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. 2024 Jan 25;11(1):131.
doi: 10.1038/s41597-023-02787-8.

An EEG motor imagery dataset for brain computer interface in acute stroke patients

Haijie Liu  1   2 , Penghu Wei  2   3 , Haochong Wang  4 , Xiaodong Lv  1   2 , Wei Duan  5 , Meijie Li  1   2 , Yan Zhao  1   2 , Qingmei Wang  6 , Xinyuan Chen  7 , Gaige Shi  4 , Bo Han  1   2 , Junwei Hao  8   9   10
Affiliations

An EEG motor imagery dataset for brain computer interface in acute stroke patients

Haijie Liu et al. Sci Data. .

Abstract

The brain-computer interface (BCI) is a technology that involves direct communication with parts of the brain and has evolved rapidly in recent years; it has begun to be used in clinical practice, such as for patient rehabilitation. Patient electroencephalography (EEG) datasets are critical for algorithm optimization and clinical applications of BCIs but are rare at present. We collected data from 50 acute stroke patients with wireless portable saline EEG devices during the performance of two tasks: 1) imagining right-handed movements and 2) imagining left-handed movements. The dataset consists of four types of data: 1) the motor imagery instructions, 2) raw recording data, 3) pre-processed data after removing artefacts and other manipulations, and 4) patient characteristics. This is the first open dataset to address left- and right-handed motor imagery in acute stroke patients. We believe that the dataset will be very helpful for analysing brain activation and designing decoding methods that are more applicable for acute stroke patients, which will greatly facilitate research in the field of motor imagery-BCI.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Experimental procedures. Informed consent was obtained from the individual in the figure for the publication of the images. (a–c) The process used to collect data during left- and right-hand motor imagery. (d) Horizontal and vertical EOG data were collected. (e) One trial of the MI experiment. (f) EEG event markers.
Fig. 2
Fig. 2
Real-time snapshot of the EEG signal and electrode impedance. (a) A snapshot of the raw signals recorded in 30 EEG channels and 2 EOG channels. (b) The impedances of all 32 electrodes were kept close to or below 20 kΩ.
Fig. 3
Fig. 3
Detailed structure of datasets in the BIDS format.
Fig. 4
Fig. 4
ERP, time-frequency, and average power results (on topographical maps) of three subjects.
Fig. 5
Fig. 5
Process used to validate the findings.
Fig. 6
Fig. 6
Classification accuracy in 50 subjects when using four decoding methods. (a) Subject 1–25. (b) Subject 26–50.
Fig. 7
Fig. 7
Confusion matrices of motor imagery EEG classes for the four decoding methods. (a) CSP + LDA, (b) FBCSP + SVM, (c) TSLDA + DGFMDM, and (d) TWFB + DGFMDM.
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