We proposed a multi-resolution-wavelet-transform based method to extract brainstem auditory evoked potential (BAEP) from the background noise and then to identify its characteristics correctly. Firstly we discussed the mother wavelet and wavelet transform algorithm and proved that bi-orthogonal wavelet bior5.5 and stationary discrete wavelet transform (SWT) were more suitable for BAEP signals. The correlation analysis of D6 scale wavelet coefficients between single trails and the ensemble average of all trails showed that the trails with good correlation (> 0.4) had higher signal-to-noise ratio, so that we could get a clear BAEP from a few trails by an average and wavelet filter method. Finally, we used this method to select desirable trails, extracted BAEP from every 10 trails and calculated theⅠ-Ⅴinter-waves' latency. The results showed that this strategy of trail selection was efficient. This method can not only achieve better de-noising effect, but also greatly reduce the stimulation time needed as well.
The maximum length sequence (m-sequence) has been successfully used to study the linear/nonlinear components of auditory evoked potential (AEP) with rapid stimulation. However, more study is needed to evaluate the effect of the m-sequence order in terms of the noise attenuation performance. This study aimed to address this issue using response-free electroencephalogram (EEG) and EEGs with nonlinear AEPs. We examined the noise attenuation ratios to evaluate the noise variation for the calculations of superimposed averaging and cross-correlation, respectively, which constitutes the main process in the deconvolution method using the dataset of spontaneous EEGs to simulate the cases of different orders (order 5 to 12) of m-sequences. And an experiment using m-sequences of order 7 and 9 was performed in true cases with substantial linear and nonlinear AEPs. The results demonstrate that the noise attenuation ratio is well agreed with the theoretical value derived from the properties of m-sequences on the random noise condition. The comparison of waveforms for AEP components from two m-sequences showed high similarity suggesting the insensitivity of AEP to the m-sequence order. This study provides a more comprehensive solution to the selection of m-sequences which will facilitate the feasible application on the nonlinear AEP with m-sequence method.