Spike sorting based on wavelet feature and dynamic mixture-of-Gaussians models

To realize unsupervised spike sorting in the research of invasive brain activity, we have proposed a novel spike sorting algorithm framework based on wavelet feature and dynamic mixture-of-Gaussians clustering. After spike detection using amplitude threshold method, sym5 wavelet is employed to extract the time-frequency features representing spikes generated by different source neurons. Considering the non-stationary nature of spike train data, the wavelet time-frequency feature is divided into short time frames. Then, the dynamic clustering process proceeds in a Bayesian framework, with the source neurons modeled as Gaussian mixtures. Experimental results demonstrate that our spike sorting method achieves better robustness and reliability. Experiments on simulated spike signals show an encouraging misclassified rate below 8.44%. Furthermore, experiments on real spike signals show that the clustering results highly agree with those of human sorter.