Abstract

Brain Machine Interface (BMI) is a spike sorting provide a connection between the external behavior and neural behavior of animals. Moreover, the spike sorting is significant for stability of the advanced application. To detect neuronal activity, multichannel recording is one of major methods. This paper proposed a software-hardware co-design framework with a 16- channel neural recording. Two-stage spike detection usually included a threshold method and a nonlinear energy operator (NEO). The spike clustering used the feature extraction. This multichannel spike sorting system algorithm were verified by simulations data and experiments results. The results presented a significant improvement on feature space during spike separation, due to the discrete derivative method.

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