EEG adaptive noise cancellation using information theoretic approach
Source abstract
Abstract
OBJECTIVE: In this paper, an adaptive method based on error entropy criterion is presented in order to eliminate noise from Electroencephalogram (EEG) signals. METHOD: Conventionally, the Mean-Squared Error (MSE) criterion is the dominant criterion deployed in the adaptive filters for this purpose. By deploying MSE, only second-order moment of the error distribution is optimized, which is not adequate for the noisy EEG signal in which the contaminating noises are typically non-Gaussian. By minimizing error entropy, all moments of the error distribution are minimized; hence, using the Minimum Error Entropy (MEE) algorithm instead of MSE-based adaptive algorithms will improve the performance of noise elimination. RESULTS: Simulation results indicate that the proposed method has a better performance compared to conventional MSE-based algorithm in terms of signal to noise ratio and steady state error.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Advanced Adaptive Filtering Techniques · EEG and Brain-Computer Interfaces · Blind Source Separation Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Electrical Engineering, Sadjad University of Technology, Mashhad, Iran
Department of Biomedical Engineering, Sadjad University of Technology, Mashhad, Iran
References (30)
Show source-provided references
1. EEG-based brain–computer interfaces: An overview of basic concepts and clinical applications in neurorehabilitation10.1515/REVNEURO.2010.21.6.451 ↗
2. Brain–computer interface technology: A review of the first international meeting10.1109/TRE.2000.847807 ↗
3. S. Sanei and J.A. Chambers, EEG Signal Processing, Wiley, New York, 2007.10.1002/9780470511923 ↗
4. 10.1088/1741-2560/7/1/01600310.1088/1741-2560/7/1/016003 ↗
5. Sensorimotor rhythm-based brain–computer interface (BCI): Feature selection by regression improves performance10.1109/TNSRE.2005.848627 ↗
6. Removal of ocular artifacts from the EEG: A comparison between time-domain regression method and adaptive filtering method using simulated data10.1007/s11517-007-0179-9 ↗
7. The removal of the eye-movement artifact from the EEG by regression analysis in the frequency domain10.1016/0301-0511(83)90059-5 ↗
8. Removal of ocular artifacts from EEG signals using adaptive threshold PCA and wavelet transforms
9. Removal of eye blinking artifact from the electro-encephalogram, incorporating a new constrained blink source separation algorithm10.1007/BF02345968 ↗
10. Dimension reduction: Additional benefit of an optimal filter for independent component analysis to extract event-related potentials10.1016/j.jneumeth.2011.07.015 ↗
11. Source localization of event-related potentials incorporating spatial notch filters10.1109/TBME.2008.920872 ↗
12. A study on discrete wavelet-based noise removal from EEG signals10.1007/978-1-4419-5913-3_65 ↗
13. Single-trial evoked potential estimation using wavelets10.1016/j.compbiomed.2006.08.011 ↗
14. Removal of ocular artifact from the EEG: A review10.1016/S0987-7053(00)00055-1 ↗
15. Adaptive noise cancellation: Principles and applications10.1109/PROC.1975.10036 ↗
16. Adaptive spatio-temporal filtering for movement related potentials in EEG-based brain–computer interfaces10.1109/TNSRE.2014.2315717 ↗
17. Automatic identification and removal of ocular artifacts in EEG – Improved adaptive predictor filtering for portable applications10.1109/TNB.2014.2316811 ↗
18. Comparison of basic linear filters in extracting auditory evoked potentials
19. Removal of ocular artifacts from electroencephalogram by adaptive filtering10.1007/BF02344717 ↗
20. EEG/ERP adaptive noise canceller design with controlled search space (CSS) approach in cuckoo and other optimization algorithms10.1109/TCBB.2013.119 ↗
21. J.C. Principe, Renyi’s entropy, divergence and their nonparametric estimators, in: Information Theoretic Learning: Renyi’s Entropy and Kernel Perspectives, Springer, New York, 2010, pp. 47–102.10.1007/978-1-4419-1570-2_2 ↗
22. A mathematical theory of communications10.1002/j.1538-7305.1948.tb01338.x ↗
23. An error-entropy minimization algorithm for supervised training of nonlinear adaptive systems10.1109/TSP.2002.1011217 ↗
24. Online entropy manipulation: Stochastic information gradient10.1109/LSP.2003.814400 ↗
25. S. Han, S. Rao, D. Erdogmus and J. Principe, An improved minimum error entropy criterion with self adjusting step-size, in: IEEE Workshop on Machine Learning for Signal Processing, 2005, 317–322.
26. S. Han, S. Rao, K. Jeong and J. Principe, A normalized minimum error entropy stochastic algorithm, in: IEEE International Conference on Acoustics Speech and Signal Processing: Proceedings, Vol. 5, 2006, pp. 609–612.10.1109/ICASSP.2006.1661349 ↗
27. Minimum entropy filtering for multivariate stochastic systems with non-Gaussian noises10.1109/TAC.2006.872771 ↗
28. A class of adaptive algorithms based on entropy estimation achieving CRLB for linear non-Gaussian filtering10.1109/TSP.2011.2182345 ↗
29. Minimum total error entropy method for parameter estimation10.1109/TSP.2015.2437836 ↗
30. J.C. Principe, Adaptive information filtering with error entropy and error correntropy criterion, in: Information Theoretic Learning: Renyi’s Entropy and Kernel Perspectives, Springer, New York, 2010, pp. 103–141.10.1007/978-1-4419-1570-2_3 ↗