• Zoran Nenadic and Joel W Burdick.
• Division of Engineering and Applied Science, Califomia Institute of Technology, Pasadena, CA 91125, USA. zoran@caltech.edu
• IEEE Trans Biomed Eng. 2005 Jan 1; 52 (1): 74-87.

AbstractThis paper combines wavelet transforms with basic detection theory to develop a new unsupervised method for robustly detecting and localizing spikes in noisy neural recordings. The method does not require the construction of templates, or the supervised setting of thresholds. We present extensive Monte Carlo simulations, based on actual extracellular recordings, to show that this technique surpasses other commonly used methods in a wide variety of recording conditions. We further demonstrate that falsely detected spikes corresponding to our method resemble actual spikes more than the false positives of other techniques such as amplitude thresholding. Moreover, the simplicity of the method allows for nearly real-time execution.

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