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In this paper, a system for filtering event-related potentials/electroencephalograph is exhibited by adaptive noise canceller through an optimization algorithm, oppositional hybrid whale-grey wolf optimization algorithm (OWGWA). The OWGWA can choose the control parameters of the grey wolf algorithm utilizing whale parameters. To balance out the randomness of optimization strategies another methodology is implemented called controlled search space. Adaptive filter's noise reduction capability has been tested through adding adaptive white Gaussian noise over contaminated EEG signals at different noise levels. The performance of the proposed OWGWA-CSS algorithm is evaluated by signal to noise ratio in dB, mean value, and the relationship between resultant and input ERP. In this work, ANCs are also implemented by utilizing other optimization techniques. In average cases of noisy environment, comparative analysis shows that the proposed OWGWA-CSS technique provides higher SNR value, significantly lower mean and higher correlation as compared to other techniques.

This paper presents the Variable Step Size Least Mean Square algorithm formulated in frequency domain by taking the (Fast Fourier Transform) FFT of signal obtained from filter. This way the algorithms performed better than its implementation in time domain in terms of Signal to Noise Ratio (SNR). The algorithms implemented in MATLAB with different colored noise surroundings. To evaluate the performance of the algorithm its comparison has been done with time domain. The algorithm has given 5–44% increased SNR compared to that implemented in time domain with different type of colored noises. The algorithm has also been tested in frequency domain for different step sizes.

This paper presents a novel approach for Active Noise Cancellation through the technique of Adaptive filtering. In majority applications, algorithm used for practically implementing this concept is LMS (Least Mean Square) Algorithm. The work done here includes testing of LMS algorithm and its variants for Low and Mid Range frequency audio environments. The experimental results demonstrated in this paper are quite beneficial in identification of specific LMS variant corresponding to particular category of signals in terms of application.