Darroudi Ali, Parchami Jaber, Razavi Morteza Kafaee, Sarbisheie Ghazaleh
Department of Electrical Engineering, Sadjad University of Technology, Mashhad, Iran.
Department of Biomedical Engineering, Sadjad University of Technology, Mashhad, Iran.
Biomed Mater Eng. 2017;28(4):325-338. doi: 10.3233/BME-171680.
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.
本文提出一种基于误差熵准则的自适应方法，用于消除脑电图（EEG）信号中的噪声。
传统上，均方误差（MSE）准则是用于此目的的自适应滤波器中使用的主要准则。通过采用MSE，仅优化了误差分布的二阶矩，这对于其中污染噪声通常为非高斯的噪声EEG信号而言是不够的。通过最小化误差熵，误差分布的所有矩都被最小化；因此，使用最小误差熵（MEE）算法而非基于MSE的自适应算法将提高噪声消除性能。
仿真结果表明，与传统的基于MSE的算法相比，该方法在信噪比和稳态误差方面具有更好的性能。
