Adaptive Feature Extraction of Motor Imagery EEG with OptimalWavelet Packets and SE-Isomap
Adaptive Feature Extraction of Motor Imagery EEG with OptimalWavelet Packets and SE-Isomap
复制标题
用最优小波包和SE-Isomap自适应运动想象脑电特征提取
DOI：
10.3390/app7040390
复制
发表时间：
2017-04-01
影响因子：
2.7
通讯作者：
Yang, Jin-fu
中科院分区：
文献类型：
--
作者：
Li, Ming-ai;Zhu, Wei;Yang, Jin-fu
运动想象脑电（motorimagingEEG，MI-EEG）作为反映人主动运动意图的脑电信号，在康复治疗中越来越受到重视，而准确、快速的特征提取是成功应用的关键。提出了一种基于小波包分解和SE-isomap的自适应特征提取方法。通过平均功率谱分析对MI-EEG进行预处理，以确定更有效的时间间隔。然后将WPD应用于MI-EEG的选定片段，并自主获得具有最大均值方差差的基于主题的最佳小波包（OWP）。进一步利用OWP系数统计计算时频特征，通过SE-isomap得到非线性流形结构特征和显式非线性映射。混合特征以串行融合的方式获得，并由k-最近邻（KNN）分类器进行评估。在公开数据集上进行了大量实验，10倍交叉验证实验结果表明，与常用的线性和非线性方法相比，该方法具有较高的分类精度和计算效率.
Motor imagery EEG (MI-EEG), which reflects one's active movement intention, has attracted increasing attention in rehabilitation therapy, and accurate and fast feature extraction is the key problem to successful applications. Based on wavelet packet decomposition (WPD) and SE-isomap, an adaptive feature extraction method is proposed in this paper. The MI-EEG is preprocessed to determine a more effective time interval through average power spectrum analysis. WPD is then applied to the selected segment of MI-EEG, and the subject-based optimal wavelet packets (OWPs) with top mean variance difference are obtained autonomously. The OWP coefficients are further used to calculate the time-frequency features statistically and acquire the nonlinear manifold structure features, as well as the explicit nonlinear mapping, through SE-isomap. The hybrid features are obtained in a serial fusion way and evaluated by a k-nearest neighbor (KNN) classifier. The extensive experiments are conducted on a publicly available dataset, and the experiment results of 10-fold cross-validation show that the proposed method yields relatively higher classification accuracy and computation efficiency simultaneously compared with the commonly-used linear and nonlinear approaches.