Center for Sensory-Motor Interaction (SMI), Department of Health Science and Technology, Aalborg University, Aalborg, Denmark.
Med Biol Eng Comput. 2010 Feb;48(2):123-32. doi: 10.1007/s11517-009-0569-2. Epub 2009 Dec 30.
The aim of this study was to compare methods for feature extraction and classification of EEG signals for a brain-computer interface (BCI) driven by auditory and spatial navigation imagery. Features were extracted using autoregressive modeling and optimized discrete wavelet transform. The features were selected with exhaustive search, from the combination of features of two and three channels, and with a discriminative measure (r (2)). Moreover, Bayesian classifier and support vector machine (SVM) with Gaussian kernel were compared. The results showed that the two classifiers provided similar classification accuracy. Conversely, the exhaustive search of the optimal combination of features from two and three channels significantly improved performance with respect to using r(2) for channel selection. With features optimally extracted from three channels with optimized scaling filter in the discrete wavelet transform, the classification accuracy was on average 72.2%. Thus, the choice of features had greater impact on performance than the choice of the classifier for discrimination between the two non-motor imagery tasks investigated. The results are relevant for the choice of the translation algorithm for an on-line BCI system based on non-motor imagery.
本研究旨在比较基于听觉和空间导航想象的脑-机接口（BCI）的 EEG 信号特征提取和分类方法。使用自回归建模和优化离散小波变换提取特征。特征通过穷举搜索从两个和三个通道的特征组合中选择，并使用判别度量（r（2））进行选择。此外，还比较了贝叶斯分类器和具有高斯核的支持向量机（SVM）。结果表明，两种分类器提供了相似的分类准确性。相反，从两个和三个通道的最优特征组合进行穷举搜索，与使用 r（2）进行通道选择相比，显著提高了性能。使用优化离散小波变换中的最优缩放滤波器从三个通道最优提取特征，平均分类准确率为 72.2%。因此，对于两个非运动想象任务的区分，特征的选择比对分类器的选择对性能的影响更大。研究结果对于基于非运动想象的在线 BCI 系统的转换算法选择具有重要意义。
