基于小波-双谱分析的视觉诱发脑电特征提取

针对脑电信号非平稳性、非线性和非高斯性特点,利用小波变换和双谱分析相结合的方法提取视觉诱发脑电特征.采用Oddball实验范式,采集视觉诱发脑电数据.首先,对脑电信号进行少次相干平均以去除自发脑电;然后,选择合适的小波函数和分解层数,进行小波分解与重构,并对重构后细节系数进行白化处理;最后,利用双谱分析提取视觉诱发脑电特征.结果表明:该方法可以提取蕴涵于脑电中丰富的高阶时频信息,并且在处理脑电非线性和抑制高斯噪声方面具有较强的优越性.

Feature Extraction for Visual Evoked EEG Based on Wavelet and Bispectrum Analysis

Considering that the electroencephalogram(EEG) signal is nonlinear,nonstationary and non-Gaussian,a wavelet-bispectrum analysis method was proposed to extract the feature for visual evoked EEG.Visual evoked EEG data were acquired by Oddball experimental paradigm.First,a coherence average was used to eliminate spontaneous EEG.Then,wavelet decomposition and reconstruction were performed by selecting the appropriate wavelet function and decomposing levels,and the coefficients were whitened for the reconstructed single.Finally,the feature for visual evoked EEG was extracted by bispectrum analysis.The results have shown that this method can obtain the plentiful high-order time-frequency information in the EEG,and has strong advantage for the non-linear processing and Gaussian noise suppression of EEG.