Abstract
A novel method for kernel design of a quadratic time frequency distribution (TFD) as the initial step for neural source estimation is proposed. The kernel is constructed based on the product ambiguity function (AF), which efficiently suppresses cross terms and noise in the ambiguity domain. In order to reduce the influence from the strong signal to the weak signal, an iterative approach is implemented. Simulation results validate the method and demonstrate suppression of cross terms and noise, and high resolution in the time frequency domain. © Springer-Verlag Berlin Heidelberg 2006.
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CITATION STYLE
Wang, P., Yang, J., Zhang, Z. L., Wang, G., & Mo, Q. (2006). A signal-dependent quadratic time frequency distribution for neural source estimation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3972 LNCS, pp. 700–705). Springer Verlag. https://doi.org/10.1007/11760023_103
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