Spike sorting based on discrete wavelet transform coefficients

Using the novel mathematical technique known as wavelet analysis, a new method (WSC) is presented to sort spikes according to a decomposition of neural signals in the time-frequency space. The WSC method is implemented by a pyramidal algorithm that acts upon neural signals as a bank of quadrature mirror filters. This algorithm is clearly explained and an overview of the mathematical background of wavelet analysis is given. An artificial spike train, especially designed to test the specificity and sensibility of sorting procedures, was used to assess the performance of the WSC method as well as of methods based on principal component analysis (PCA) and reduced feature set (RFS). The WSC method outperformed the other two methods. Its superior performance was largely due to the fact that spike profiles that could not be separated by previous methods (because of the similarity of their temporal profile and the masking action of noise) were separable by the WSC method. The WSC method is particularly noise resistant, as it implicitly eliminates the irrelevant information contained in the noise frequency range. But the main advantage of the WSC method is its use of parameters that describe the joint time-frequency localization of spike features to build a fast and unspecialized pattern recognition procedure.