Aydin Serap
Electrical and Electronics Engineering Department, Engineering Faculty, Ondokuz Mayis University, Kurupelit, Samsun, Turkey.
Med Biol Eng Comput. 2009 Apr;47(4):435-40. doi: 10.1007/s11517-009-0450-3. Epub 2009 Feb 11.
In the present study, well known scale-space filtering (SSF) algorithm is used in combination with a linear mapping approach (LMA) to obtain clear auditory evoked potential (EP) waveform. The proposed combination involves two sequential steps: At first, the EEG noise level is reduced from -5 to 0 dB owing to the LMA based on the singular-value-decomposition. In the secondary process, the EEG noise remaining on the projected data is removed by using the SSF. A small number of sweeps are composed as a raw matrix to project the data without using the ensemble averaging at the beginning of the proposed method. Then, single sweeps are individually filtered in wavelet domain by using the SSF in the secondary step. The experimental results show that the SSF can extract the clear single-sweep auditory EP waveform where the LMA is used as a primary filtering. As well, the results indicate that the EP signal and background EEG noise create different wavelet coefficients due to their different characteristics. However, this characteristic difference can be considered to distinguish the EP signal and the EEG noise when the Signal-to-Noise-Ratio is higher than 0 dB.
在本研究中，著名的尺度空间滤波（SSF）算法与线性映射方法（LMA）相结合，以获得清晰的听觉诱发电位（EP）波形。所提出的组合包括两个连续步骤：首先，基于奇异值分解的LMA将脑电图噪声水平从-5 dB降低到0 dB。在第二步中，使用SSF去除投影数据上残留的脑电图噪声。在所提出方法的开始阶段，少量扫描被组成一个原始矩阵来投影数据，而不使用总体平均。然后，在第二步中通过使用SSF在小波域对单个扫描进行单独滤波。实验结果表明，当LMA用作主要滤波时，SSF可以提取清晰的单扫描听觉EP波形。同样，结果表明，由于EP信号和背景脑电图噪声的不同特性，它们会产生不同的小波系数。然而，当信噪比高于0 dB时，可以考虑利用这种特性差异来区分EP信号和脑电图噪声。
