EEG analysis using fast wavelet transform
Zhong Zhang, Hiroaki Kawabata, Zhiqiang Liu · 2002
The continuous wavelet transform is a new approach to the problem of time-frequency analysis of signals such as EEG and is a promising method for EEG analysis. However, it requires a convolution integral in the time domain, so the amount of computation is enormous. In this study, we propose a fast wavelet transform which is comprised of the corrected basic fast algorithm and the fast wavelet transform for high accuracy to realize high computation speed and at the same time to improve computation accuracy. The corrected basic fast algorithm is based on using mother wavelets whose frequencies are lower by 2 octaves than the Nyquist frequency in the basic fast algorithm. The fast wavelet transform for high accuracy is realized by using upsampling which uses L-Spline interpolation. The experiments demonstrate advantages of our approach and show its effectiveness for EEG analysis.