A method for spike sorting and detection based on wavelet packets and Shannon's mutual information.

Studying the dynamics of neural activity via electrical recording, relies on the ability to detect and sort neural spikes recorded from a number of neurons by the same electrode. We suggest the wavelet packets decomposition (WPD) as a tool to analyze neural spikes and extract their main features. The unique quality of the wavelet packets-adaptive coverage of both time and frequency domains using a set of localized packets, facilitate the task. The best basis algorithm utilizing the Shannon's information cost function and local discriminant basis (LDB) using mutual information are employed to select a few packets that are sufficient for both detection and sorting of spikes. The efficiency of the method is demonstrated on data recorded from in vitro 2D neural networks, placed on electrodes that read data from as many as five neurons. Comparison between our method and the widely used principal components method and a sorting technique based on the ordinary wavelet transform (WT) shows that our method is more efficient both in separating spikes from noise and in resolving overlapping spikes.