With the development of electrophysiological technology

the spike signals that electrodes record contain multi-neuron overlapped spikes. This paper presents a classification method based on a compressed sensing algorithm and a maximum a posteriori (MAP) estimate to sort the overlapped spikes. The compressed sensing algorithm is used to obtain sparse signals

and the maximum a posteriori estimate is used to search an optimal value in the sparse signals. In experiments

we use one group of simulation data and two groups of measured data to verify the method. The experimental results show that when the spike waveform shapes in the data are similar

the proposed method has fewer sorting errors compared with the existing algorithms

k

-means clustering and CBP (Continuous Basis Pursuit).