Database Open Access

EEG Motor Movement/Imagery Dataset

Gerwin Schalk

Published: Sept. 9, 2009. Version: 1.0.0


When using this resource, please cite:
Schalk, G. (2009). EEG Motor Movement/Imagery Dataset (version 1.0.0). PhysioNet. RRID:SCR_007345. https://doi.org/10.13026/C28G6P

Additionally, please cite the original publication:

Schalk, G., McFarland, D.J., Hinterberger, T., Birbaumer, N., Wolpaw, J.R. BCI2000: A General-Purpose Brain-Computer Interface (BCI) System. IEEE Transactions on Biomedical Engineering 51(6):1034-1043, 2004.

Please include the standard citation for PhysioNet: (show more options)
Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220. RRID:SCR_007345.

Abstract

The EEG Motor Movement/Imagery Dataset provides 64-channel electroencephalography recordings collected with the BCI2000 system during real and imagined motor tasks. Participants completed 14 experimental runs, including two one-minute baseline recordings with eyes open and eyes closed, followed by three repetitions of four two-minute task conditions. These conditions involved either executing or imagining unilateral fist movements in response to left/right visual targets, or executing or imagining bilateral fist or foot movements in response to top/bottom visual targets. EEG signals were recorded according to the international 10-10 electrode placement system at a sampling rate of 160 Hz and are provided in EDF+ format with accompanying annotation channels. Event labels identify rest periods and task onsets using three codes: T0 for rest, T1 for left-fist or both-fists movement/imagery depending on the run type, and T2 for right-fist or both-feet movement/imagery. The dataset supports research in brain-computer interfaces, motor imagery classification, movement-related EEG dynamics, and the development of signal-processing and machine-learning methods for neural decoding.

Background

The EEG Motor Movement/Imagery Dataset was developed to support research on brain-computer interfaces, motor execution, and motor imagery using electroencephalography. The dataset contains EEG recordings collected while subjects performed or imagined specific motor actions in response to visual targets. Because the protocol includes both actual movement and imagined movement conditions, the dataset is useful for studying movement-related EEG activity and for developing algorithms that distinguish rest, motor execution, and motor imagery states.

Methods

EEG data were recorded using the BCI2000 system from 64 scalp electrodes arranged according to the international 10-10 system. Each subject completed 14 experimental runs. The first two runs were one-minute baseline recordings, one with eyes open and one with eyes closed. The remaining 12 runs consisted of three repetitions of four two-minute task conditions. In the unilateral fist tasks, a visual target appeared on either the left or right side of the screen. Depending on the task, the subject either opened and closed the corresponding fist or imagined doing so until the target disappeared, after which the subject relaxed. In the bilateral hand/foot tasks, a visual target appeared at the top or bottom of the screen. Depending on the task, the subject either opened and closed both fists for a top target or both feet for a bottom target, or imagined performing those actions, until the target disappeared, followed by relaxation.

Data Description

The data are provided in EDF+ format. Each recording contains 64 EEG signals sampled at 160 samples per second, along with an annotation channel. For compatibility with PhysioToolkit software, a corresponding PhysioNet-compatible .event annotation file was also generated for each recording. The .event files and the annotation channels in the EDF+ files contain identical annotation information.

Annotations use three event codes. T0 corresponds to rest. T1 corresponds to the onset of left-fist movement or imagery in unilateral fist runs, and to both-fists movement or imagery in bilateral hand/foot runs. T2 corresponds to the onset of right-fist movement or imagery in unilateral fist runs, and to both-feet movement or imagery in bilateral hand/foot runs. In BCI2000-format versions of the data, these labels correspond to values 0, 1, and 2 in the TargetCode state variable.

The EEG montage includes 64 electrodes from the international 10-10 system, excluding Nz, F9, F10, FT9, FT10, A1, A2, TP9, TP10, P9, and P10. Signal indices in the recordings are numbered from 0 to 63, while the associated montage figure numbers electrodes from 1 to 64.


Usage Notes

Users should interpret the event labels in the context of the specific run. The meanings of T1 and T2 differ between unilateral fist runs and bilateral hand/foot runs: in some runs they indicate left- or right-fist events, while in others they indicate both-fists or both-feet events. Analyses should therefore map event codes to task labels using the run number.

The baseline recordings provide eyes-open and eyes-closed resting EEG data and should be treated separately from the motor execution and imagery task runs. The dataset may be used for classification of motor imagery, comparison of executed versus imagined movement, analysis of rest versus task-related EEG activity, and evaluation of signal-processing methods for EEG-based brain-computer interfaces.

Care should be taken when working with channel labels and electrode indices, since the signal numbering in the records begins at 0, whereas the montage figure uses numbering from 1 to 64.


Acknowledgements

This dataset was created and contributed to PhysioNet by Gerwin Schalk and colleagues at the BCI R&D Program, Wadsworth Center, New York State Department of Health, Albany, New York. W. A. Sarnacki collected the data. Aditya Joshi compiled the dataset and prepared the documentation. D. J. McFarland was responsible for the experimental design, and J. R. Wolpaw provided project oversight. The work was supported by NIH/NIBIB grants EB006356 and EB00856.


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