Abstract
Spike sorting plays an important role in analysing electrophysiological data and understanding neural functions. Developing spike sorting methods that are highly accurate and computationally inexpensive is always a challenge in the biomedical engineering practice. This paper proposes an automatic unsupervised spike sorting method using the landmark-based spectral clustering (LSC) method in connection with features extracted by the locality preserving projection (LPP) technique. Gap statistics is employed to evaluate the number of clusters before the LSC can be performed. Experimental results show that LPP spike features are more discriminative than those of the popular wavelet transformation (WT). Accordingly, the proposed method LPP-LSC demonstrates a significant dominance compared to the existing method that is the combination between WT feature extraction and the superparamagnetic clustering. LPP and LSC are both linear algorithms that help reduce computational burden and thus their combination can be applied into realtime spike analysis.
Original language | English |
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Title of host publication | Proceedings of the International Joint Conference on Neural Networks |
Publisher | IEEE, Institute of Electrical and Electronics Engineers |
Pages | 4042-4049 |
Number of pages | 8 |
ISBN (Electronic) | 9781479914845 |
DOIs | |
Publication status | Published - 2014 |
Externally published | Yes |
Event | IEEE International Joint Conference on Neural Networks 2014 - Beijing, China Duration: 6 Jul 2014 → 11 Jul 2014 https://ieeexplore.ieee.org/xpl/conhome/6880678/proceeding (Proceedings) |
Conference
Conference | IEEE International Joint Conference on Neural Networks 2014 |
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Abbreviated title | IJCNN 2014 |
Country/Territory | China |
City | Beijing |
Period | 6/07/14 → 11/07/14 |
Internet address |