Evolutionary Algorithm-Based Optimal Wiener-Adaptive Filter Design: An Application on EEG Noise Mitigation

Electroencephalogram (EEG) signals are well-known nonstationary brain signals of lower strength. Due to their small amplitude, they attract other biomedical artifacts from the surroundings. This research mainly focuses on removing artifacts from the EEG. The presented work uses the recent metaheuristics to efficiently design a Wiener-based adaptive noise mitigation structure (WANMS). Many powerful evolutionary optimization algorithms (EOAs), such as particle swarm optimization algorithm (PSOA), moth flame optimization algorithm (MFOA), symbiotic organism search optimization algorithm (SOSOA), and the student psychology-based optimization algorithm (SPBOA), have been applied for the optimal design of WANMS. The proposed structure is analyzed with various noisy signals, such as electrooculogram (EOG) and electrocardiogram (ECG) with white Gaussian noise (WGN). Among all the metaheuristic algorithms applied to WANMS, the SPBOA-based WANMS has performed better with improved signal-to-noise-ratio (SNR) and minimal mean-squared-error (MSE) values. The results obtained through the proposed technique ensure its supremacy compared to other state-of-the-art techniques. Hence, the proposed method can be utilized for EEG signal enhancement.