| 引用本文: | 王洪涛,李霆,黄辉,贺跃帮,刘旭程.一种基于时–空–频联合选择与相关向量机的 运动想象脑电信号分析算法[J].控制理论与应用,2017,34(10):1403~1408.[点击复制] |
| WANG Hong-tao,LI Ting,HUANG Hui,HE Yue-bang,LIU Xu-cheng.A motor imagery analysis algorithm based on spatio-temporal-frequency joint selection and relevance vector machine[J].Control Theory and Technology,2017,34(10):1403~1408.[点击复制] |
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| 一种基于时–空–频联合选择与相关向量机的 运动想象脑电信号分析算法 |
| A motor imagery analysis algorithm based on spatio-temporal-frequency joint selection and relevance vector machine |
| 摘要点击 2231 全文点击 1295 投稿时间：2017-03-19 修订日期：2017-07-24 |
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| DOI编号 10.7641/CTA.2017.70169 |
| 2017,34(10):1403-1408 |
| 中文关键词 脑机接口 运动想象 共空域滤波 相关向量机 |
| 英文关键词 brain-computer interface motor imagery common spatial patten relevance vector machine |
| 基金项目 广东省科技发展专项资金(2017A010101034), 广东高校特色创新类项目(2016KTSCX141), 五邑大学博士启动项目, 江门市基础理论与科学研究 类科技计划项目(江科[2016]189号), 五邑大学青年基金项目(2013zk08), 国家留学基金项目([2016]5113) |
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| 中文摘要 |
| 研究表明: 不同受试者由于个体差异, 会引起在执行相同运动想象任务时, 产生与受试者关联的特定脑电信号
特征, 这是设计脑机接口系统面临的一个实际问题. 为解决这个问题, 本文提出了一种基于时–空–频联合特征的提取方
法. 首先, 对原始118导联的EEG进行空间特征分析, 从中提取出与运动想象相关脑区对应的55导联EEG信号. 进一步,
在训练集上, 通过7–折交叉验证, 训练出与受试者匹配的时间窗和频带. 其次, 利用8个共空域滤波器进行特征提取. 最
后, 将获得基于样本的运动想象特征, 采用相关向量机进行分类. 仿真结果表明: 该算法在第3届脑机接口竞赛数据
集Data IVa分类上获得5位受试者平均分类精度为94.49%, 结果优于当年第1名94.17%. 此外, 与其他3种常用的方法比
较亦具有明显优势. 本文提出的基于样本的时–空–频特征提取方法和相关向量机的结合, 该算法整体性能优越, 为基于
运动想象的脑机接口在线系统设计提供了一种新方法. |
| 英文摘要 |
| Convergent studies have reported inter-subject variability in EEG representation when subjects performed
same cognitive tasks, yielding a significant drawback for developing a practical BCI system. In order to address this
problem, we have introduced a subject-dependent specio-temporal-frequecy joint feature selection method. Specifically,
we first selected 55-channel EEG signals among the original 118-channel recordings according to the close relevance of
the signals in motor-related areas. A 7-fold cross validation approach was applied to select the optimal time-window and
frequency bands, which match individual subject based upon the training data set. Then motor imagery related features were
determined via the common spatial pattern method. The obtained subject-dependent features were feeded to a relevance
vector machine for motor imagery classification. The experiment results show that our framework demonstrated superior
performance as showing in the higher classification accuracy (94.49% in comparison with the highest classification accuracy
94.17%) in the competition III. Compared with the other three existing methods, our method also has obvious advantages.
In summary, we provided feasible framework to account for inter-subject variability, which would be a new method for the
designing of the online motor imagery brain computer interface system. |
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