Power Aware Hessian Multi-set Canonical Correlations Based Algorithm for Wireless Eeg Sensor Networks

  • M. ManojprabuEmail author
  • V. R. Sarma Dhulipala


The miniaturized Electroencephalography (EEG) modules monitors the EEG signals over a smaller area, however, thus modules suffer from poor spatial coverage. The wireless EEG sensor network (WESN) provides an improved spatial coverage with multiple EEG modules, which communicated through a shorter distances. Further, the miniaturized EEG modules tend to create a high energy cost in wireless communication. This paper aims to remove the eye blink artifacts from the WESN EEG channels. The exploitation of correlation among the signals from various EEG modules is taken into consideration for resolving the problem of artifact removal in stringent bandwidth constraints. In this paper, Hessian multi-set canonical correlations based algorithm is used that computes optimal linear combination of the EEG signal between the local EEG channels and other modules. This correlation makes the EEG signal to be correlated at a maximum rate. The use of Hessian multi-set canonical correlations to remove the eye blink artifacts in a distributed realization reduces the transmission cost in the network. The proposed method has been validated against the real and synthetic EEG datasets, collected from the WESN shortest distance communication. The removal of redundant data from the wireless nodes, the algorithm attains an improved performance to remove the eye blink artifact with reduced power consumption in the wireless networks.


Wireless EEG sensor network Electroencephalography Hessian multi-set canonical correlations Eye blink artifacts Signal-to-error ratio 


Compliance with Ethical Standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical Statement

This article does not contain any studies with animals performed by any of the authors.


