Electroencephalogram denoising using discrete wavelet transform and adaptive noise cancellation based on information theory

This paper studies the behaviour of normalized least mean square (NLMS) adaptive filter algorithm-based noise canceller to eliminate intense background noise of high and low frequency from a desired signal. Noise signal filtration requires a filter which automatically adapts with the variation of input signal and noise signal. Performance is measured by optimizing rate of convergence and mean square error (MSE) using MATLAB. The experimental results indicate that adaptive noise canceller can remove low- and high-frequency noise of signals conveniently, and for small values of step size MSE decreases and for larger value of step size the rate of convergence increases. The computation time increases with the increase of filter length.

Electroencephalogram (EEG) plays an important role in E-healthcare systems, especially in the mental healthcare area, where constant and unobtrusive monitoring is desirable. In the context of OPTIMI project, a novel, low cost, and light weight wearable EEG sensor has been designed and produced. In order to improve the performance and reliability of EEG sensors in real-life settings, we propose a method to evaluate the quality of EEG signals, based on which users can easily adjust the connection between electrodes and their skin. Our method helps to filter invalid EEG data from personal trials in both domestic and office settings. We then apply an algorithm based on Discrete Wavelet Transformation (DWT) and Adaptive Noise Cancellation (ANC) which has been designed to remove ocular artifacts (OA) from the EEG signal. DWT is applied to obtain a reconstructed OA signal as a reference while ANC, based on recursive least squares, is used to remove the OA from the original EEG data. The newly produced sensors were tested and deployed within the OPTIMI framework for chronic stress detection. EEG nonlinear dynamics features and frontal asymmetry of theta, alpha, and beta bands have been selected as biological indicators for chronic stress, showing relative greater right anterior EEG data activity in stressful individuals. Evaluation results demonstrate that our EEG sensor and data processing algorithms have successfully addressed the requirements and challenges of a portable system for patient monitoring, as envisioned by the EU OPTIMI project.

The article contains the operational principle of adaptive active wideband acoustic noise cancellation system. The algorithm of operation of these systems provides to work with changes of the medium, acoustic or electrical parameters of their elements, is described. A modification of this algorithm is proposed, which can increase the efficiency of the system and reduce the level of residual noise during system calibration. The results of simulation the modified algorithm of an active noise cancellation system are presented.

Artifacts are variations in the desired signal due to other external phenomenon. Major contributors of EEG artifacts are eye movement-related artifacts. As this signal propagates over the scalp, it appears in the recorded electroencephalogram (EEG) as noise that presents serious problems in EEG interpretation and analysis. Hence, preprocessing is required before any further analysis. This paper reports on effective ocular artifact suppression using discrete wavelet transform (DWT)-combined recursive least square (RLS) adaptive noise canceller (ANC) by comparing the mean square error (MSE) and artifact-to-signal ratio (ASR) metrics for the signal obtained in the fronto-polar region where the ocular artifact is dominant.

Least Mean Square (LMS) algorithm is the most popular adaptive algorithm (AA) for realization of a digital adaptive filter (AF). In digital implementation of AF, finite word length hardware is unavoidable. This finite precision (FP) effect on the performance of the AF is crucial in the sense that, if proper word length is not used the performance in terms of the mean square error (MSE) of the filter degrades substantially. In this paper, a new approach based on the probability theory is used to analyze the FP effect of the LMS based finite impulse response (FIR) AF. An expression of the adaptation failure in terms of word length and the step size is derived. Simulation study of a FP LMS adaptive equalizer is carried out to demonstrate and verify this effect. This study provides an important guide-line to the designers for selecting the optimum word length for a specific application for a given MSE criterion.

This paper describes a method for noise removal from ultrasonic signals used in NDE (Non Destructive Evaluation) applications. The method used is FPGA based adaptive noise cancellation. Indeed adaptive filtering is one of the core technologies in digital signal processing. We can find numerous application areas in science as well as in industry. Adaptive filtering techniques are used in a wide range of applications, including system identification, adaptive equalization, adaptive noise cancellation and echo cancellation. Characteristics of signals that are generated by systems of the above application are not known a priori. Under this condition, a significant improvement in performance can be achieved by using adaptive rather than fixed filters. An adaptive filter is a self-designing filter that uses a recursive algorithm (known as adaptation algorithm or adaptive filtering algorithm) to design itself. Here the algorithm used is normalized least mean square algorithm, which has good convergence and stability. Keywords; Adaptive noise cancellation, NLMS algorithm, NDE applications, ultrasonic grain noise.

In this paper, the use of the genetic algorithm in optimization of a nonlinear filter in adaptive noise cancellation (ANC) system is proposed. While the standard adaptive algorithms in nonlinear systems may converge to a local minimum, genetic algorithms (GAs) handle this problem efficiently. Computer simulations show that a superior performance is achieved using the proposed system with a not complex GA. A comparison of the proposed system with a popular ANC system also shows a high reduction of estimation error's power.

The problem of forming images that are optimal with respect to a Mean Square Error (MSE) criterion, based on nite data, is considered. First, it is shown that the MSE criterion is consistent with the general goal of classifying images, in that decreasing the MSE guarantees a decrease in the probability of misclassifying an image. The problem of choosing sampling locations for image formation that optimize the MSE is then formulated. It is shown that this MSE minimization problem has a solution under certain conditions and necessary conditions for a minimum are obtained. The results are illustrated on a simple image formation problem.

