Algorithm Contest of Calibration-free Motor Imagery BCI in the BCI Controlled Robot Contest in World Robot Contest 2021: A survey

Jing Luo; Qi Mao; Yaojie Wang; Zhenghao Shi; Xinhong Hei

doi:10.26599/BSA.2022.9050011

Brain Science Advances 2022, 8(2): 127-141 https://doi.org/10.26599/BSA.2022.9050011

Research Article |

Open Access | Issue | Published: 29 June 2022

Algorithm Contest of Calibration-free Motor Imagery BCI in the BCI Controlled Robot Contest in World Robot Contest 2021: A survey

Show Author's Information Hide Author's Information Jing Luo(

), Qi Mao, Yaojie Wang, Zhenghao Shi, Xinhong Hei

Shaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710054, Shaanxi, China

Keywords:

brain-computer interface, motor imagery, convolutional neural network, World Robot Contest

Cite this article:

Luo J, Mao Q, Wang Y, et al. Algorithm Contest of Calibration-free Motor Imagery BCI in the BCI Controlled Robot Contest in World Robot Contest 2021: A survey. Brain Science Advances, 2022, 8(2): 127-141. https://doi.org/10.26599/BSA.2022.9050011

Download citation

EndNote(RIS)

BibTeX

460

Views

Downloads

Citations

Crossref

N/A

WoS

N/A

Scopus

N/A

CSCD

Abstract Full text About this article

Abstract

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.

Full text

Abstract

Full text

Outline

About this article

Algorithm Contest of Calibration-free Motor Imagery BCI in the BCI Controlled Robot Contest in World Robot Contest 2021: A survey

Show Author's information Hide Author's Information Jing Luo(

), Qi Mao, Yaojie Wang, Zhenghao Shi, Xinhong Hei

Shaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710054, Shaanxi, China

Abstract

Objective:

Method:

Results:

Keywords: brain-computer interface, motor imagery, convolutional neural network, World Robot Contest

References(28)

[1]

2021 World Robot Conference. http://www.worldrobotconference.com/en/ (accessed Jan 14, 2022).

[2]

Choi JW, Huh S, Jo S. Improving performance in motor imagery BCI-based control applications via virtually embodied feedback. Comput Biol Med 2020, 127: 104079.

DOI Google Scholar

[3]

Wolpaw JR, Birbaumer N, McFarland DJ, et al. Brain-computer interfaces for communication and control. Clin Neurophysiol 2002, 113(6): 767-791.

DOI Google Scholar

[4]

Rodríguez-Bermúdez G, García-Laencina PJ. Automatic and adaptive classification of electroencephalographic signals for brain computer interfaces. J Med Syst 2012, 36(Suppl 1): S51-S63.

DOI Google Scholar

[5]

Herman P, Prasad G, McGinnity TM, et al. Comparative analysis of spectral approaches to feature extraction for EEG-based motor imagery classification. IEEE Trans Neural Syst Rehabil Eng 2008, 16(4): 317-326.

DOI Google Scholar

[6]

Tabar YR, Halici U. A novel deep learning approach for classification of EEG motor imagery signals. J Neural Eng 2017, 14(1): 016003.

DOI Google Scholar

[7]

Luo J, Feng ZR, Zhang J, et al. Dynamic frequency feature selection based approach for classification of motor imageries. Comput Biol Med 2016, 75: 45-53.

DOI Google Scholar

[8]

Luo J, Shi WW, Lu N, et al. Improving the performance of multisubject motor imagery-based BCIs using twin cascaded softmax CNNs. J Neural Eng 2021, 18(3): 036024.

DOI Google Scholar

[1]

Ang KK, Chin ZY, Wang CC, et al. Filter bank common spatial pattern algorithm on BCI competition IV datasets 2a and 2b. Front Neurosci 2012, 6: 39.

DOI Google Scholar

[10]

Zhang Y, Wang Y, Jin J, et al. Sparse Bayesian learning for obtaining sparsity of EEG frequency bands based feature vectors in motor imagery classification. Int J Neural Syst 2017, 27(2): 1650032.

DOI Google Scholar

[11]

Olivas-Padilla BE, Chacon-Murguia MI. Classification of multiple motor imagery using deep convolutional neural networks and spatial filters. Appl Soft Comput 2019, 75: 461-472.

