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Adaptive-multi-reference least means squares filter

Version 2 2024-06-04, 04:05
Version 1 2015-03-16, 15:47
chapter
posted on 2024-06-04, 04:05 authored by L Nyhof, Imali HettiarachchiImali Hettiarachchi, S Mohammed, S Nahavandi
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

Series

Lecture notes in computer science