EEG adaptive noise cancellation using information theoretic approach

Abstract

In this paper, an adaptive method based on error entropy criterion is presented in order to eliminate noise from Electroencephalogram (EEG) signals. 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. 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.