Abstract
A new spike sorting method based on the support vector machine (SVM) is proposed to resolve the superposition problem. The spike superposition is generally resolved by the template matching. Previous template matching methods separate the spikes through linear classifiers. The classification performance is severely influenced by the background noise included in spike trains. The nonlinear classifiers with high generation ability are required to deal with the task. A multi-class SVM classifier is therefore applied to separate the spikes, which contains several binary SVM classifiers. Every binary SVM classifier corresponding to one spike class is used to identify the single and superposition spikes. The superposition spikes are decomposed through template extraction. The experimental results on the simulated and real data demonstrate the utility of the proposed method.
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Acknowledgments
This study is supported by the National Natural Science Foundation of China (Grant No. 60574038) and the Specialized Research Fund for the Doctoral Program of Higher Education China (Grant No.20060248015).
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Ding, W., Yuan, J. Spike sorting based on multi-class support vector machine with superposition resolution. Med Bio Eng Comput 46, 139–145 (2008). https://doi.org/10.1007/s11517-007-0248-0
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DOI: https://doi.org/10.1007/s11517-007-0248-0