McFarland DJ, Wolpaw JR. Brain-computer interfaces for communication and control. Commun ACM. 2011;54(5):60–6.
Lebedev MA, Nicolelis MA. Brain-machine interfaces: past, present and future. Trends Neurosci. 2006;29(9):536–46.
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.
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.
Gao S, Wang Y, Gao X, Hong B. Visual and auditory brain-computer interfaces. IEEE Trans Biomed Eng. 2014;61(5):1436–47.
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.
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.
Galloway N. Human brain electrophysiology: evoked potentials and evoked magnetic fields in science and medicine. Br J Ophthalmol. 1990;74(4):255.
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.
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.
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.
Moore MM. Real-world applications for brain-computer interface technology. IEEE Trans Neural Syst Rehabil Eng. 2003;11(2):162–5.
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.
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.
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.
Rezeika A, Benda M, Stawicki P, Gembler F, Saboor A, Volosyak I. Brain-computer interface spellers: a review. Brain Sci. 2018;8(4):57.
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.
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.
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.
Suefusa K, Tanaka T. Asynchronous brain-computer interfacing based on mixed-coded visual stimuli. IEEE Trans Biomed Eng. 2017;65(9):2119–29.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Brainard DH, Vision S. The psychophysics toolbox. Spat Vis. 1997;10(4):433–6.
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.
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.
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.
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.
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.
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.
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