EEG Signal Denoising Using Hybridizing Method Between Wavelet Transform with Genetic Algorithm

Abstract

The most common and successful technique for signal denoising with non-stationary signals, such as electroencephalogram (EEG) and electrocardiogram (ECG) is the wavelet transform (WT). The success of WT depends on the optimal configuration of its control parameters which are often experimentally set. Fortunately, the optimality of the combination of these parameters can be measured in advance by using the mean squared error (MSE) function. In this paper, genetic algorithm (GA) is proposed to find the optimal WT parameters for EEG signal denoising. It is worth mentioning that this is the initial investigation of using optimization method for WT parameter configuration. This paper then examines which efficient algorithm has obtained the minimum MSE and the best WT parameter configurations. The performance of the proposed algorithm is tested using two standard EEG dataset, namely, EEG Motor Movement/Imagery dataset. The results of the proposed algorithm are evaluated using five common criteria: signal-to-noise-ratio (SNR), SNR improvement, mean square error (MSE), root mean square error (RMSE), and percentage root mean square difference (PRD). In conclusion, the results show that the proposed method for EEG signal denoising can produce better results than manual configurations based on ad hoc strategy. Therefore, using metaheuristic approaches to optimize the parameters for EEG signals positively affects the denoising process performance of the WT method.