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.
History
Volume
8836
Chapter number
64
Pagination
527-534
ISSN
0302-9743
eISSN
1611-3349
ISBN-13
9783319126432
Language
eng
Publication classification
B Book chapter, B1 Book chapter
Copyright notice
2014, Springer
Extent
83
Editor/Contributor(s)
Loo CK, Yap KS, Wong KW, Teoh A, Huang K
Publisher
Springer International Publishing
Place of publication
Berlin, Germany
Title of book
Neural information processing : 21st International Conference ICONIP 2014 Kuching, Malaysia, November 3-6, 2014 Proceedings, Part III