Comparison of recognition methods for an asynchronous (un-cued) BCI system: an investigation with 40-class SSVEP dataset

McFarland DJ, Wolpaw JR. Brain-computer interfaces for communication and control. Commun ACM. 2011;54(5):60–6.

Article  Google Scholar 

Lebedev MA, Nicolelis MA. Brain-machine interfaces: past, present and future. Trends Neurosci. 2006;29(9):536–46.

Article  Google Scholar 

Wolpaw JR, Birbaumer N, Heetderks WJ, McFarland DJ, Peckham PH, Schalk G, Donchin E, Quatrano LA, Robinson CJ, Vaughan TM, et al. Brain-computer interface technology: a review of the first international meeting. IEEE Trans Rehabil Eng. 2000;8(2):164–73.

Article  Google Scholar 

Moghimi S, Kushki A, Marie Guerguerian A, Chau T. A review of EEG-based brain-computer interfaces as access pathways for individuals with severe disabilities. Assist Technol. 2013;25(2):99–110.

Article  Google Scholar 

Gao S, Wang Y, Gao X, Hong B. Visual and auditory brain-computer interfaces. IEEE Trans Biomed Eng. 2014;61(5):1436–47.

Article  Google Scholar 

Kritzman L, Eidelman-Rothman M, Keil A, Freche D, Sheppes G, Levit-Binnun N. Steady-state visual evoked potentials differentiate between internally and externally directed attention. Neuroimage. 2022;254: 119133.

Article  Google Scholar 

Wang Y, Wang R, Gao X, Hong B, Gao S. A practical VEP-based brain-computer interface. IEEE Trans Neural Syst Rehabil Eng. 2006;14(2):234–40.

Article  Google Scholar 

Galloway N. Human brain electrophysiology: evoked potentials and evoked magnetic fields in science and medicine. Br J Ophthalmol. 1990;74(4):255.

Article  Google Scholar 

Xia B, Li X, Xie H, Yang W, Li J, He L. Asynchronous brain-computer interface based on steady-state visual-evoked potential. Cogn Comput. 2013;5:243–51.

Article  Google Scholar 

Zhang W, Sun F, Wu H, Tan C, Ma Y. Asynchronous brain-computer interface shared control of robotic grasping. Tsinghua Sci Technol. 2019;24(3):360–70.

Article  Google Scholar 

Edelman BJ, Meng J, Suma D, Zurn C, Nagarajan E, Baxter B, Cline CC, He B. Noninvasive neuroimaging enhances continuous neural tracking for robotic device control. Sci Robot. 2019;4(31):4.

Article  Google Scholar 

Moore MM. Real-world applications for brain-computer interface technology. IEEE Trans Neural Syst Rehabil Eng. 2003;11(2):162–5.

Article  Google Scholar 

Townsend G, Graimann B, Pfurtscheller G. Continuous EEG classification during motor imagery-simulation of an asynchronous BCI. IEEE Trans Neural Syst Rehabil Eng. 2004;12(2):258–65.

Article  Google Scholar 

Mangalampalli A, Pudi V. Far-hd: a fast and efficient algorithm for mining fuzzy association rules in large high-dimensional datasets, In: 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE, 2013. pp. 1–6.

Cao Z, Lin C-T. Inherent fuzzy entropy for the improvement of EEG complexity evaluation. IEEE Trans Fuzzy Syst. 2017;26(2):1032–5.

Article  Google Scholar 

Cao Z, Ding W, Wang Y-K, Hussain FK, Al-Jumaily A, Lin C-T. Effects of repetitive SSVEPS on EEG complexity using multiscale inherent fuzzy entropy. Neurocomputing. 2020;389:198–206.

Article  Google Scholar 

Rezeika A, Benda M, Stawicki P, Gembler F, Saboor A, Volosyak I. Brain-computer interface spellers: a review. Brain Sci. 2018;8(4):57.

Article  Google Scholar 

Li Y, Pan J, Wang F, Yu Z. A hybrid BCI system combining p300 and SSVEP and its application to wheelchair control. IEEE Trans Biomed Eng. 2013;60(11):3156–66.

Article  Google Scholar 

Liu Y-H, Wang S-H, Hu M-R. A self-paced p300 healthcare brain-computer interface system with SSVEP-based switching control and kernel FDA+ SVM-based detector. Appl Sci. 2016;6(5):142.

Article  Google Scholar 

Panicker RC, Puthusserypady S, Sun Y. An asynchronous p300 BCI with SSVEP-based control state detection. IEEE Trans Biomed Eng. 2011;58(6):1781–8.

Article  Google Scholar 

Suefusa K, Tanaka T. Asynchronous brain-computer interfacing based on mixed-coded visual stimuli. IEEE Trans Biomed Eng. 2017;65(9):2119–29.

Article  Google Scholar 

Abu-Alqumsan M, Peer A. Advancing the detection of steady-state visual evoked potentials in brain-computer interfaces. J Neural Eng. 2016;13(3): 036005.

Article  Google Scholar 

da Cruz JN, Wan F, Wong CM, Cao T. Adaptive time-window length based on online performance measurement in SSVEP-based BCIS. Neurocomputing. 2015;149:93–9.

Article  Google Scholar 

Xia B, Li X, Xie H, Yang W, Li J, He L. Asynchronous brain-computer interface based on steady-state visual-evoked potential. Cogn Comput. 2013;5:243–51.

