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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Looney, D., Li, L., Rutkowski, T.M., Mandic, D.P., Cichocki, A.: Ocular artifacts removal from EEG using EMD (2007)
Romo-vazquez, R., Ratna, R., Louis Dorr, V., Maquin, D.: Ocular artifacts removal in scalp EEG: combining ICA and wavelet denoising (2010)
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)
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)
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 2007
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 2012
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)
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)
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)
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)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Devulapalli, S.P., Srinivasa Rao, C., Satya Prasad, K. (2019). Novel Noise Reduction Scheme of Brain Waves. In: Hemanth, J., Fernando, X., Lafata, P., Baig, Z. (eds) International Conference on Intelligent Data Communication Technologies and Internet of Things (ICICI) 2018. ICICI 2018. Lecture Notes on Data Engineering and Communications Technologies, vol 26. Springer, Cham. https://doi.org/10.1007/978-3-030-03146-6_29
Download citation
DOI: https://doi.org/10.1007/978-3-030-03146-6_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-03145-9
Online ISBN: 978-3-030-03146-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)