DOI Google Scholar

[12]

Tang ZC, Li C, Sun SQ. Single-trial EEG classification of motor imagery using deep convolutional neural networks. Optik 2017, 130: 11-18.

DOI Google Scholar

[13]

Sakhavi S, Guan CT. Convolutional neural network- based transfer learning and knowledge distillation using multi-subject data in motor imagery BCI. In 2017 8th International IEEE/EMBS Conference on Neural Engineering (NER), Shanghai, China, 2017, pp 588-591.

DOI Google Scholar

[14]

Uktveris T, Jusas V. Application of convolutional neural networks to four-class motor imagery classification problem. Inf Technol Control 2017, 46(2): 260-273.

DOI Google Scholar

[15]

Wang ZJ, Cao L, Zhang Z, et al. Short time Fourier transformation and deep neural networks for motor imagery brain computer interface recognition. Concurrency Computat Pract Exper 2018, 30(23): e4413.

DOI Google Scholar

[16]

Schirrmeister RT, Springenberg JT, Fiederer LDJ, et al. Deep learning with convolutional neural networks for EEG decoding and visualization. Hum Brain Mapp 2017, 38(11): 5391-5420.

DOI Google Scholar

[17]

Lawhern VJ, Solon AJ, Waytowich NR, et al. EEGNet: a compact convolutional neural network for EEG-based brain-computer interfaces. J Neural Eng 2018, 15(5): 056013.

DOI Google Scholar

[18]

Kwon OY, Lee MH, Guan CT, et al. Subject- independent brain-computer interfaces based on deep convolutional neural networks. IEEE Trans Neural Netw Learn Syst 2020, 31(10): 3839-3852.

DOI Google Scholar

[19]

Zhang DL, Yao LN, Chen KX, et al. A convolutional recurrent attention model for subject-independent EEG signal analysis. IEEE Signal Process Lett 2019, 26(5): 715-719.

DOI Google Scholar

[20]

Autthasan P, Chaisaen R, Sudhawiyangkul T, et al. MIN2Net: end-to-end multi-task learning for subject- independent motor imagery EEG classification. IEEE Trans Biomed Eng 2021, .

DOI Google Scholar

[21]

Blankertz B, Losch F, Krauledat M, et al. The Berlin Brain-Computer Interface: accurate performance from first-session in BCI-naive subjects. IEEE Trans Biomed Eng 2008, 55(10): 2452-2462.

DOI Google Scholar

[22]

Mane R, Chew E, Chua K, et al. FBCNet: a multi-view convolutional neural network for brain-computer interface. arXiv preprint 2021, arXiv:2104.01233.

Google Scholar

[23]

Ioffe S, Szegedy C. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In Proceedings of the 32nd International Conference on Machine Learning, Lile, France, 2015, pp 448-456.

Google Scholar

[24]

Srivastava N, Hinton G, Krizhevsky A, et al. Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 2014, 15(1): 1929-1958.

Google Scholar

[25]

Clevert D-A, Unterthiner T, Hochreiter S. Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs). arXiv preprint 2015, rXiv:1511.07289.

Google Scholar

[26]

Chollet F. Xception: deep learning with depthwise separable convolutions. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 2017, pp 1800-1807.

DOI Google Scholar

[27]

Dosovitskiy A, Beyer L, Kolesnikov A, et al. An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint 2020, arXiv:2010.11929.

Google Scholar

[28]

Dornhege G, Millán JDR, Hinterberger T, et al. Toward Brain-computer Interfacing. Vol. 63. USA: The MIT Press, 2007.

DOI

About this article

Publication history

Acknowledgements

Rights and permissions

Publication history

Received: 17 January 2022

Revised: 23 March 2022

Accepted: 11 April 2022

Published: 29 June 2022

Issue date: June 2022

Copyright

Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grant Nos. 61906152 and 62076198), and Key Research and Development Program of Shaanxi (Program Nos. 2021GY-080 and 2020GXLH-Y005)

Rights and permissions

This article is published with open access at journals.sagepub.com/home/BSA

Creative Commons Non Commercial CC BY- NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (http://www.creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/ en-us/nam/open-access-at-sage).