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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 an optimization algorithm for Adaptive Noise Canceller by taking Electroencephalography/Event-Related Potentials (EEG/ERP) as an input signal. In this paper Adaptive Noise Cancellers are implemented by utilizing gradient based algorithms, swarm-based algorithm and nature inspired algorithm which is Grey Wolf Optimization (GWO). To improve the ANC performance, an optimal weight value is calculated and GWO is used to update optimal weight value. Testing of an adaptive filter has been done through consideration of White Gaussian Noise (WGN) over sample input EEG signals at various SNR levels. The performance of the GWO algorithm is evaluated in terms of mean value, Signal to Noise Ratio (SNR) in dB and correlation. A comparative analysis shows that proposed GWO technique gives better performance when compared with the gradient-based techniques like RLS, LMS and swarm-based technique like Particle Swarm Optimization (PSO). The novelty of this proposed approach can be considered, as because first-time GWO is applied on denoising EEG signals from the contaminated EEG signal, the result shows that extracting the desired EEG component is more effective in the proposed GWO method.

This paper discusses the implementation of variants of LMS (Least Mean Square) algorithm on new model named as Suspended Feedback (SuFee). Implementation of LMS (Least Mean Square) algorithm along with its variants like Constraint Stability Least Mean Square (CSLMS) and Non-Linear Data Least Mean Square (NDLMS) with conventional model and new SuFee model has been done. This model is an advanced version of conventional adaptive noise canceller. It is a simulation-based signal flow model. The conventional and SuFee models are evaluated and simulated using MATLAB, taking speech as an input signal mixed with white noise. The new SuFee model provides an advantage of faster error convergence with significant improvement in the output quality of the signal. Convergence was evaluated by displaying the learning curves and error signals of the different adaptive filter algorithms. Signal to Noise Ratio(SER) is the parameter to evaluate the quality of the output signal. The result shows the signal quality has been enhanced by three times better than the conventional model.

This paper presents the reduction of baseline wander noise found in ECG signals. The reduction has been done using wavelet transform inspired error normalized step size least mean square (ENSS-LMS) algorithm. We are presenting a wavelet decomposition-based filtering technique to minimize the computational complexity along with the good quality of output signal. The MATLAB simulation results validate the good noise rejection in output signal by analyzing parameters, excess mean square error (EMSE) and misadjustment.

In this paper, the Oppositional Whale Optimization Algorithm (OWOA) is applied to Adaptive Noise Canceller (ANC) for the filtering of Electroencephalography/Event-Related Potentials (EEG/ERP) signals. Performance of ANC will be improved by calculating the optimal weight value and proposed OWOA technique is used to update weight value. Adaptive filter’s noise reduction capability has been tested through consideration of White Gaussian Noise (WGN) over contaminated EEG signals at various SNR levels ([Formula: see text]10[Formula: see text]dB, [Formula: see text]15[Formula: see text]dB and [Formula: see text]20[Formula: see text]dB). The performance of the proposed OWOA algorithm is assessed in terms of Signal to Noise Ratio (SNR) in dB, mean value, and the correlation between resultant and input ERP. In this work, ANCs are also implemented by utilizing conventional gradient-based techniques like Recursive Least Square (RLS), Least Mean Square (LMS) and other optimization algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and WOA techniques. In average cases of noisy environment, comparative analysis shows that the proposed OWOA technique provides higher SNR value and significantly lower mean, and correlation as compared to gradient-based and swarm-based techniques. The comparative results show that extracting the desired EEG component is more effective in the proposed OWOA method. So, it has seen that OWOA-based noise reduction technique removing the artifacts and improving the quality of EEG signals significantly for biomedical analysis.

This paper presents the performance analysis of Least Mean Square (LMS) algorithm for adaptive noise cancellation by varying its step size parameter μ for different filter order and no of iteration. The presented work has been simulated in MATLAB and verified that the step size parameter plays a vital role for implementation of Least Mean Square (LMS) algorithm. Increasing the step size parameter μ leads to fast convergence rate and instability of the least mean square algorithm. On the other side if the step size parameter μ is small then the error reduced to great amount but algorithm converges slowly and becomes stable. On the basis of obtained results we can conclude that step size parameter μ is directly proportional to convergence rate and error reduction and inversely proportional to stability. The work presented here also shown the comparison of actual weights and the estimated weights.

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.