Affiliations: [a] Department of Electrical Engineering, Sadjad University of Technology, Mashhad, Iran | [b] Department of Biomedical Engineering, Sadjad University of Technology, Mashhad, Iran
Correspondence:
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Corresponding author: Jaber Parchami, Department of Electrical Engineering, Sajad University of Technology, Jalal 64 St., Jalal Blvd, Mashhad, Iran. Tel.: +989304775665; E-mail: [email protected].
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.
Keywords: Adaptive filtering, entropy, LMS algorithm, MEE algorithm, noise cancellation
