Key result
EEG signal quality and BCI classification performance significantly decreased as participant movement speed increased from standing to fast walking and running.
The presented dataset of scalp- and ear-EEGs with locomotion sensors during different movement speeds will facilitate the development and evaluation of practical brain-computer interfaces in mobile environments.
May limit ambulatory BCI reliability; leaves open motion-robust algorithm development with this dataset.
We present a mobile dataset obtained from electroencephalography (EEG) of the scalp and around the ear as well as from locomotion sensors by 24 participants moving at four different speeds while performing two brain-computer interface (BCI) tasks. The data were collected from 32-channel scalp-EEG, 14-channel ear-EEG, 4-channel electrooculography, and 9-channel inertial measurement units placed at the forehead, left ankle, and right ankle. The recording conditions were as follows: standing, slow walking, fast walking, and slight running at speeds of 0, 0.8, 1.6, and 2.0 m/s, respectively. For each speed, two different BCI paradigms, event-related potential and steady-state visual evoked potential, were recorded. To evaluate the signal quality, scalp- and ear-EEG data were qualitatively and quantitatively validated during each speed. We believe that the dataset will facilitate BCIs in diverse mobile environments to analyze brain activities and evaluate the performance quantitatively for expanding the use of practical BCIs.
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Lee et al. (2021) studied Healthy (n=24). Movement at different speeds (walking, running) vs. Standing (0 m/s) was evaluated on Signal quality and classification accuracy (AUC for ERP, Accuracy for SSVEP). EEG signal quality and BCI classification performance significantly decreased as participant movement speed increased from standing to fast walking and running.
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