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Sensors 2014, 14(6), 11049-11069; doi:10.3390/s140611049

Spike Detection Based on Normalized Correlation with Automatic Template Generation

Department of Computer Science and Information Engineering, National Taiwan Normal University,Taipei 116, Taiwan
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Author to whom correspondence should be addressed.
Received: 6 May 2014 / Revised: 16 June 2014 / Accepted: 19 June 2014 / Published: 23 June 2014
(This article belongs to the Section Physical Sensors)
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Abstract

A novel feedback-based spike detection algorithm for noisy spike trains is presented in this paper. It uses the information extracted from the results of spike classification for the enhancement of spike detection. The algorithm performs template matching for spike detection by a normalized correlator. The detected spikes are then sorted by the OSortalgorithm. The mean of spikes of each cluster produced by the OSort algorithm is used as the template of the normalized correlator for subsequent detection. The automatic generation and updating of templates enhance the robustness of the spike detection to input trains with various spike waveforms and noise levels. Experimental results show that the proposed algorithm operating in conjunction with OSort is an efficient design for attaining high detection and classification accuracy for spike sorting. View Full-Text
Keywords: spike sorting; spike detection; brain machine interface spike sorting; spike detection; brain machine interface
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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Hwang, W.-J.; Wang, S.-H.; Hsu, Y.-T. Spike Detection Based on Normalized Correlation with Automatic Template Generation. Sensors 2014, 14, 11049-11069.

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