A motor imagery vs. rest dataset with low-cost consumer grade EEG hardware

PETERSON, VICTORIA; CATALINA M. GALVÁN; HUGO S. U. HERNÁNDEZ; MARÍA PAULA SAAVEDRA; RUBEN SPIES

Data In Brief

Elsevier

Lugar: Amsterdam; Año: 2021

2352-3409

The data consist of electroencephalography (EEG) signals acquiredby means of low-cost consumer-grade devices from 10participants (four females, right-handed, mean age SD = 26.1 4.0 years) without any previous experience in Brain-ComputerInterfaces (BCIs) usage. The BCI protocol consisted of two conditions,namely the kinesthetic imagination of grasping movement(motor imagery, MI) of the dominant hand and a rest/idlecondition. Five protocol runs were required to be performed byeach participant in a single-day session, of about 1.5 hours. Thefirst run, called RUN0, involved 5 trials of real grasping movementtogether with the same number of trials in a rest condition.This first run was done to both better explain the protocol andto encourage the participant to focus on the sensation of executingthe movement. The rest of the runs (RUN1-RUN4) wereidentical, consisting of 20 trials for each condition presentedin a random order. The electrical brain activity was registeredfrom 15 electrodes covering the sensorimotor area, at a samplingfrequency of 125 Hz. Muscle activity of the dominant handwas controlled via the EMG activity by two electrodes placed attwo antagonist muscles involved in the flexion/extension of the wrist. The recordings were performed in a non-shielded oce,by means of low-cost consumer grade devices and free multiplatformopen source software. The EMG corruption level wasanalyzed and EEG trials for which the EMG activity was higherthan a prescribed threshold value, were discarded. During acquisition,EEG data was digitally band-pass filtered between0.5 and 45 Hz. These data provide a motor imagery vs. restEEG dataset, relevant for BCI for motor rehabilitation applications.Since the recordings were performed by means of lowcostconsumer grade devices in a non-controlled environment,this dataset provides an excellent source for exploring robustbrain decoding techniques for future in-home BCI usage.