Tetrode Spike Detection Method Based on Quaternion Principle Component Feature Extraction

DOI

10.2991/meic-14.2014.103How to use a DOI?

Keywords

tetrode; spike; spike detection; QPCA; K-means clustering

Abstract

Multi-unit recording of tetrode has been used in spike detections for many years, but traditional feature extraction methods of spikes sorting based on analyzing correlations of single channel data don’t consider relations of each channel data. A new feature extraction method based on quaternion principle component analysis (QPCA) algorithm used for sorting the tetrode spikes trains is presented. A quaternion vector formed of four-dimensional numbers can replace a set of tetrode spikes data. Firstly, the four channels of spikes were achieved using dual threshold detection method. We used the modulus values of the vectors grouped by 4 channels corresponding data to constitute the new features vectors of spikes through QPCA algorithm. The features vectors contain correlations of 4 channels. Our method fully fuses the information of tetrode data. Thus it has higher clustering accuracy than traditional feature extraction methods.

Copyright

© 2014, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yong Zhao
AU  - Yibo Wang
AU  - Ailing Tan
PY  - 2014/11
DA  - 2014/11
TI  - Tetrode Spike Detection Method Based on Quaternion Principle Component Feature Extraction
BT  - Proceedings of the 2014 International Conference on Mechatronics, Electronic, Industrial and Control Engineering
PB  - Atlantis Press
SP  - 456
EP  - 460
SN  - 2352-5401
UR  - https://doi.org/10.2991/meic-14.2014.103
DO  - 10.2991/meic-14.2014.103
ID  - Zhao2014/11
ER  -