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清华大学学报(自然科学版)  2018, Vol. 58 Issue (9): 788-795    DOI: 10.16511/j.cnki.qhdxxb.2018.22.038
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基于动态自适应策略的SSVEP快速目标选择方法
王春慧1, 江京1, 李海洋2, 许敏鹏3,4, 印二威5, 明东3,4
1. 中国航天员科研训练中心 人因工程重点实验室, 北京 100092;
2. 白城兵器试验中心, 白城 137001;
3. 天津大学 精密仪器与光电子工程学院, 神经工程与康复实验室, 天津 300072;
4. 天津大学 医学工程与转化医学研究院, 天津神经工程国际联合研究中心, 天津 300072;
5. 军事科学院 国防科技创新研究院, 无人系统技术研究中心, 北京 100081
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
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摘要 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。
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王春慧
江京
李海洋
许敏鹏
印二威
明东
关键词 脑-机接口稳态视觉诱发电位脑电图动态自适应策略任务相关成分分析    
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.
Key wordsbrain-computer interface    steady-state visual evoked potential    electroencephalography    dynamic stopping strategy    task-related component analysis
收稿日期: 2018-02-14      出版日期: 2018-09-19
基金资助:国家重点研发计划(2017YFB1300305);国家自然科学基金项目(81630051,81601565,61703407);载人航天第四批预先研究基金项目(030602);天津市科技重大专项与工程(16ZXHLSY00270,17ZXRGGX00020);国防科技重点实验室基金项目(6142222030301)
引用本文:   
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.
链接本文:  
http://jst.tsinghuajournals.com/CN/10.16511/j.cnki.qhdxxb.2018.22.038  或          http://jst.tsinghuajournals.com/CN/Y2018/V58/I9/788
  图1 基于 Bayes动态自适应策略的流程图
  图2 DS策略与整合 TRCA分类模型的对比
  图3 各被试 DS策略相对 FS策略的ITR提升比例
  表1 模拟在线测试结果
  图4 DS策略和理论值的累积输出比例随时间的变化
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