Physical Sciences and Mathematics Commons^™

The Effects Of Individual Differences, Non‐Stationarity, And The Importance Of Data Partitioning Decisions For Training And Testing Of Eeg Cross‐Participant Models, Alexander J. Kamrud [*], Brett J. Borghetti, Christine M. Schubert Kabban

Faculty Publications

EEG-based deep learning models have trended toward models that are designed to perform classification on any individual (cross-participant models). However, because EEG varies across participants due to non-stationarity and individual differences, certain guidelines must be followed for partitioning data into training, validation, and testing sets, in order for cross-participant models to avoid overestimation of model accuracy. Despite this necessity, the majority of EEG-based cross-participant models have not adopted such guidelines. Furthermore, some data repositories may unwittingly contribute to the problem by providing partitioned test and non-test datasets for reasons such as competition support. In this study, we demonstrate how improper …