| Abstract: | Spikes detection and sorting play an important role in study of neural information coding. Spikes were generally obtained by threshold detection after filtered in traditional detection, which failed to suppress the random pulse interference(RPI), In this paper, a novel algorithm was provided to suppress RPI using integrated feature. The raw neural signals from the primary visual cortex in rats were detected with microelectrode array. After the feature differences between spikes and RPls were compared, the features which include waveform and non-waveform features were extracted respectively, and then the integrated feature was established based on Fisher's discrimi nant ratio to separate between spikes and RPls. The test results of simulation and experiment show that the separability capability of the integrated feature is nearly two times greater than the individual feature, the average correct recognition rate of spikes and RPls is over 93%, and the detection rate of spike is effectively improved. |
