Zanotelli T, Santos Filho S A, Tierra-Criollo C J
Biomedical Engineering Studies and Research Group (NEPEB), Department of Electrical Engineering, Federal University of Minas Gerais, Brazil.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:3646-9. doi: 10.1109/IEMBS.2010.5627418.
Several techniques have been used to improve the signal-to-noise ratio to increase the detection rate of Event Related Potentials (ERPs). This work investigates the application of spatial filtering based on principal component analysis (PCA) to detect ERP due to left-hand index finger movement imagination. The EEG signals were recorded of central derivations (C4, C2, Cz, C1 and C3), positioned according to 10-10 International System. The optimal spatial filter was found by using the first principal component and the ERP detection was obtained by magnitude squared coherence technique. The best detection rate, by using original signal (without filtering), was obtained at C2 derivation, with 54.73% for significance level of 5%. For the same significance level, the detection rate of the filtered signal was drastically improved to 96.84%. Results suggest that spatial filter by using PCA might be a very useful tool in assisting the ERP detection for movement imagination for applications on brain machine interface.
已经使用了几种技术来提高信噪比，以提高事件相关电位（ERP）的检测率。这项工作研究了基于主成分分析（PCA）的空间滤波在检测因左手食指运动想象而产生的ERP中的应用。根据10-10国际系统定位，记录了中央导联（C4、C2、Cz、C1和C3）的脑电图信号。通过使用第一主成分找到了最佳空间滤波器，并通过幅度平方相干技术获得了ERP检测结果。使用原始信号（未滤波）时，在C2导联获得了最佳检测率，在5%的显著性水平下为54.73%。对于相同的显著性水平，滤波后信号的检测率大幅提高到96.84%。结果表明，使用PCA的空间滤波器可能是协助脑机接口应用中运动想象的ERP检测的非常有用的工具。
