DORA cross-database EEG workload portability: supporting data, code and statistical methods
Abstract
Supporting materials for “Target-independent verification of electroencephalography workload decoders for adaptive human–machine systems.” This dataset contains analysis code, software environment specifications, a public pre-target analytical specification, complete model-selection traces, non-identifying UNIVERSE prediction scores, participant-level AUC summaries, aggregate statistical results, diagnostics, generated figures, and machine-readable tables for a seven-database EEG workload portability audit. Four controlled-workload databases (RITHM, EEGMAT, COG-PBCI and STEW) supplied development evidence; UNIVERSE supplied untouched external confirmation; MultiPhysio-HRC and SenseCobot supplied joint environment-and-reported-effort stress tests. Third-party raw EEG archives and participant-level derived feature matrices are excluded. Source accessions and licenses are listed in data_sources.tsv. Original package materials are licensed under CC BY 4.0; analysis code is additionally licensed under the MIT License. Version 2 aligns the record and documentation with the current Engineering Applications of Artificial Intelligence submission. Scientific data, analysis code, results, predictions, figures and statistical methods are unchanged from Version 1. The canonical prediction file SHA-256 remains d3be68bf4efa9bd7ef5d72e6bd8604cbbb0dc1ba42cbf935fb34f6dae3df827c.
