1/2
EEG dataset for the analysis of age-related changes in motor-related cortical activity during a series of fine motor tasks performance
Version 2 2020-11-19, 13:02
Version 1 2020-05-14, 10:46
dataset
posted on 2020-05-14, 10:46 authored by Nikita FrolovNikita Frolov, Elena Pitsik, Vadim V. Grubov, Anton R. Kiselev, Vladimir MaksimenkoVladimir Maksimenko, Alexander E. HramovEEG signals were acquired from 20 healthy right-handed subjects performing a series of fine motor tasks cued by the audio command. The participants were divided equally into two distinct age groups: (i) 10 elderly adults (EA group, aged 55-72, 6 females); (ii) 10 young adults (YA group, aged 19-33, 3 females).
The active phase of the experimental session included sequential execution of 60 fine motor tasks - squeezing a hand into a fist after the first audio command and holding it until the second audio command (30 repetitions per hand) (see Fig.1). Duration of the audio command determined type of the motor action to be executed: 0.25s for left hand (LH) movement and 0.75s for right rand (RH) movement. The time interval between two audio signals was selected randomly in the range 4-5s for each trial. The sequence of motor tasks was randomized and the pause between tasks was also chosen randomly in the range 6-8s to exclude possible training or motor-preparation effects caused by the sequential execution of the same tasks.
Acquired EEG signals were then processed via preprocessing tools implemented in MNE Python package. Specifically, raw EEG signals were filtered by a Butterworth 5th order filter in the range 1-100 Hz, and by 50Hz Notch filter. Further, Independent Component Analysis (ICA) was applied to remove ocular and cardiac artifacts. Artifact-free EEG recordings were then segmented into 60 epochs according to the experimental protocol. Each epoch was 10s long, including 2s of baseline and 8s of motor-related brain activity, and centered at the first audio command indicating the start of motor execution. After visual inspection epochs that still contained artifacts were rejected. Finally, 15 epochs per movement type were stored for each subject.
Individual epochs for each subject are stored in the attached MNE .fif files. Prefix EA or YA in the name of the file identifies the age group, which subject belongs to. Postfix LH or RH in the name of the file indicates the type of motor tasks.
The active phase of the experimental session included sequential execution of 60 fine motor tasks - squeezing a hand into a fist after the first audio command and holding it until the second audio command (30 repetitions per hand) (see Fig.1). Duration of the audio command determined type of the motor action to be executed: 0.25s for left hand (LH) movement and 0.75s for right rand (RH) movement. The time interval between two audio signals was selected randomly in the range 4-5s for each trial. The sequence of motor tasks was randomized and the pause between tasks was also chosen randomly in the range 6-8s to exclude possible training or motor-preparation effects caused by the sequential execution of the same tasks.
Acquired EEG signals were then processed via preprocessing tools implemented in MNE Python package. Specifically, raw EEG signals were filtered by a Butterworth 5th order filter in the range 1-100 Hz, and by 50Hz Notch filter. Further, Independent Component Analysis (ICA) was applied to remove ocular and cardiac artifacts. Artifact-free EEG recordings were then segmented into 60 epochs according to the experimental protocol. Each epoch was 10s long, including 2s of baseline and 8s of motor-related brain activity, and centered at the first audio command indicating the start of motor execution. After visual inspection epochs that still contained artifacts were rejected. Finally, 15 epochs per movement type were stored for each subject.
Individual epochs for each subject are stored in the attached MNE .fif files. Prefix EA or YA in the name of the file identifies the age group, which subject belongs to. Postfix LH or RH in the name of the file indicates the type of motor tasks.
