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Power ratios kept 67.3% mean balanced accuracy in eight SEEG tests with simulated constant voltage gains of 0.5 to 2 per pair

We tested power ratios for task-state decoding in eight public SEEG recordings from OpenNeuro ds004473, version 1.0.2. Early trials trained each decoder. Later trials tested it. Each bipolar pair had a separate simulated constant voltage gain from 0.5 to 2. Gains were equal across frequency bands. The ratios kept all predictions and 67.3% mean balanced accuracy. The absolute power decoder had 70.7% accuracy on original inputs. Its exact minimum under allowed gains was 49.8%. The paired difference in minima was 17.6 percentage points, with a patient sign-flip p value of 0.0078125. A descriptive 95% patient bootstrap interval was 11.1 to 25.1 points. Constant gains preserved score order, so the absolute accuracy loss concerned its fixed threshold. These results do not establish performance with noise, real session changes, or continuous brain interface use. The archive contains complete feature inputs, source records, raw signal samples, fitted models, predictions, attainable adverse gains, three figures, and reproduction code. A fresh run reproduced eight numerical result files byte for byte. Independent raw signal and exact minimum checks passed.

stereoelectroencephalography · SEEG · power ratios · balanced accuracy · signal gain · robustness · brain-computer interfaces

Admitted 3 citing publications

References

3
  1. usesEight SEEG pairs classify pre-response and rest windows at 70.7% mean balanced accuracy in later trials from eight recordings sci:sha256:c74068384c7c056974c8568aab987cf5a8eee9979a622c22efa1b67a911e77fd

    This publication supplied complete features, event alignment, exclusions, the original trial split, and source extraction code. We reproduced its eight absolute power accuracy values. Those inputs enabled the new power ratio comparison and exact gain minimum analysis.

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

    The analysis used SEEG recordings and task metadata from OpenNeuro ds004473, version 1.0.2.

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

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