Spike sorting based on PCA and improved fuzzy c-means
Yi Yu, Yun Zhao, Han Liu, Bingchao Dong, Zhenxin Li
Available Online April 2015.
- https://doi.org/10.2991/icmra-15.2015.159How to use a DOI?
- Spike sorting; detection; clustering; PCA; improvement fuzzy c-means
- Proper classification of spikes from extracellular recordings is essential for the study of neuronal behavior. A lot of algorithms have been presented in the technical literature. Combining with subtractive clustering and fuzzy c-means, we present a new algorithm named improved fuzzy c-means. Compared with fuzzy c-means, the dependence on the initial centers of improved fuzzy c-means is reduced. Not only do the new algorithm improve the accuracy of classification, but also the results of classification are more stable. Three types of classifier were employed in this paper to assess the performance of the spike sorting algorithm. When the noise level rises gradually, the accuracy of k-means of fuzzy c-means decreased quickly.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Yi Yu AU - Yun Zhao AU - Han Liu AU - Bingchao Dong AU - Zhenxin Li PY - 2015/04 DA - 2015/04 TI - Spike sorting based on PCA and improved fuzzy c-means BT - Proceedings of the 3rd International Conference on Mechatronics, Robotics and Automation PB - Atlantis Press SP - 818 EP - 822 SN - 2352-538X UR - https://doi.org/10.2991/icmra-15.2015.159 DO - https://doi.org/10.2991/icmra-15.2015.159 ID - Yu2015/04 ER -