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Adaptive-multi-reference least means squares filter
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posted on 2014-01-01, 00:00 authored by Luke Nyhof, Imali HettiarachchiImali Hettiarachchi, S Mohammed, Saeid NahavandiAdaptive 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.
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Title of book
Neural information processing : 21st International Conference ICONIP 2014 Kuching, Malaysia, November 3-6, 2014 Proceedings, Part IIIVolume
8836Series
Lecture notes in computer scienceChapter number
64Pagination
527 - 534Publisher
Springer International PublishingPlace of publication
Berlin, GermanyPublisher DOI
ISSN
0302-9743eISSN
1611-3349ISBN-13
9783319126432Language
engPublication classification
B Book chapter; B1 Book chapterCopyright notice
2014, SpringerExtent
83Editor/Contributor(s)
C Loo, K Yap, K Wong, A Teoh, K HuangUsage metrics
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