Review of public motor imagery and execution datasets in brain-computer interfaces

Frontiers in Human Neuroscience

Neuroscience

Minseok Song¹  Daeun Gwon¹  Minkyu Ahn²  Chang S. Nam³  Kyungho Won⁴  Sung Chan Jun⁵ 

[1] Department of Computer Science and Electrical Engineering, Handong Global University, Pohang, Republic of Korea;Department of Computer Science and Electrical Engineering, Handong Global University, Pohang, Republic of Korea;School of Computer Science and Electrical Engineering, Handong Global University, Pohang, Republic of Korea;Edward P. Fitts Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, NC, United States;Department of Industrial and Management Systems Engineering, Kyung Hee University, Yongin-si, Republic of Korea;School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea;School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea;AI Graudate School, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea;

关键词: brain-computer interface (BCI);    motor imagery;    motor execution;    public dataset;    data quality;    meta-analysis;   

DOI  :  10.3389/fnhum.2023.1134869

 received in 2022-12-31, accepted in 2023-03-10,  发布年份 2023

来源: Frontiers

【 摘 要 】

The demand for public datasets has increased as data-driven methodologies have been introduced in the field of brain-computer interfaces (BCIs). Indeed, many BCI datasets are available in various platforms or repositories on the web, and the studies that have employed these datasets appear to be increasing. Motor imagery is one of the significant control paradigms in the BCI field, and many datasets related to motor tasks are open to the public already. However, to the best of our knowledge, these studies have yet to investigate and evaluate the datasets, although data quality is essential for reliable results and the design of subject− or system-independent BCIs. In this study, we conducted a thorough investigation of motor imagery/execution EEG datasets recorded from healthy participants published over the past 13 years. The 25 datasets were collected from six repositories and subjected to a meta-analysis. In particular, we reviewed the specifications of the recording settings and experimental design, and evaluated the data quality measured by classification accuracy from standard algorithms such as Common Spatial Pattern (CSP) and Linear Discriminant Analysis (LDA) for comparison and compatibility across the datasets. As a result, we found that various stimulation types, such as text, figure, or arrow, were used to instruct subjects what to imagine and the length of each trial also differed, ranging from 2.5 to 29 s with a mean of 9.8 s. Typically, each trial consisted of multiple sections: pre-rest (2.38 s), imagination ready (1.64 s), imagination (4.26 s, ranging from 1 to 10 s), the post-rest (3.38 s). In a meta-analysis of the total of 861 sessions from all datasets, the mean classification accuracy of the two-class (left-hand vs. right-hand motor imagery) problem was 66.53%, and the population of the BCI poor performers, those who are unable to reach proficiency in using a BCI system, was 36.27% according to the estimated accuracy distribution. Further, we analyzed the CSP features and found that each dataset forms a cluster, and some datasets overlap in the feature space, indicating a greater similarity among them. Finally, we checked the minimal essential information (continuous signals, event type/latency, and channel information) that should be included in the datasets for convenient use, and found that only 71% of the datasets met those criteria. Our attempts to evaluate and compare the public datasets are timely, and these results will contribute to understanding the dataset’s quality and recording settings as well as the use of using public datasets for future work on BCIs.

【 授权许可】

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Copyright © 2023 Gwon, Won, Song, Nam, Jun and Ahn.