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

PETERSON, VICTORIA; CATALINA M. GALVÁN; HUGO HERNANDEZ; MARÍA PAULA SAAVEDRA; RUBEN SPIES

Data In Brief

Elsevier

Lugar: Amsterdam; Año: 2022 vol. 42 p. 1 - 9

2352-3409

The data consist of electroencephalography (EEG) signals ac- quired by means of low-cost consumer-grade devices from 10 participants (four females, right-handed, mean age ±SD = 26.1 ±4.0 years) without any previous experience in Brain-Computer Interfaces (BCIs) usage. The BCI protocol con- sisted of two conditions, namely the kinesthetic imagina- tion of grasping movement (motor imagery, MI) of the dom- inant hand and a rest/idle condition. Five protocol runs were required to be performed by each participant in a single- day session, of about 1.5 h. The first run, called RUN0, in- volved 5 trials of real grasping movement together with the same number of trials in a rest condition. This first run was done to both better explain the protocol and to encourage the participant to focus on the sensation of executing the movement. The rest of the runs (RUN1-RUN4) were identi- cal, consisting of 20 trials for each condition presented in a random order. The electrical brain activity was registered from 15 electrodes covering the sensorimotor area, at a sampling frequency of 125 Hz. Muscle activity of the dominant hand was controlled via the electromyography (EMG) activ- ity by two electrodes placed at two antagonist muscles in- volved in the flexion/extension of the wrist. The recordings were performed in a non-shielded office, by means of low- cost consumer grade devices and free multi-platform open source software. The EMG corruption level was analyzed and EEG trials for which the EMG activity was higher than a prescribed threshold value, were discarded. During acquisi- tion, EEG data was digitally band-pass filtered between 0.5 and 45 Hz. These data provide a motor imagery vs. rest EEG dataset, relevant for BCI for motor rehabilitation applications. Since the recordings were performed by means of low-cost consumer grade devices in a non-controlled environment, this dataset provides an excellent source for exploring robust brain decoding techniques for future in-home BCI usage.