Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
410787 | Neurocomputing | 2008 | 4 Pages |
Abstract
Spike sorting is a prerequisite for all researches on multi-channel extracellular neural signal recordings. In this paper, we develop a new method for action potential classification. We introduce a mathematical model consisting of three Gaussian waveforms, which appropriately represents the general shapes of action potentials. Then we search for the best-fit waveform for each noise-corrupted spike based on the model, using peak fitting method. These processes result in increased separability among different classes of action potentials. The performance of the proposed method is assessed with synthesized neural recordings composed by spike templates and white Gaussian noise in various SNR environments.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Seong-eun Roh, Joon Hwan Choi, Taejeong Kim,