Advanced signal processing on brain event-related potentials by Cong, Fengyu; Lyytinen, Heikki; Ristaniemi, Tapani

By Cong, Fengyu; Lyytinen, Heikki; Ristaniemi, Tapani

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An Introduction to the Event-Related Potential Technique Cambridge, MA: The MIT Press. , Jung, T. , Bell, A. , & Sejnowski, T. J. (1997). Blind separation of auditory event-related brain responses into independent components. Proceedings of the National Academy of Sciences of the United States of America, 94(20), 10979–10984. , Hansen, L. , & Arnfred, S. M. (2007). ERPWAVELAB a toolbox for multichannel analysis of time-frequency transformed event related potentials. Journal of Neuroscience Methods, 161(2), 361–368.

2-11), and the coefficients at levels #9, #8, #7, and #6 are used for the signal reconstruction. For the DFT filter, the number of bins is 10 times the sampling frequency, and the pass band is from 1 to 15 Hz. 8 order. 8 to design a satisfactory wavelet filter. 14 shows its mother wavelet. Specifically, when the sampling frequencies are 1000, 500, and 250 Hz, the numbers of levels for the wavelet decomposition are 10, 9, and 8, respectively, and the detail coefficients of the number of levels for the signal reconstruction are at levels #9, #8, #7, and #6; levels #8, #7, #6, and #5; and levels #7, #6, #5 and #4, respectively.

Therefore, when the detail coefficients at one level are used for signal reconstruction, the magnitude response of the filter frequency response allows examination of the frequency contents of the detail coefficients at that level. 7 shows the magnitude responses of the wavelet filter frequency responses of wavelet filters when the detail coefficients at each of some levels are used for the signal reconstruction. Obviously, the frequency contents of the detail coefficients of some levels largely overlap with each other and the degrees of overlapping of the different wavelets can be different as well.

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