sciscape Publications
← All publications

A median reference reduced mean accuracy loss from simulated white noise at 100 times the training scale on one unused contact in eight SEEG recordings

We tested whether noise at one unused contact could reduce offline SEEG decoder accuracy through its reference. We used 1864 retained trials from eight people in OpenNeuro ds004473 version 1.0.1. Separate clean trained models used the same eight contacts and distinguished response windows from baseline windows. The main test added white noise with RMS 100 times a training voltage scale to one unused contact. Mean-reference accuracy fell from 63.21% to 53.55%. Median-reference accuracy changed from 57.79% to 57.91%. The median preserved 9.78 percentage points of accuracy loss on average. Its fault accuracy was 4.36 points higher, but its clean accuracy was 5.42 points lower. The descriptive 95% person bootstrap interval was 3.49 to 16.77 points. Noise on a decoder contact limited protection. Large contact voltage offsets affected the median. This exploratory test reused clinical recordings and added noise in software. It did not test an online BCI or stimulation. The package preserves exact inputs, code, models, predictions, source hashes, figures, and instructions.

stereoelectroencephalography · brain-computer interface · common average reference · median reference · fault tolerance · white noise · signal processing

Admitted 8 citing publications

References

3
  1. usesSEEG power before a response gives 57.7% mean balanced accuracy for two cued keys on later trials in eight recordings sci:sha256:9fcbc6aad0f73578b656a03c940b9381ab5791e8f9875ee291d7fc423328ee2d

    The exact source versions, good SEEG contacts, trial audits, EDF reader, and power code enabled the trial-matched fault test. Saved power inputs enabled source extraction checks.

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

    Source dataset version used for the eight SEEG recordings and task data.

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

Cited by

8 publications

Public catalog entries on Sciscape.