Journal Home > Volume 8 , Issue 2

Recently, steady-state visual evoked potential (SSVEP) has become one of the most popular electroencephalography paradigms due to its high information transfer rate. Several approaches have been proposed to improve the performance of SSVEP. The calibration- free scenario is significant in SSVEP-based brain-computer interface systems, where the subject is the first time to use the system. The participating teams proposed several effective calibration-free algorithm frameworks in the SSVEP competition (calibration-free) of the BCI Controlled Robot Contest in World Robot Contest 2021. This paper introduces the approaches used in the algorithms of the top five teams in the final. The results of the five subjects in the final proved the effectiveness of the approaches. This paper discusses the effectiveness of each approach in improving the system performance in the calibration-free scenario and gives suggestions on how to use these approaches in a real-world system.


menu
Abstract
Full text
Outline
About this article

Overview of the winning approaches in BCI Controlled Robot Contest in World Robot Contest 2021: Calibration-free SSVEP

Show Author's information Rui BianDongrui Wu( )
School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China

Abstract

Recently, steady-state visual evoked potential (SSVEP) has become one of the most popular electroencephalography paradigms due to its high information transfer rate. Several approaches have been proposed to improve the performance of SSVEP. The calibration- free scenario is significant in SSVEP-based brain-computer interface systems, where the subject is the first time to use the system. The participating teams proposed several effective calibration-free algorithm frameworks in the SSVEP competition (calibration-free) of the BCI Controlled Robot Contest in World Robot Contest 2021. This paper introduces the approaches used in the algorithms of the top five teams in the final. The results of the five subjects in the final proved the effectiveness of the approaches. This paper discusses the effectiveness of each approach in improving the system performance in the calibration-free scenario and gives suggestions on how to use these approaches in a real-world system.

Keywords:

brain-computer interfaces, electroencephalogram, steady-state visual evoked potential, SSVEP spellers, calibration-free
Received: 03 January 2022 Revised: 20 February 2022 Accepted: 04 March 2022 Published: 29 June 2022 Issue date: June 2022
References(24)
[1]
Graimann B, Allison B, Pfurtscheller G. Brain-computer interfaces: A gentle introduction. In Brain-Computer Interfaces: Revolutionizing Human-Computer Interaction. Graimann B, Gert P, Brendan A, Eds. Berlin, Heidelberg: Springer, 2010.
DOI
[2]
Zhang X, Wu DR, Ding LY, et al. Tiny noise, big mistakes: adversarial perturbations induce errors in brain-computer interface spellers. Natl Sci Rev 2020, 8(4): nwaa233.
[3]
Farwell LA, Donchin E. Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalogr Clin Neurophysiol 1988, 70(6): 510-523.
[4]
Pfurtscheller G, Neuper C. Motor imagery and direct brain-computer communication. Proc IEEE 2001, 89(7): 1123-1134.
[5]
Zhu DH, Bieger J, Garcia Molina G, et al. A survey of stimulation methods used in SSVEP-based BCIs. Comput Intell Neurosci 2010: 702357.
[6]
Beverina F, Palmas G, Silvoni S, et al. User adaptive BCIs: SSVEP and P300 based interfaces. Psychnology J 2003, 1(4): 331-354.
[7]
Lin ZL, Zhang CS, Wu W, et al. Frequency recognition based on canonical correlation analysis for SSVEP- based BCIs. IEEE Trans Biomed Eng 2006, 53(12): 2610-2614.
[8]
Friman O, Volosyak I, Gräser A. Multiple channel detection of steady-state visual evoked potentials for brain-computer interfaces. IEEE Trans Biomed Eng 2007, 54(4): 742-750.
[9]
Zhang YS, Xu P, Cheng KW, et al. Multivariate synchronization index for frequency recognition of SSVEP-based brain-computer interface. J Neurosci Methods 2014, 221: 32-40.
[10]
Saidi P, Vosoughi A, Atia G. Detection of brain stimuli using Ramanujan periodicity transforms. J Neural Eng 2019, 16(3): 036021.
[11]
Nakanishi M, Wang YJ, Wang YT, et al. A high-speed brain speller using steady-state visual evoked potentials. Int J Neural Syst 2014, 24(6): 1450019.
[12]
Chen XG, Wang YJ, Nakanishi M, et al. High-speed spelling with a noninvasive brain-computer interface. Proc Natl Acad Sci USA 2015, 112(44): E6058-E6067.
[13]
Zhang Y, Zhou GX, Jin J, et al. L1-regularized multiway canonical correlation analysis for SSVEP-based BCI. IEEE Trans Neural Syst Rehabil Eng 2013, 21(6): 887-896.
[14]
Nakanishi M, Wang YJ, Chen XG, et al. Enhancing detection of SSVEPs for a high-speed brain speller using task-related component analysis. IEEE Trans Biomed Eng 2018, 65(1): 104-112.
[15]
Liu BC, Chen XG, Shi NL, et al. Improving the performance of individually calibrated SSVEP-BCI by task- discriminant component analysis. IEEE Trans Neural Syst Rehabil Eng 2021, 29: 1998-2007.
[16]
Mahmood M, Mzurikwao D, Kim YS, et al. Fully portable and wireless universal brain-machine interfaces enabled by flexible scalp electronics and deep learning algorithm. Nat Mach Intell 2019, 1(9): 412-422.
[17]
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.
[18]
di Russo F, Spinelli D. Electrophysiological evidence for an early attentional mechanism in visual processing in humans. Vision Res 1999, 39(18): 2975-2985.
[19]
Yan W, Du C, Wu Y, et al. SSVEP-EEG denoising via image filtering methods. IEEE Trans Neural Syst Rehabil Eng 2021, 29: 1634-1643.
[20]
Akaike H. Canonical correlation analysis of time series and the use of an information criterion. Math Sci Eng 1976, 126: 27-96.
[21]
Tanaka H, Katura T, Sato H. Task-related component analysis for functional neuroimaging and application to near-infrared spectroscopy data. Neuroimage 2013, 64: 308-327.
[22]
Chen Y, Yang C, Chen X, et al. A novel training-free recognition method for SSVEP-based BCIs using dynamic window strategy. J Neural Eng 2021, 18(3), .
[23]
Yang C, Han X, Wang YJ, et al. A dynamic window recognition algorithm for SSVEP-based brain-computer interfaces using a spatio-temporal equalizer. Int J Neural Syst 2018, 28(10): 1850028.
[24]
Wang YJ, Chen XG, Gao XR, et al. A large benchmark database toward SSVEP-BCI application. IEEE Trans Neural Syst Rehabil Eng 2020, 25(10): 1746-1752.
Publication history
Copyright
Acknowledgements
Rights and permissions

Publication history

Received: 03 January 2022
Revised: 20 February 2022
Accepted: 04 March 2022
Published: 29 June 2022
Issue date: June 2022

Copyright

© The authors 2022.

Acknowledgements

Acknowledgements

This research was supported by the National Key Research and Development Program of China (Grant No. 2021ZD0201303), the Technology Innovation Project of Hubei Province of China (Grant No. 2019AEA171), and the Hubei Province Funds for Distinguished Young Scholars (Grant No. 2020CFA050).

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).

Return