Algorithm Contest of Calibration-free Motor Imagery BCI in the BCI Controlled Robot Contest in World Robot Contest 2021: A survey
Open accessAbstract
Objective: From September 10 to 13, 2021, the finals of the BCI Controlled Robot Contest in World Robot Contest 2021 were held in Beijing, China. Eleven teams participated in the Algorithm Contest of Calibration-free Motor Imagery BCI. The participants employed both traditional electroencephalograph (EEG) analysis methods and deep learning-based methods in the contest. In this paper, we reviewed the algorithms utilized by the participants, extracted the trends and highlighted interesting approaches from these methods to inform future contests and research recommendations. Method: First, we analyzed the algorithms in separate steps, including EEG channel and signal segment setup, prepossessing technology, and classification model. Then, we emphasized the highlights of each algorithm. Finally, we compared the competition algorithm with the SOTA algorithm. Results: The algorithm employed in the finals performed better than that of the SOTA algorithm. During the final stage of the contest, four of the top five teams used convolutional neural network models, suggesting that with the rapid development of deep learning, convolutional neural network-based models have been the most popular methods in the field of motor imagery BCI.
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EEGNet has been proven its effectiveness in BCI competitions.
“Furthermore, the EEGNet has been proven its effectiveness in BCI competitions [41],[51],[52].”
From A Cascade xDAWN EEGNet Structure for Unified Visual-Evoked Related Potential Detection (2024) · cites this paper as [51]
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A Cascade xDAWN EEGNet Structure for Unified Visual-Evoked Related Potential Detection
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- Authors
- Luo, J., Mao, Q., Wang, Y., Shi, Z., & Hei, X.
- Affiliations
- Xi'an University of Technology (CN)
- Corresponding author
- Luo, Jing
- Venue
- Brain Science Advances
- Publisher
- Tsinghua University Press
- Published
- 2022-06
- Volume
- 8
- Issue
- 2
- Pages
- 127-141
- ISSN
- 2096-5958 (ISSN-L)
- OpenAlex
- W4283734500
- Cited by
- 4
- References
- 21
- Citations by year
- 2025: 2 · 2024: 2
- FWCI
- 0.52
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Brain Science Advances (publishedVersion, cc-by-nc)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Keywords
- motor imagery, calibration-free BCI, EEG signal processing, convolutional neural networks, EEG channel selection, signal preprocessing, motor imagery classification
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