[1]姜月,邹任玲. 基于多特征融合的运动想象脑电信号识别研究[J].中国医学物理学杂志,2019,36(5):590-596.[doi:DOI:10.3969/j.issn.1005-202X.2019.05.019]
　JIANG Yue,ZOU Renling. Recognition of motor imagery EEG signals based on multi-feature fusion[J].Chinese Journal of Medical Physics,2019,36(5):590-596.[doi:DOI:10.3969/j.issn.1005-202X.2019.05.019]
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 基于多特征融合的运动想象脑电信号识别研究()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
36卷
期数:
2019年第5期
页码:
590-596
栏目:
医学信号处理与医学仪器
出版日期:
2019-05-25

文章信息/Info

Title:
 Recognition of motor imagery EEG signals based on multi-feature fusion
文章编号:
1005-202X（2019）05-0590-07
作者:
 姜月; 邹任玲
 上海理工大学医疗器械与食品学院， 上海 200093
Author(s):
 JIANG Yue;  ZOU Renling
 School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
关键词:
 ; 脑电识别; 特征融合; 主成分分析; 支持向量机; 运动想象
Keywords:
 ; Keywords: electroencephalogram recognition;  feature fusion;  principle component analysis;  support vector machine;  motor imagery
分类号:
R318
DOI:
DOI:10.3969/j.issn.1005-202X.2019.05.019
文献标志码:
A
摘要:
 目的：脑机接口通过识别脑电信号后对外部设备进行控制，针对传统的提取单一特征方法无法多角度表征脑电，提出一种多特征融合的特征提取方法。方法：分别使用自回归模型、经验模态分解、共空间模式提取结合时-频-空域的初始特征向量，用主成分分析降维，最后用支持向量机分类。结果：对BCI2003数据处理后，得到91.9%的识别率，高于单一特征和两两组合特征下的识别率以及BP神经网络、概率神经网络的识别率。结论：多特征融合的特征提取方法更好地代表了脑电特征，同时采用支持向量机分类可取得较好的效果，证明本研究方法的有效性，可进一步用于脑机接口中。
Abstract:
 Abstract: Objective The external devices are controlled with brain computer interface after the detection of electroencephalogram (EEG) signals. A feature extraction method based on multi-feature fusion is proposed to solve the problem that the traditional method of single feature extraction cannot realize the multi-angle characterization of EEG. Methods The initial eigenvectors of time-frequency-space domain were extracted by autoregressive model, empirical mode decomposition and common spatial pattern, separately. Subsequently, principal component analysis was used to reduce the dimension. Finally, support vector machine is used to classify the motor imagery EEG signals. Results After the data processing of BCI2003, the recognition rate reached 91.9%, higher than that obtained by the extraction based on single feature and the combination of any two features, and that obtained with BP neural network and probabilistic neural network. Conclusion Feature extraction method based on multi-feature fusion can characterize EEG better, and the combination with support vector machine can achieve better classification results, which proves the effectiveness of the combined use of multi-feature fusion and support vector machine. The proposed method can be further applied in brain computer interface.

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备注/Memo

备注/Memo:
【收稿日期】2018-12-11
【基金项目】微创励志创新基金（YS30810174）
【作者简介】姜月，硕士研究生，主要研究方向：脑机接口、康复医疗器械，E-mail: jiangyue925h@163.com
【通信作者】邹任玲，博士，副教授，主要研究方向：康复医疗仪器、医疗器械检测，E-mail: zourenling@163.com
更新日期/Last Update: 2019-05-23