Adaptive-multi-reference least means squares filter Nyhof,L, Hettiarachchi,I, Mohammed,S and Nahavandi,S 2014, Adaptive-multi-reference least means squares filter. In Loo,CK, Yap,KS, Wong,KW, Teoh,A and Huang,K (ed), Neural information processing : 21st International Conference ICONIP 2014 Kuching, Malaysia, November 3-6, 2014 Proceedings, Part III, Springer International Publishing, Berlin, Germany, pp.527-534. Attached Files Name Description MIMEType Size Downloads Title Adaptive-multi-reference least means squares filter Author(s) Nyhof,L Hettiarachchi,I orcid.org/0000-0002-4220-0970 Mohammed,S orcid.org/0000-0002-8851-1635 Nahavandi,S orcid.org/0000-0002-0360-5270 Title of book Neural information processing : 21st International Conference ICONIP 2014 Kuching, Malaysia, November 3-6, 2014 Proceedings, Part III Editor(s) Loo,CK Yap,KS Wong,KW Teoh,A Huang,K Publication date 2014 Series Lecture notes in computer science ; v.8836 Chapter number 64 Total chapters 83 Start page 527 End page 534 Total pages 8 Publisher Springer International Publishing Place of Publication Berlin, Germany Keyword(s) Adaptive Multi-Reference Artefact filter Biopotential Electroencephalograph (EEG) Signal filter Science & Technology Technology Computer Science, Artificial Intelligence Computer Science, Information Systems Computer Science, Theory & Methods Computer Science EEG ARTIFACTS Summary Adaptive filters are now becoming increasingly studied for their suitability in application to complex and non-stationary signals. Many adaptive filters utilise a reference input, that is used to form an estimate of the noise in the target signal. In this paper we discuss the application of adaptive filters for high electromyography contaminated electroencephalography data. We propose the use of multiple referential inputs instead of the traditional single input. These references are formed using multiple EMG sensors during an EEG experiment, each reference input is processed and ordered through firstly determining the Pearson’s r-squared correlation coefficient, from this a weighting metric is determined and used to scale and order the reference channels according to the paradigm shown in this paper. This paper presents the use and application of the Adaptive-Multi-Reference (AMR) Least Means Square adaptive filter in the domain of electroencephalograph signal acquisition. ISBN 9783319126425 ISSN 0302-9743 1611-3349 Language eng Field of Research 080108 Neural, Evolutionary and Fuzzy Computation Socio Economic Objective 920111 Nervous System and Disorders HERDC Research category B1 Book chapter ERA Research output type B Book chapter Copyright notice ©2014, Springer Persistent URL http://hdl.handle.net/10536/DRO/DU:30070732 Document type: Book Chapter Collections: Institute for Intelligent Systems Research and Innovation (IISRI) ERA Postprints 2018 ERA Submission GTP Research Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Mon, 16 Mar 2015, 14:47:47 EST Tue, 17 Mar 2015, 04:05:27 EST Fri, 20 Mar 2015, 13:02:20 EST Fri, 20 Mar 2015, 13:19:39 EST Wed, 01 Apr 2015, 05:10:51 EST Wed, 08 Apr 2015, 14:34:40 EST Wed, 08 Apr 2015, 14:35:40 EST Tue, 19 May 2015, 14:27:31 EST Fri, 12 Jun 2015, 13:39:32 EST Thu, 22 Feb 2018, 07:52:28 EST Fri, 06 Apr 2018, 09:52:42 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 646 Abstract Views, 13 File Downloads  -  Detailed Statistics Created: Fri, 20 Mar 2015, 13:19:39 EST

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