Artifacts Removal using Dragonfly Levenberg Marquardt-Based Learning Algorithm from Electroencephalogram Signal

Abstract

: Electroencephalogram (EEG) is the recording of the electrical activity of the brain. The waveforms that are recorded from the brain regions show the cortical activity. The integration of EEG signals with other bio-signals is known as artifacts. Some of the artifacts are Electrooculogram (EOG), Electrocardiogram (ECG), and Electromyogram (EMG). The artifacts removed from the EEG signal are very challenging in medical. This paper presents the Dragonfly Levenberg Marquardt (DrLM) optimization-based Neural Network (NN) to remove the artifacts from EEG. Initially, the EEG signal is subjected to adaptive filter for determining the optimal weights based on Dragonfly Algorithm (DA) and LM. These two approaches are hybridized and given to the NN to identify the weights. At last, the artifacts are removed from the EEG signal. The performance of DrLM-NN is evaluated in terms of SNR, MSE, and RMSE. The proposed artifact removal method achieves the maximum SNR of 45.67, minimal MSE of 2982, and minimal RMSE of 1.11 that indicates its superiority.