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A fixed EEG model predicts shuffled labels when training and test sets share the 88 ds004504 subjects

This study tested whether shared subjects permit high EEG test scores for shuffled labels. The data were 88 recordings from OpenNeuro ds004504, version 1.0.9. The fixed model used three nearest neighbors and 95 relative band power features. Each person supplied 20 non-overlapping epochs. Each shuffled label stayed constant across one person's epochs. Across 999 assignments, mean participant balanced accuracy was 93.4% when training and test sets shared people. It was 33.1% when the sets kept people separate. Training on early epochs and testing on late epochs from the same people gave 69.6%. The median gap between these blocks was 570 seconds in the supplied derivative. For diagnosis labels, scores were 96.2% with shared people and 52.0% with separate people. The package preserves features, source hashes, source metadata, code, predictions, and reproduction instructions. A fresh source replay matched stored features. A second analysis matched all seven output files byte for byte. The result applies to this cohort, feature rule, and fixed model. One recording per person prevents separation of person traits from session and preprocessing effects. The study measures validation behavior. It does not establish clinical diagnostic accuracy.

electroencephalography · EEG · subject leakage · data leakage · validation · label permutation · nearest neighbors · balanced accuracy · OpenNeuro · ds004504

Admitted 60 citing publications

References

6
  1. usesdoi:10.18112/openneuro.ds004504.v1.0.9

    The study used the supplied derivatives and group labels from OpenNeuro ds004504 version 1.0.9; its pinned snapshot commit and source file hashes are preserved.

  2. supportsdoi:10.3390/data8060095

    This paper describes the source dataset and preprocessing already present in the supplied derivatives.

  3. doi:10.1109/ACCESS.2023.3294618
  4. doi:10.1007/s11571-026-10464-w
  5. doi:10.3389/fnins.2024.1373515
  6. doi:10.1038/s41746-019-0178-x

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