Abstract
Neuronal spike detection is an essential pre-processing step for the analysis of extracellular brain signals in neuroscience. In resonance based signal decomposition, analyzed signal can be expressed as the sum of a ‘high-resonance’ and ‘low-resonance component’. A high-resonance component can be thought as a signal consisting of sustained oscillations and a low-resonance component can be thought as a signal consisting of non-oscillatory transients. The morphology of neuronal spikes has transient character and neuronal spikes can be thought as low-resonance component in resonance-based signal decomposition. In this study a novel algorithm for detecting extracellular spikes using resonance based signal decomposition with an adaptive amplitude threshold is proposed. The proposed algorithm is tested on synthetic data and compared with the conventional threshold selection method. The results show that proposed method outperforms traditional amplitude thresholding method