  1. 1.
    Casson, A. J., Yates, D. C., Smith, S. J., Duncan, J. S., & Rodriguez-Villegas, E. (2010). Wearable electroencephalography. IEEE Engineering in Medicine and Biology Magazine,29(3), 44–56.CrossRefGoogle Scholar
  2. 2.
    Sun, M., Jia, W., Liang, W., & Sclabassi, R. J. (2012). A low-impedance, skin-grabbing, and gel-free EEG electrode. In Engineering in Medicine and Biology Society (EMBC), 2012 annual international conference of the IEEE (pp. 1992–1995). IEEE.Google Scholar
  3. 3.
    Bertrand, A. (2015). Distributed signal processing for wireless EEG sensor networks. IEEE Transactions on Neural Systems and Rehabilitation Engineering,23(6), 923–935.CrossRefGoogle Scholar
  4. 4.
    Grech, R., Cassar, T., Muscat, J., Camilleri, K. P., Fabri, S. G., Zervakis, M., et al. (2008). Review on solving the inverse problem in EEG source analysis. Journal of NeuroEngineering and Rehabilitation,5(1), 25.CrossRefGoogle Scholar
  5. 5.
    Blankertz, B., Dornhege, G., Krauledat, M., Müller, K. R., & Curio, G. (2007). The non-invasive Berlin brain–computer interface: Fast acquisition of effective performance in untrained subjects. NeuroImage,37(2), 539–550.CrossRefGoogle Scholar
  6. 6.
    Nazarpour, K., Wongsawat, Y., Sanei, S., Chambers, J. A., & Oraintara, S. (2008). Removal of the eye-blink artifacts from EEGs via STF-TS modeling and robust minimum variance beamforming. IEEE Transactions on Biomedical Engineering,55(9), 2221–2231.CrossRefGoogle Scholar
  7. 7.
    Noureddin, B., Lawrence, P. D., & Birch, G. E. (2012). Online removal of eye movement and blink EEG artifacts using a high-speed eye tracker. IEEE Transactions on Biomedical Engineering,59(8), 2103–2110.CrossRefGoogle Scholar
  8. 8.
    Corsini, J., Shoker, L., Sanei, S., & Alarcón, G. (2006). Epileptic seizure predictability from scalp EEG incorporating constrained blind source separation. IEEE Transactions on Biomedical Engineering,53(5), 790–799.CrossRefGoogle Scholar
  9. 9.
    Khatun, S., Mahajan, R., & Morshed, B. I. (2016). Comparative study of wavelet-based unsupervised ocular artifact removal techniques for single-channel EEG data. IEEE Journal of Translational Engineering in Health and Medicine,4, 1–8.CrossRefGoogle Scholar
  10. 10.
    Mahajan, R., & Morshed, B. I. (2015). Unsupervised eye blink artifact denoising of EEG data with modified multiscale sample entropy, kurtosis, and Wavelet-ICA. IEEE Journal of Biomedical and Health Informatics,19(1), 158–165.CrossRefGoogle Scholar
  11. 11.
    Guerrero-Mosquera, C., & Navia-Vázquez, A. (2012). Automatic removal of ocular artefacts using adaptive filtering and independent component analysis for electroencephalogram data. IET Signal Processing,6(2), 99–106.MathSciNetCrossRefGoogle Scholar
  12. 12.
    Ting, C. M., Salleh, S. H., Zainuddin, Z. M., & Bahar, A. (2014). Artifact removal from single-trial ERPs using non-Gaussian stochastic volatility models and particle filter. IEEE Signal Processing Letters,21(8), 923–927.CrossRefGoogle Scholar
  13. 13.
    Dammers, J., Schiek, M., Boers, F., Silex, C., Zvyagintsev, M., Pietrzyk, U., et al. (2008). Integration of amplitude and phase statistics for complete artifact removal in independent components of neuromagnetic recordings. IEEE Transactions on Biomedical Engineering,55(10), 2353–2362.CrossRefGoogle Scholar
  14. 14.
    Constantin, I., Richard, C., Lengelle, R., & Soufflet, L. (2006). Nonlinear regularized Wiener filtering with kernels: Application in denoising MEG data corrupted by ECG. IEEE Transactions on Signal Processing,54(12), 4796–4806.CrossRefGoogle Scholar
  15. 15.
    Shoker, L., Sanei, S., & Chambers, J. (2005). Artifact removal from electroencephalograms using a hybrid BSS-SVM algorithm. IEEE Signal Processing Letters,12(10), 721–724.CrossRefGoogle Scholar
  16. 16.
    Shao, S. Y., Shen, K. Q., Ong, C. J., Wilder-Smith, E. P., & Li, X. P. (2009). Automatic EEG artifact removal: A weighted support vector machine approach with error correction. IEEE Transactions on Biomedical Engineering,56(2), 336–344.CrossRefGoogle Scholar
  17. 17.
    Vigon, L., Saatchi, M. R., Mayhew, J. E. W., & Fernandes, R. (2000). Quantitative evaluation of techniques for ocular artefact filtering of EEG waveforms. IEE Proceedings-Science, Measurement and Technology,147(5), 219–228.CrossRefGoogle Scholar
  18. 18.
    Wu, J., Ifeachor, E. C., Allen, E. M., Wimalaratna, S. K., & Hudson, N. R. (1997). Intelligent artefact identification in electroencephalography signal processing. IEEE Proceedings-Science, Measurement and Technology,144(5), 193–201.CrossRefGoogle Scholar
  19. 19.
    Bertrand, A., & Moonen, M. (2015). Distributed canonical correlation analysis in wireless sensor networks with application to distributed blind source separation. IEEE Transactions on Signal Processing,63(18), 4800–4813.MathSciNetCrossRefGoogle Scholar
  20. 20.
    Bertrand, A., & Moonen, M. (2014). Distributed eye blink artifact removal in a wireless EEG sensor network. In 2014 IEEE international conference on acoustics, speech and signal processing (ICASSP) (pp. 5849–5853). IEEE.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2020

Authors and Affiliations

  1. 1.Angel College of Engineering and TechnologyTirupurIndia
  2. 2.Anna University, BIT CampusTiruchirappalliIndia

Personalised recommendations