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Review Article | Open Access

Winning algorithms in BCI Controlled Robot Contest in World Robot Contest 2022: BCI Turing Test

College of Computer Science, Hangzhou Dianzi University, Hangzhou 310018, Zhejiang, China
Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou 310018, Zhejiang, China
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Abstract

The Turing Test is a method of testing whether a machine has human intelligence. A novel brain–computer interface (BCI) Turing Test was proposed in the BCI Controlled Robot Contest in World Robot Contest 2022. Contestants developed algorithms that can distinguish if an instruction is issued by a human. Participants collaborated with an electroencephalogram-based BCI to play a soccer game in a virtual scenario. Participants were asked to perform steady-state visual evoked potential (SSVEP) tasks or motor imagery (MI) tasks to control the robots or be in an idle state to mimic the system giving instructions on behalf of the participants. Several algorithms proposed in this competition are developed based on the concept that the idle state is a category in multiclass classification problems. This paper details the algorithms of the top five teams with the best performance in the final, lists the popular classification models and algorithms for MI and SSVEP, and discusses the effectiveness of each approach in improving classification performance and reducing the computation time.

References

[1]
World Robot Contest: BCI Controlled Robot Contest. http://www.worldrobotconference.com/cn/about/138.html (Accessed February 23, 2023).
[2]
Gao XR, Wang YJ, Chen XG, et al. Interface, interaction, and intelligence in generalized brain-computer interfaces. Trends Cogn Sci 2021, 25(8): 671684.
[3]
Yu Y, Zhou ZT, Liu YD, et al. Self-paced operation of a wheelchair based on a hybrid brain-computer interface combining motor imagery and P300 potential. IEEE T Neur Sys Reh 2017, 25(12): 25162526.
[4]
Rakshit A, Konar A, Nagar AK. A hybrid brain-computer interface for closed-loop position control of a robot arm. IEEE/CAA J Autom Sin 2020, 7(5): 13441360.
[5]
Khan MJ, Zafar A, Hong KS. Hybrid EEG-NIRS based active command generation for quadcopter movement control. In 2016 International Automatic Control Conference (CACS). Taichung, Taiwan, China, 2017, pp 200205.
[6]
Nunez PL, Srinivasan R. Electric fields of the brain: the neurophysics of EEG (2nd edn). Oxford University Press, 2006.
[7]
Herman P, Prasad G, McGinnity TM, et al. Comparative analysis of spectral approaches to feature extraction for EEG-based motor imagery classification. IEEE T Neur Sys Reh 2008, 16(4): 317326.
[8]
Wang YJ, Gao SK, Gao X. Common spatial pattern method for channel selelction in motor imagery based brain-computer interface. Conf Proc IEEE Eng Med Biol Soc 2005, 2005: 53925395.
[9]
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): 53915420.
[10]
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.
[11]
Yin EW, Zhou ZT, Jiang J, et al. A dynamically optimized SSVEP brain-computer interface (BCI) speller. IEEE T Bio-med Eng 2015, 62(6): 14471456.
[12]
Chen XG, Zhao B, Wang YJ, et al. Control of a 7-DOF robotic arm system with an SSVEP-based BCI. Int J Neural Syst 2018, 28(8): 1850018.
[13]
Wong CM, Wan F, Wang BY, et al. Learning across multi-stimulus enhances target recognition methods in SSVEP-based BCIs. J Neural Eng 2020, 17(1): 016026.
[14]
Gramfort A, Luessi M, Larson E, et al. MNE software for processing MEG and EEG data. NeuroImage 2014, 86: 446460.
[15]
Virtanen P, Gommers R, Oliphant TE, et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat Methods 2020, 17(3): 261272.
[16]
Roach BJ, Mathalon DH. Event-related EEG time-frequency analysis: an overview of measures and an analysis of early gamma band phase locking in schizophrenia. Schizophr Bull 2008, 34(5): 907926.
[17]
Pfurtscheller G, Guger C, Ramoser H. EEG-based brain–computer interface using subject-specific spatial filters. In Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999, pp 248254.
[18]
Ang KK, Chin ZY, Zhang HH, et al. Filter bank common spatial pattern (FBCSP) in brain-computer interface. In 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence). Hong Kong, China, 2008, pp 23902397.
[19]
Zhang X, Wu DR. On the vulnerability of CNN classifiers in EEG-based BCIs. IEEE T Neur Sys Reh 2019, 27(5): 814825.
[20]
Akaike H. Canonical correlation analysis of time series and the use of an information criterion. In Mathematics in Science and Engineering. Mehra RK, Lainiotis DG, Eds. Amsterdam: Elsevier, 1976, pp 2796.
[21]
Lin ZL, Zhang CS, Wu W, et al. Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs. IEEE T Bio-med Eng 2007, 54(6 Pt 2): 11721176.
[22]
Chen XG, Wang YJ, Gao SK, et al. Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain-computer interface. J Neural Eng 2015, 12(4): 046008.
Brain Science Advances
Pages 182-194
Cite this article:
Yi H, Liu D, Jin X, et al. Winning algorithms in BCI Controlled Robot Contest in World Robot Contest 2022: BCI Turing Test. Brain Science Advances, 2023, 9(3): 182-194. https://doi.org/10.26599/BSA.2023.9050012

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Received: 28 February 2023
Revised: 18 April 2023
Accepted: 04 May 2023
Published: 05 September 2023
© The authors 2023.

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).

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