Shelton D, Longbotham H
Nonlinear Signal Processing Lab, University of Texas, San Antonio 78249.
Biomed Sci Instrum. 1993;29:177-82.
Various methods have been used in the analysis of visually evoked potential signals. Due to the high noise content of the ssVEP signal, the signal is usually filtered using averaging or an order statistic filter, and then analyzed for frequency content. A frequency analysis technique often employed is the short-time Fourier transform (STFT) which yields information about both the frequency content and the spatial localization simultaneously. In 1946, D. Gabor modified the STFT by using a Gaussian window instead of a Hanning, Hamming, etc. for optimal simultaneous location in time and frequency. The common problem with the windowed DFT/FFT is the ambiguity of the estimate of frequency at a point (frequency localization). This paper will introduce a frequency analysis technique utilizing the WMMR (weighted majority with minimum range) filter, which, while in the time domain, will determine the frequency and spatial properties of a ssVEP signal within one period. This technique has been shown to be robust to impulsive noise of up to 40% and also robust to independent and identically distributed (i.i.d.) noise and DC shifts, for frequency content and spatial localization analysis for sinusoidal signals. The WMMR technique will be compared to the Gabor STFT by application to steady state visual evoked potential data.
在视觉诱发电位信号分析中已经使用了多种方法。由于稳态视觉诱发电位(ssVEP)信号的噪声含量较高,通常使用平均法或顺序统计滤波器对该信号进行滤波,然后分析其频率成分。一种经常采用的频率分析技术是短时傅里叶变换(STFT),它能同时给出频率成分和空间定位的信息。1946年,D. 加博尔对STFT进行了改进,他使用高斯窗代替汉宁窗、汉明窗等,以实现时间和频率上的最优同时定位。加窗离散傅里叶变换/快速傅里叶变换(DFT/FFT)的常见问题是某一点频率估计的模糊性(频率定位)。本文将介绍一种利用加权多数最小范围(WMMR)滤波器的频率分析技术,该技术在时域中能在一个周期内确定ssVEP信号的频率和空间特性。对于正弦信号的频率成分和空间定位分析,该技术已被证明对高达40%的脉冲噪声具有鲁棒性,并且对独立同分布(i.i.d.)噪声和直流偏移也具有鲁棒性。将通过应用于稳态视觉诱发电位数据,把WMMR技术与加博尔STFT进行比较。