.18112/openneuro.ds004473.v1.0.1

    The analysis used the version 1.0.1 dataset and records its source commit, hashes, and pinned source objects.

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

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