A Novel Extracellular Spike Detection Approach for Noisy Neuronal Data

Neural action potential, named spike, plays an important role in com-prehending the central nervous systems. Neuronal spike detection is a technical challenge due to the effect of strong noise and nonstationarity. There are two main problems for almost all conventional spike detection approaches. First, a filtering approach is often followed for pre-processing the data. Selection of the filter parameters is a time-consuming task. To overcome this problem we sug-gest utilizing empirical mode decomposition (EMD) and a modified adaptive filter that its parameters are tuned automatically. The second problem is that the spike detection method is signal dependent and the performance changes consi-derably when the data changes. To tackle this problem, a novel approach which utilizes the data distribution is proposed. This method exploits the fuzzy set theory to combine a number of spike detectors to achieve a higher performance. The results demonstrate the superiority of the proposed method.