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We present a mobile dataset obtained from electroencephalography (EEG) of thescalp and around the ear as well as from locomotion sensors by 24 participantsmoving at four different speeds while performing two brain-computer interface(BCI) tasks. The data were collected from 32-channel scalp-EEG, 14-channelear-EEG, 4-channel electrooculography, and 9-channel inertial measurement unitsplaced at the forehead, left ankle, and right ankle. The recording conditionswere as follows: standing, slow walking, fast walking, and slight running atspeeds of 0, 0.8, 1.6, and 2.0m/s, respectively. For each speed, two differentBCI paradigms, event-related potential and steady-state visual evokedpotential, were recorded. To evaluate the signal quality, scalp- and ear-EEGdata were qualitatively and quantitatively validated during each speed. Webelieve that the dataset will facilitate BCIs in diverse mobile environments toanalyze brain activities and evaluate the performance quantitatively forexpanding the use of practical BCIs.

Mobile BCI dataset of scalp- and ear-EEGs with ERP and SSVEP paradigms while standing, walking, and running