Optimization of wavelets for classification of movement-related cortical potentials generated by variation of force-related parameters
Optimization of wavelets for classification of movement-related cortical potentials generated by variation of force-related parameters
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DOI：
10.1016/j.jneumeth.2007.01.011
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发表时间：
2007-05-15
影响因子：
3
通讯作者：
Doncarli, Christian
中科院分区：
文献类型：
--
作者：
Farina, Dario;do Nascimento, Omar Feix;Doncarli, Christian
提出了一种新的模式识别方法，对志愿任务中力相关参数变化产生的单次运动相关皮层电位(MRCP)进行分类。特征空间由离散二进小波变换的系数构成。母小波参数化使基函数得以调整以投影信号。对母小波进行了优化，使训练集估计的分类误差最小。通过优化高斯核的宽度和正则化参数，采用支持向量机方法进行分类。两名受试者在两个目标扭矩和两个扭矩发展速率下进行单侧等长足底屈曲的脑电记录中，代表性地展示了优化程序的有效性。所提出的分类方法在四对类别上进行了测试，这些类别对应于任务的两个参数中的一个参数的变化。对任务开始前的1-S脑电活动进行分类，错误分类率(测试集)由最差小波和最接近代表分类器的50.8+/-2.9%降低到40.2+/-7.3%，最优小波和最接近代表分类器的错误分类率下降到15.8+/-3.4%。所提出的模式识别方法对于受力相关参数变化调制的MRCP的分类具有很好的应用前景。(C)2007 Elsevier B.V.保留所有权利。
The paper presents a novel pattern recognition approach for the classification of single-trial movement-related cortical potentials (MRCPs) generated by variations of force-related parameters during voluntary tasks. The feature space was built from the coefficients of a discrete dyadic wavelet transformation. Mother wavelet parameterization allowed the tuning of basis functions to project the signals. The mother wavelet was optimized to minimize the classification error estimated from the training set. Classification was performed with a support vector machine (SVM) approach with optimization of the width of a Gaussian kernel and of the regularization parameter. The efficacy of the optimization procedures was representatively shown on electroencephalographic recordings from two subjects who performed unilateral isometric plantar flexions at two target torques and two rates of torque development. The proposed classification method was tested on four pairs of classes corresponding to the change in only one of the two parameters of the task. Misclassification rate (test set) in the classification of 1-s EEG activity immediately before the onset of the tasks was reduced from 50.8 +/- 2.9% with worst wavelet and nearest representative classifier, to 40.2 +/- 7.3% with optimal wavelet and nearest representative classifier, and to 15.8 +/- 3.4% with optimal wavelet and SVM with optimization of the kernel and regularization parameter. The proposed pattern recognition method is promising for classification of MRCPs modulated by variations of force-related parameters. (C) 2007 Elsevier B.V. All rights reserved.