Skip to main content
Springer Nature Link
Log in

Adaptive-Multi-Reference Least Means Squares Filter

  • Conference paper
Neural Information Processing (ICONIP 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8836))

Included in the following conference series:

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Goncharova, I., McFarland, D., Vaughan, T., Wolpaw, J.: Emg contamination of eeg: spectral and topographical characteristics. Clinical Neurophysiology 114(9), 1580–1593 (2003)

    Article  Google Scholar 

  2. Luca, C.J.D., Gilmore, L.D., Kuznetsov, M., Roy, S.H.: Filtering the surface emg signal: Movement artifact and baseline noise contamination. Journal of Biomechanics 43(8), 1573–1579 (2010)

    Article  Google Scholar 

  3. Jung, T., Makeig, S., Humphries, C., Lee, T., Mckeown, M., Iragui, V., Sejnowski, T.: Removing electroencephalographic artifacts by blind source separation. Psychophysiology 37(02), 163–178 (2000)

    Article  Google Scholar 

  4. Barlow, J.S.: Artifact processing (rejection and minimization) in eeg data processing. In: Handbook of Electroencephalography and Clinical Neurophysiology, vol. 2, pp. 15–62 (1986)

    Google Scholar 

  5. Vigon, L., Saatchi, M.R., Mayhew, J.E.W., Fernandes, R.: Quantitative evaluation of techniques for ocular artefact filtering of eeg waveforms. IEE Proceedings on Science, Measurement and Technology 147(5), 219–228 (2000)

    Article  Google Scholar 

  6. Junfeng, G., Pan, L., Yong, Y., Pei, W.: Online emg artifacts removal from eeg based on blind source separation. International Asia Conference on Informatics in Control, Automation and Robotics 1, 28–31 (2010)

    Google Scholar 

  7. Delorme, A., Sejnowski, T., Makeig, S.: Enhanced detection of artifacts in eeg data using higher-order statistics and independent component analysis. NeuroImage 34(4), 1443–1449 (2007)

    Article  Google Scholar 

  8. Schlogl, A., Keinrath, C., Zimmermann, D., Scherer, R., Leeb, R., Pfurtscheller, G.: A fully automated correction method of eog artifacts in eeg recordings. Clinical Neurophysiology 118(1), 98–104 (2007)

    Article  Google Scholar 

  9. Kher, R.K., Thakker, B.: Eeg signal enhancement and estimation using adaptive filtering. International Journal of Engineering 2(1) (2013)

    Google Scholar 

  10. He, P., Wilson, G., Russell, C.: Removal of ocular artifacts from electro-encephalogram by adaptive filtering. Medical and Biological Engineering and Computing 42(3), 407–412 (2004)

    Article  Google Scholar 

  11. Molla, M.K.I., Islam, R., Tanaka, T., Rutkowski, T.M., et al.: Artifact suppression from eeg signals using data adaptive time domain filtering. Neurocomputing 97, 297–308 (2012)

    Article  Google Scholar 

  12. Stern, J.M.: Atlas of EEG Patterns. 2 edn. Lippincott Williams & Wilkin (2013)

    Google Scholar 

  13. Patrick Berg, B.G.: Besa simulator v1.0. Electronic (June 2013)

    Google Scholar 

  14. CompuMedics/NeuroScan: Synampsrt technical specifications (2012)

    Google Scholar 

  15. Gray, H., Lewis, W.H.: Anatomy of the Human Body. Philadelphia: Lea and Febiger, 1918 Bartleby, 2000 (1825-1861)

    Google Scholar 

  16. Jasper, H.: Report of the committee on methods of clinical examination in electroencephalography. Electroenceph. Clin. Neurophysiol. 10, 370–375 (1958)

    Article  Google Scholar 

  17. Wonnacott, T.H., Wonnacott, R.J.: Introductory statistics. vol. 19690. Wiley, New York (1972)

    Google Scholar 

  18. Billings, S.A.: Nonlinear system identification: NARMAX methods in the time, frequency, and spatio-temporal domains. John Wiley & Sons (2013)

    Google Scholar 

  19. Taheri, B.A., Knight, R.T., Smith, R.L.: A dry electrode for eeg recording. Electroencephalography and Clinical Neurophysiology 90(5), 376–383 (1994)

    Article  Google Scholar 

  20. Lee, K., Gan, W.: Improving convergence of the nlms algorithm using constrained subband updates. IEEE Signal Processing Letters 11(9), 736–739 (2004)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Centre for Intelligent Systems Research, Deakin University, Waurn Ponds, VIC, 3217, Australia

    Luke Nyhof, Imali Hettiarachchi, Shady Mohammed & Saeid Nahavandi

Authors
  1. Luke Nyhof
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Imali Hettiarachchi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shady Mohammed
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Saeid Nahavandi
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Artificial Intelligence, Faculty of Computer Science and Information Technology Building, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Chu Kiong Loo

  2. Department of Electronics and Communication Engineering, College of Engineering, Jalan IKRAM-UNITEN, Universiti Tenaga Nasional, 43009, Kajang, Selangor, Malaysia

    Keem Siah Yap

  3. School of Engineering and Information Technology, Murdoch University, 6150, South St, Murdoch, Western Australia, Australia

    Kok Wai Wong

  4. Department of Electrical and Electronics Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, 120-749, Seoul, South Korea

    Andrew Teoh Beng Jin

  5. Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, Ren’ai Road 111, SIP 215123, Suzhou, Jiangsu Province, China

    Kaizhu Huang

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Nyhof, L., Hettiarachchi, I., Mohammed, S., Nahavandi, S. (2014). Adaptive-Multi-Reference Least Means Squares Filter. In: Loo, C.K., Yap, K.S., Wong, K.W., Beng Jin, A.T., Huang, K. (eds) Neural Information Processing. ICONIP 2014. Lecture Notes in Computer Science, vol 8836. Springer, Cham. https://doi.org/10.1007/978-3-319-12643-2_64

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-12643-2_64

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12642-5

  • Online ISBN: 978-3-319-12643-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics