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- 2018
基于动态自适应策略的SSVEP快速目标选择方法
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Abstract:
基于稳态视觉诱发电位（SSVEP）的脑-机接口（BCI）系统具有高速、大指令集等优点，其信息传输率（ITR）的进一步提升对系统走向实际应用具有重要意义。该文采用包含35名被试的公开数据集，使用任务相关成分分析的分类模型识别脑电图数据中的SSVEP成分，进而运用基于Bayes估计的动态自适应策略评估分类结果的置信度。实验结果表明：动态自适应策略所得到的平均ITR（230 b/min）比传统的静态自适应策略（204 b/min）提升了12.7%，基于Bayes的动态自适应策略可以进一步提升SSVEP-BCI的性能。
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.
| [1] | 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. |
| [2] | 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. |
| [3] | 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. |
| [4] | WANG Y, WANG Y T, JUNG T P. Visual stimulus design for high-rate SSVEP BCI[J]. Electronics Letters, 2010, 46(15):1057-1058. |
| [5] | 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. |
| [6] | 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. |
| [7] | 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. |
| [8] | 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. |
| [9] | SCHREUDER M, H？HNE 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. |
| [10] | 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. |
| [11] | 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. |
| [12] | 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. |
| [13] | 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. |
| [14] | AKMAN AYDIN E, BAY ？ 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. |
| [15] | 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. |
| [16] | 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. |
| [17] | 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. |
| [18] | 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. |
| [19] | 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. |
| [20] | 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. |
| [21] | ABDULKADER S N, ATIA A, MOSTAFA M S M. Brain computer interfacing:Applications and challenges[J]. Egyptian Informatics Journal, 2015, 16(2):213-230. |
| [22] | 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. |
| [23] | 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. |
