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Spike detection approaches for noisy neuronal data: Assessment and comparison

Azami, H and Sanei, S (2014) Spike detection approaches for noisy neuronal data: Assessment and comparison NEUROCOMPUTING, 133. pp. 491-506.

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Item Type: Article
Authors :
NameEmailORCID
Azami, HUNSPECIFIEDUNSPECIFIED
Sanei, Ss.sanei@surrey.ac.ukUNSPECIFIED
Date : 10 June 2014
Identification Number : 10.1016/j.neucom.2013.12.006
Uncontrolled Keywords : Science & Technology, Technology, Computer Science, Artificial Intelligence, Computer Science, COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE, Spike detection, Fractal dimension, Smoothed nonlinear energy operator, Standard deviation, Empirical mode decomposition, Singular spectrum analysis, WAVELET, SIGNALS, FILTER
Related URLs :
Depositing User : Symplectic Elements
Date Deposited : 17 May 2017 13:12
Last Modified : 17 May 2017 15:09
URI: http://epubs.surrey.ac.uk/id/eprint/838354

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