Fake Spike Removal Based on the Reconstruction Error of the KPCA

The effective detection of neuron spikes plays an important role in the brain-computer interface (BCI). In order to improve the reliability of spikes, the method based on the reconstruction error of Kernel principal component analysis (KPCA) is used to remove the fake spikes mixed in the detected spikes. Simulation experiments with different noise levels and different fake spikes ratios are carried out to verify the performance of the reconstruction error of KPCA. The results show that the KPCA method outperforms the traditional template matching method in removing fake spikes.