High-speed target selection method for SSVEP based on a dynamic stopping strategy
WANG Chunhui1, JIANG Jing1, LI Haiyang2, XU Minpeng3,4, YIN Erwei5, MING Dong3,4
1. National Key Laboratory of Human Factors Engineering, China Astronaut Research and Training Center, Beijing 100092, China; 2. Baicheng Ordnance Test Center, Baicheng 137001, China; 3. Laboratory of Neural Engineering and Rehabilitation, College of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, China; 4. Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China; 5. Unmanned Systems Research Center, National Institute of Defense Technology Innovation, Academy of Military Sciences China, Beijing 100081, China
Abstract：The steady-state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs) are very fast and have huge instruction sets. However, the information transfer rate (ITR) needs to be increased for practical applications. A classification model of the task-relevant component analysis is used on a public data set of 35 subjects to recognize the SSVEP component in the electroencephalography data. A dynamic stopping strategy based on Bayes estimation is used to evaluate the confidence of the classification results. The results show that the dynamic stopping strategy (230 b/min) improves the average ITR by 12.7% compared with the conventional fixed stopping strategy (204 b/min). Thus, this result shows how SSVEP-BCI can be further improved by the Bayes-based dynamic stopping strategy.
王春慧, 江京, 李海洋, 许敏鹏, 印二威, 明东. 基于动态自适应策略的SSVEP快速目标选择方法[J]. 清华大学学报（自然科学版）, 2018, 58(9): 788-795.
WANG Chunhui, JIANG Jing, LI Haiyang, XU Minpeng, YIN Erwei, MING Dong. High-speed target selection method for SSVEP based on a dynamic stopping strategy. Journal of Tsinghua University(Science and Technology), 2018, 58(9): 788-795.
[1] ABDULKADER S N, ATIA A, MOSTAFA M S M. Brain computer interfacing:Applications and challenges[J]. Egyptian Informatics Journal, 2015, 16(2):213-230. [2] KVBLER A, MVLLER-PUTZ G, MATTIA D. User-centred design in brain-computer interface research and development[J]. Annals of Physical and Rehabilitation Medicine, 2015, 58(5):312-314. [3] CHEN X G, CHEN Z K, GAO S K, et al. A high-ITR SSVEP-based BCI speller[J]. Brain-Computer Interfaces, 2014, 1(3-4):181-191. [4] GAO S K, WANG Y J, GAO X R, et al. Visual and auditory brain-computer interfaces[J]. IEEE Transactions on Biomedical Engineering, 2014, 61(5):1436-1447. [5] XU M P, QI H Z, WAN B K, et al. A hybrid BCI speller paradigm combining P300 potential and the SSVEP blocking feature[J]. Journal of Neural Engineering, 2013, 10(2):026001. [6] YIN E W, ZHOU Z T, JIANG J, et al. A dynamically optimized SSVEP brain-computer interface (BCI) speller[J]. IEEE Transactions on Biomedical Engineering, 2015, 62(6):1447-1456. [7] YUAN P, GAO X R, ALLISON B, et al. A study of the existing problems of estimating the information transfer rate in online brain-computer interfaces[J]. Journal of Neural Engineering, 2013, 10(2):026014. [8] ZHANG Y S, XU P, LIU T J, et al. Multiple frequencies sequential coding for SSVEP-based brain-computer interface[J]. PLoS One, 2012, 7(3):e29519. [9] WANG Y, WANG Y T, JUNG T P. Visual stimulus design for high-rate SSVEP BCI[J]. Electronics Letters, 2010, 46(15):1057-1058. [10] CHEN X, WANG Y, NAKANISHI M, et al. High-speed spelling with a noninvasive brain-computer interface[J]. Proceedings of the National Academy of Sciences of the United States of America, 2015, 112(44):E6058-E6067. [11] CHEN X G, WANG Y J, ZHANG S G, et al. A novel stimulation method for multi-class SSVEP-BCI using intermodulation frequencies[J]. Journal of Neural Engineering, 2017, 14(2):026013. [12] YIN E W, ZEYL T, SAAB R, et al. A hybrid brain-computer interface based on the fusion of P300 and SSVEP scores[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2015, 23(4):693-701. [13] CHENG M, GAO X R, GAO S K, et al. Design and implementation of a brain-computer interface with high transfer rates[J]. IEEE Transactions on Biomedical Engineering, 2002, 49(10):1181-1186. [14] BIN G Y, GAO X R, YAN Z, et al. An online multi-channel SSVEP-based brain-computer interface using a canonical correlation analysis method[J]. Journal of Neural Engineering, 2009, 6(4):046002. [15] WANG Y J, NAKANISHI M, WANG Y T, et al. Enhancing detection of steady-state visual evoked potentials using individual training data[C]//Proceedings of the 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Chicago, USA, 2014:3037-3040. [16] NAKANISHI M, WANG Y J, CHEN X G, et al. Enhancing detection of SSVEPs for a high-speed brain speller using task-related component analysis[J]. IEEE Transactions on Biomedical Engineering, 2018, 65(1):104-112. [17] SCHREUDER M, HÖHNE J, BLANKERTZ B, et al. Optimizing event-related potential based brain-computer interfaces:A systematic evaluation of dynamic stopping methods[J]. Journal of Neural Engineering, 2013, 10(3):036025. [18] LENHARDT A, KAPER M, RITTER H J. An adaptive P300-based online brain-computer interface[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2008, 16(2):121-130. [19] LIU T, GOLDBERG L, GAO S K, et al. An online brain-computer interface using non-flashing visual evoked potentials[J]. Journal of Neural Engineering, 2010, 7(3):036003. [20] JIN J, ALLISON B Z, SELLERS E W, et al. An adaptive P300-based control system[J]. Journal of Neural Engineering, 2011, 8(3):036006. [21] MAINSAH B O, COLLINS L M, COLWELL K A, et al. Increasing BCI communication rates with dynamic stopping towards more practical use:An ALS study[J]. Journal of Neural Engineering, 2015, 12(1):016013. [22] AKMAN AYDIN E, BAY Ö F, GVLER AI·U. A dynamic stopping algorithm for P300 based brain computer interface systems[C]//Proceedings of the International Conference on Medical and Biological Engineering 2017. Singapore:Springer, 2017:723-728. [23] WANG Y J, CHEN X G, GAO X R, et al. A benchmark dataset for SSVEP-based brain-computer interfaces[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2017, 25(10):1746-1752.
2018, Vol. 58 