Article  Google Scholar 

Zhang W, Zhou T, Zhao J, Ji B, Wu Z. Recognition of the idle state based on a novel IFB-OCN method for an asynchronous brain-computer interface. J Neurosci Methods. 2020;341: 108776.

Article  Google Scholar 

Zhang D, Huang B, Wu W, Li S. An idle-state detection algorithm for SSVEP-based brain-computer interfaces using a maximum evoked response spatial filter. Int J Neural Syst. 2015;25(07):1550030.

Article  Google Scholar 

Soni D, Malan N. S, Sharma S. CCA model with training approach to improve recognition rate of SSVEP in real time, In: Proceedings of the 2019 3rd International Conference on Artificial Intelligence and Virtual Reality, 2019. pp. 56–59.

Meriño L. M, Nayak T, Hall G, Pack D. J, Huang Y. Detection of control or idle state with a likelihood ratio test in asynchronous ssvep-based brain-computer interface systems, In: 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, 2016. pp. 1568–1571.

Du J, Ke Y, Liu P, Liu W, Kong L, Wang N, Xu M, An X, Ming D. A two-step idle-state detection method for SSVEP BCI, In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, 2019. pp. 3095–3098.

Li X, Deng Z. Research on the fractal feature extraction based SSVEP idle-state detection. Int J Comput Commun Eng. 2012;1(4):331.

Article  Google Scholar 

Zhang Z, Deng Z. A two-stage state recognition method for asynchronous SSVEP-based brain-computer interface system, Jiqiren-Robot, 35(1), 2013.

Han X, Lin K, Gao S, Gao X. A novel system of SSVEP-based human-robot coordination. J Neural Eng. 2018;16(1): 016006.

Article  Google Scholar 

Kaczmarek P, Salomon P. Towards SSVEP-based, portable, responsive brain-computer interface, In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, 2015. pp. 1095–1098.

Pan J, Li Y, Zhang R, Gu Z, Li F. Discrimination between control and idle states in asynchronous SSVEP-based brain switches: a pseudo-key-based approach. IEEE Trans Neural Syst Rehabil Eng. 2013;21(3):435–43.

Article  Google Scholar 

Ren R, Bin G, Gao X. Idle state detection in ssvep-based brain-computer interfaces, In: 2008 2nd International Conference on Bioinformatics and Biomedical Engineering, IEEE, 2008. pp. 2012–2015.

Li Y, Pan J, Long J, Yu T, Wang F, Yu Z, Wu W. Multimodal BCIS: target detection, multidimensional control, and awareness evaluation in patients with disorder of consciousness. Proc IEEE. 2015;104(2):332–52.

Google Scholar 

Zhang L, Wu X, Guo X, Liu J, Zhou B. Design and implementation of an asynchronous BCI system with alpha rhythm and SSVEP. IEEE Access. 2019;7:146123–43.

Article  Google Scholar 

Zhou Y, He S, Huang Q, Li Y. A hybrid asynchronous brain-computer interface combining SSVEP and EOG signals. IEEE Trans Biomed Eng. 2020;67(10):2881–92.

Article  Google Scholar 

Wang N, Qian T, Zhuo Q, Gao X. Discrimination between idle and work states in bci based on SSVEP, In: 2010 2nd International Conference on Advanced Computer Control, IEEE, vol. 4, 2010. pp. 355–358.

Cheng M, Gao X, Gao S, Xu D. Design and implementation of a brain-computer interface with high transfer rates. IEEE Trans Biomed Eng. 2002;49(10):1181–6.

Article  Google Scholar 

Brainard DH, Vision S. The psychophysics toolbox. Spat Vis. 1997;10(4):433–6.

Article  Google Scholar 

Chen X, Wang Y, Nakanishi M, Gao X, Jung T-P, Gao S. High-speed spelling with a noninvasive brain-computer interface. Proc Natl Acad Sci. 2015;112(44):E6058–67.

Article  Google Scholar 

Lotte F, Bougrain L, Cichocki A, Clerc M, Congedo M, Rakotomamonjy A, Yger F. A review of classification algorithms for EEG-based brain-computer interfaces: a 10 year update. J Neural Eng. 2018;15(3): 031005.

Article  Google Scholar 

Zhang D, Huang B, Wu W, Li S. An idle-state detection algorithm for SSVEP-based brain-computer interfaces using a maximum evoked response spatial filter. Int J Neural Syst. 2015;25(07):1550030.

Article  Google Scholar 

Wu T.-F, Lin C.-J, Weng R. Probability estimates for multi-class classification by pairwise coupling, Advances in Neural Information Processing Systems, vol. 16, 2003.

Chang C-C, Lin C-J. Libsvm: a library for support vector machines. ACM Trans Intell Syst Technol (TIST). 2011;2(3):1–27.

Article  Google Scholar 

Zerafa R, Camilleri T, Falzon O, Camilleri KP. To train or not to train? a survey on training of feature extraction methods for SSVEP-based BCIS. J Neural Eng. 2018;15(5): 051001.

Article  Google Scholar 

Kim H, Won K, Ahn M, Jun S. C. Cognitive-switch detection for un-cued ssvep bci speller, In: 2023 11th International Winter Conference on Brain-Computer Interface (BCI), IEEE, 2023. pp. 1–5.

Comments (0)

No login
gif