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。
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 can not 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.
作者
姜月
邹任玲
JIANG Yue;ZOU Renling(School of Medical Instrument and Food Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China)
出处
《中国医学物理学杂志》
CSCD
2019年第5期590-596,共7页
Chinese Journal of Medical Physics
基金
微创励志创新基金(YS30810174)
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
electroencephalogram recognition
feature fusion
principle component analysis
support vector machine
motorimagery
