The MATLAB implementation of automatic feature extraction and clustering algorithm for spike sorting, as proposed in Huang, Libo, Lu Gan, and Bingo Wing-Kuen Ling. "A Unified Optimization Model of Feature Extraction and Clustering for Spike Sorting." IEEE Transactions on Neural Systems and Rehabilitation Engineering 29 (2021): 750-759. (available at https://ieeexplore.ieee.org/abstract/document/9408665/)
If you find this algorithm useful in your work, please give credit by citing the above paper. If you have any questions regarding the algorithm or implementation, please do not hesitate to write to the author with the following email address: www(dot)huanglibo(AT)gmail(dot)com
You need MATLAB software to use this program.
You can easily run the demo of the Unified-SS with (the 'current folder' path is Unified-SS),
>>> demoin which two parts of the test are included, fix k=3 and estimate class number with 4 methods.
Specifically, the fix k=3 part means the demo codes are implemented with fixed class/neurons number with 3 followed by the other spike sorting processes including spike filtering, spike detection, and the proposed unified feature extraction and clustering.
On the other hand, the estimate class number with 4 methods part has tested an automatic spike sorting framework with estimating the class/neurons number and unified feature extraction and clustering, etc.
For more details, please refer to the paper Huang, Libo, Lu Gan, and Bingo Wing-Kuen Ling. "A Unified Optimization Model of Feature Extraction and Clustering for Spike Sorting." IEEE Transactions on Neural Systems and Rehabilitation Engineering 29 (2021): 750-759. (available at https://ieeexplore.ieee.org/abstract/document/9408665/).
@article{huang2021unified,
title={A Unified Optimization Model of Feature Extraction and Clustering for Spike Sorting},
author={Huang, Libo and Gan, Lu and Ling, Bingo Wing-Kuen},
journal={IEEE Transactions on Neural Systems and Rehabilitation Engineering},
volume={29},
pages={750--759},
year={2021},
publisher={IEEE}
}
Copyright(C) 2021, Libo Huang, [email protected]
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