A large EEG dataset for studying cross-session variability in motor imagery brain-computer interface.

ABSTRACT: In building a practical and robust brain-computer interface (BCI), the classification of motor imagery (MI) from electroencephalography (EEG) across multiple days is a long-standing challenge due to the large variability of the EEG signals. We collected a large dataset of MI from 5 different days with 25 subjects, the first open-access dataset to address BCI issues across 5 different days with a large number of subjects. The dataset includes 5 session data from 5 different days (2-3 days apart) for each subject. Each session contains 100 trials of left-hand and right-hand MI. In this report, we provide the benchmarking classification accuracy for three conditions, namely, within-session classification (WS), cross-session classification (CS), and cross-session adaptation (CSA), with subject

SUBMITTER: Ma J 

PROVIDER: S-EPMC9436944 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature