Advertisement

Novel Noise Reduction Scheme of Brain Waves

  • Shyam Prasad DevulapalliEmail author
  • Ch. Srinivasa Rao
  • K. Satya Prasad
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 26)

Abstract

Electroencephalographic (EEG) recordings are influenced with numerous artifacts. Power line intrusion as well as baseline noise is always exist in every patient’s EEG response. Numerous schemes may be implemented for optimizing the noise efficiently during the course of EEG recording and processing the same detected signal. Prime objective of this suggested paper is presenting the basic noise sources and corresponding optimization schemes for avoidance and elimination of noise exist in detected EEG signal.

Keywords

Artefact Electroencephalogram Line interference EEG response 

References

  1. 1.
    Looney, D., Li, L., Rutkowski, T.M., Mandic, D.P., Cichocki, A.: Ocular artifacts removal from EEG using EMD (2007)Google Scholar
  2. 2.
    Romo-vazquez, R., Ratna, R., Louis Dorr, V., Maquin, D.: Ocular artifacts removal in scalp EEG: combining ICA and wavelet denoising (2010)Google Scholar
  3. 3.
    Li, M.-A., Yang, L.-B., Yang, J.-F.: A fully automatic method of removing EOG artifacts from EEG recordings. Commun. Inf. Sci. Manag. Eng. 1(2), 1–6 (2011)Google Scholar
  4. 4.
    Zhao, Q., Hu, B., Shi, Y., Li, Y., Moore, P., Sun, M., Peng, H.: Automatic identification and removal of ocular artifacts in EEG-improved adaptive predictor filtering for portable applications. IEEE Trans. Nano-Biosci. 13(2), 109–117 (2014)CrossRefGoogle Scholar
  5. 5.
    Inuso, G.: Wavelet-ICA methodology for efficient artifact removal from Electroencephalographic recordings. In: Proceedings of International Joint Conference on Neural Networks, Orlando, Florida, USA, 12–17 August 2007Google Scholar
  6. 6.
    Kang, D., Zhizeng, L.: A method of de-noising multi-channel EEG signals fast based on PCA and DEBSS algorithm. In: 2012 International Conference on Computer Science and Electronics Engineering (ICCSEE), vol. 3, pp. 322–326, 23–25 March 2012Google Scholar
  7. 7.
    Walters-Williams, J., Li, Y.: BMICA-independent component analysis based on B-spline mutual information estimation for EEG signals. Can. J. Biomed. Eng. Technol. 3(4) (2012)Google Scholar
  8. 8.
    Zhao, G., Pietikäinen, M.: Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Trans. Pattern Anal. Mach. Intell. 29(6), 915–928 (2007)CrossRefGoogle Scholar
  9. 9.
    Kollreider, K., Fronthaler, H., Faraj, M.I., Bigun, J.: Real-time face detection and motion analysis with application in ‘liveness’ assessment. IEEE Trans. Inf. Forensics Secur. 2(3), 548–558 (2007)CrossRefGoogle Scholar
  10. 10.
    Yao, S., Lin, W., Ong, E., Lu, Z.: Contrast signal-to-noise ratio for image quality assessment. In: Proceedings of IEEE ICIP, pp. 397–400 (2005)Google Scholar
  11. 11.
    Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process 13(4), 600–612 (2004)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Shyam Prasad Devulapalli
    • 1
    Email author
  • Ch. Srinivasa Rao
    • 2
  • K. Satya Prasad
    • 3
  1. 1.CVR College of EngineeringIbrahimpatnamIndia
  2. 2.University College of EngineeringHyderabadIndia
  3. 3.Viziayanagram, KL UniversityVizayawadaIndia

Personalised recommendations