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.

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Title Adaptive-multi-reference least means squares filter
Author(s) Nyhof,L
Hettiarachchi,IORCID iD for Hettiarachchi,I orcid.org/0000-0002-4220-0970
Mohammed,SORCID iD for Mohammed,S orcid.org/0000-0002-8851-1635
Nahavandi,SORCID iD for 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

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