Auditable Multiscale EEG Fusion for Offline Cognitive Workload Decoding in COG-BCI

Background/Objectives: Provenance checks and target-data access are often poorly documented in cross-participant electroencephalography (EEG) workload studies. An auditable multiscale decoder is evaluated for the 0-back versus 2-back contrast in the public COG-BCI dataset. Methods: Multiscale spectral, covariance, and bilateral features were selected from training data alone. Linear discriminant analysis and support vector machines were then combined. Identical N-back signal files were found under two nominal identifiers during a source audit. One identifier was excluded. Nominal-unit-held-out evaluation covered 23 test units, 10,682 windows, and 138 recordings. Each test unit was excluded from all supervised steps. Unlabeled target data were available for alignment. Results: Mean window balanced accuracy (BA) was 80.70% (SD, 11.23 points; median, 80.39%; range, 55.48–99.36%; descriptive 95% bootstrap interval, 76.15–85.18%). The mean unit area under the curve (AUC) was 0.8770, and pooled recording BA was 86.96%. A re-centered Riemannian minimum-distance-to-mean comparator reached 68.21%. BA fell by 16.39 points without target alignment. No reliable difference was observed for single temporal widths or linear-margin control. Conclusions: The audited pipeline supports offline, transductive decoding within one cohort. Because the cohort informed development, the estimates are internal and post hoc. Independent-cohort validation and evaluation with a fully causal processing pipeline are required.