基于小波的模拟自然阅读事件相关电位的单次提取

Single Trial Imitating Natural Reading Event-Related Potential Based on Wavelet Transform

选择合适的信号处理方法从脑电信号中提取用户的信息和命令,是改进脑计算机接口通讯速率的方法之一.由于自发脑电信号(electroencephalograph,EEG)是非高斯有色噪声,且自发脑电信号的频谱不规则、与有效信号的频谱相重叠,传统的滤波方法难以取得较好的效果.基于白噪声与有效信号的小波变换模极大值在不同尺度下的传播行为不一样的原则,本文提出了一个基于自回归模型和小波变换多尺度分析的模拟自然阅读事件相关电位的单次提取方法.经对实际脑电信号处理的实验表明,该算法能较好的提取脑电信号.

One of factors that influences brain computer interface （BCI＇ s） with high information transfer rates is selection of appropriate signal processing methods to extract the user＇s messages and commands from electroencephalograph （EEG）. EEG is highly colored and non-gauss signal, the frequency of EEG is irregular and was the same as the event-related potential（ERP）. According to the theory that decay of wavelet local modulus maxima of signal and white noise is different, we develop a new method based on Autoregressive Model and wavelet multiresolution analysis. Results from a series of actual experiments show that, this method can extract ERP components from single trial exactly.