Mitigation of ocular artifacts for EEG signal using improved earth worm optimization-based neural network and lifting wavelet transform.

An Electroencephalogram (EEG) is often tarnished by various categories of artifacts. Numerous efforts have been taken to improve its quality by eliminating the artifacts. The EEG involves the biological artifacts (ocular artifacts, ECG and EMG artifacts), and technical artifacts (noise from the electric power source, amplitude artifacts, etc.). From these physiological artifacts, ocular activities are one of the most well-known over other noise sources. Reducing the risks of this event and avoid it is practically very difficult, even impossible, as the ocular activities are involuntary tasks. To trim down the effect of ocular artifacts overlapping with EEG signal and overwhelm the subjected flaws, few intelligent approaches have to be developed. This proposal tempts to implement a novel method for detecting and preventing ocular artifacts from the EEG signal. The developed model involves two main phases: (a) Detection of Ocular artifacts and (b) Removal of ocular artifacts. For detecting the ocular artifacts, initially, the EEG is subjected to decomposition process using 5-level Discrete Wavelet Transform (DWT), and Empirical Mean Curve Decomposition (EMCD). Next to the decomposition process, the features like kurtosis, variance, Shannon's entropy, and few first-order statistical features are extracted. These features will be helpful for the detection process in the classification side. For detecting the ocular artifacts from the decomposed signal, the extracted features are subjected to a machine learning algorithm called Neural Network (NN). As an improvement to the conventional NN, the training algorithm of ANN is improved by the improved Earth Worm optimization Algorithm (EWA) termed as Dual Positioned Elitism-based EWA (DPE-EWA), which updates the weight of NN to improve the performance. In the Removal phase, the optimized Lifting Wavelet Transform (LWT) is deployed, in which the improvement is made on optimizing the filter coefficients using the proposed DPE-EWA. Thus, the integration of optimized NN and optimized LWT suggests a potential possibility to accommodate the detection and removal of ocular artifacts that exist in the EEG signals.