基于希尔伯特-黄变换的小鼠脑电信号相关性分析

Correlation Analysis of Mouse Electroencephalgram Based on Hilbert-Huang Transform

研究了小鼠脑电信号的分离问题。由于小鼠脑电信号是由多种不同波段的信号混迭而成的,且不同波段的信号是非平稳、能量差距很大的随机信号,因此对这些脑电信号的分离非常困难。先使用主成分分析(PCA)方法将信号的主要成分提取出来,然后再使用独立成分分析(ICA)在频域上对脑电信号进行分离。接着对分离的脑电信号δ波进行希尔伯特-黄变换。利用希尔伯特谱得到信号的瞬时频率信息,发现δ波的瞬时频率在某些时刻相对于其他时刻非常大。将这些瞬时频率出现异常的时刻,与呼吸信号的波峰出现的时刻进行相关性分析,得出呼吸信号与脑电信号中的δ波显著相关。

The separation of mouse brain electrical signals is studied.Since mouse brain electrical signals are formed by the aliasing of signals in many different wavebands,and the signals in different wavebands are nonstationary and have a large energy gap.So the separation of these electroencephalgram(EEG)is very difficult.Principal components analysis(PCA)method is used to extract the main components of the signal,and then the independent component analysis(ICA)is used to separate EEG in the frequency domain.After the separation is performed,the EEGδwave is subjected to the Hilbert-Huang transform.Using the Hilbert spectrum to obtain the instantaneous frequency information of the signal,it is found that the instantaneous frequency of the delta wave is very large at some moments relative to other moments.The moments at which these transient frequencies appear to be abnormal are correlated with the moments at which the crest of the respiratory signal appears,and it is concluded that the respiratory signal is significantly related to the delta wave in the brain electrical signal.