Electroencephalogram (EEG) is a highly sensitive instrument and is frequently corrupted with eye blinks. Methods based on adaptive noise cancellation (ANC) and discrete wavelet transform (DWT) have been used as a standard technique for removal of eye blink artefacts. However, these methods often require visual inspection and appropriate thresholding for identifying and removing artefactual components from the EEG signal. The proposed work describes an automated windowed method with a window size of 0.45 s that is slid forward and fed to a support vector machine (SVM) classifier for identification of artefacts, after the identification of artefacts, it is fed to an autoencoder for correction of artefacts. The proposed method is evaluated on the data collected from the project entitled ‘Analysis of Brain Waves and Development of Intelligent Model for Silent Speech Recognition’. From the results it is observed that the proposed method performs better in identifying and removing artefactual components from EEG data than existing wavelet and ANC based methods. The proposed method does not require the application of independent component analysis (ICA) before processing and can be applied to multiple channels in parallel.

Adaptive filters, as part of digital signal systems, have been widely used, as well as in applications such as adaptive noise cancellation, adaptive beam forming, channel equalization, and system identification. However, its implementation takes a great deal and becomes a very important field in digital system world. When FPGA (Field Programmable Logic Array) grows in area and provides a lot of facilities to the designers, it becomes an important competitor in the signal processing market. In general FIR structure has been used more successfully than IIR structure in adaptive filters. However, when the adaptive FIR filter was made this required appropriate algorithm to update the filter’s coefficients. The algorithm used to update the filter coefficient is the Least Mean Square (LMS) algorithm which is known for its simplification, low computational complexity, and better performance in different running environments. When compared to other algorithms used for implementing adaptive filters the LMS algorithm is seen to perform very well in terms of the number of iterations required for convergence. This phenomenon can be achieved by a sufficient choice of bit length to represent the filter’s coefficients. This paper presents a lowcost and high performance programmable digital finite impulse response (FIR) filter. It follows the adaptive algorithm used for the development of the system. The architecture employs the computation sharing algorithm to reduce the computation complexity.

In many deep learning based speech enhancement frameworks, the deep neural network (DNN) is usually trained under the mean square error (MSE) criterion to estimate the potential clean amplitudes. Yet, the MSE criterion is not directly connected with speech perception, which potentiates alternative training targets for further improvements in speech quality and intelligibility. This paper introduces maximizing the correlation between the enhanced speech and the reference, so as to further improve the speech quality and intelligibility of the enhanced speech synchronously. Based on the short-time Fourier transform amplitude spectra, the correlations are firstly calculated between the envelops along the time and frequency respectively. Then, the time and frequency envelop correlations are incorporated into the time-frequency correlation (TFC). Finally, the loss function is defined as 1 minus the TFC. In experiment, several loss functions containing the time and frequency envelop correlations are investigated. The system trained with the TFC loss outperforms the baseline trained with the MSE loss with promising improvements in both speech quality and intelligibility. Phoneme recognition results also confirm the effectiveness of maximizing the correlation for speech enhancement.

Autism Spectrum Disorder (ASD) diagnosis can greatly benefit from more efficient and accurate tools to enable early intervention and reduce long-term healthcare costs associated with delayed diagnosis. Electroencephalography (EEG) has emerged as a promising non-invasive technique for detecting neural patterns linked to ASD. This research evaluates the effectiveness of three preprocessing techniques, Butterworth, Discrete Wavelet Transform (DWT), and Independent Component Analysis (ICA), in enhancing EEG signal quality for ASD classification. The performance of each method is assessed using Signal-to-Noise Ratio (SNR), Mean Absolute Error (MAE), Mean Squared Error (MSE), Spectral Entropy (SE), and Power Spectral Density (PSD) analysis to explore frequency band distribution. Additionally, Hjorth parameters—activity, mobility, and complexity—are computed to capture neural dynamics associated with ASD. Results showed that ICA achieved the highest SNR values (normal: 86.44, ASD: 78.69), indicating superior denoising capability, while DWT offered the lowest error metrics (MAE: 4785.08, MSE: 309,690 for ASD), demonstrating its robustness in preserving signal characteristics. Butterworth provided moderate results across metrics. Notably, Hjorth parameters revealed that neurotypical EEGs exhibited higher activity and complexity, highlighting distinct neural dynamics compared to ASD. These findings suggest that ICA is optimal for applications prioritizing signal clarity, while DWT offers a balanced approach for feature preservation in ASD EEG analysis. These findings are expected to support the development of more accurate, EEG-based diagnostic tools for ASD that can be integrated into clinical decision support systems and early screening programs.

We present an estimation-based approach to the design of adaptive IIR filters. We also use this approach to design adaptive filters when a feedback signal from the output of the adaptive filter contaminates the reference signal. We use an H/sub /spl infin// criterion to cast the problem as a nonlinear H/sub /spl infin// filtering problem, and present an approximate linear H/sub /spl infin// filtering solution. This linear filtering solution is then used to adapt the adaptive IIR Filter. The presentation of the proposed adaptive algorithm is done in the context of an adaptive active noise cancellation problem. Simulations are used to examine the performance of the proposed estimation-based adaptive algorithm.

In this paper, a regularized correntropy criterion (RCC) for extreme learning machine (ELM) is proposed to deal with the training set with noises or outliers. In RCC, the Gaussian kernel function is utilized to substitute Euclidean norm of the mean square error (MSE) criterion. Replacing MSE by RCC can enhance the anti-noise ability of ELM. Moreover, the optimal weights connecting the hidden and output layers together with the optimal bias terms can be promptly obtained by the half-quadratic (HQ) optimization technique with an iterative manner. Experimental results on the four synthetic data sets and the fourteen benchmark data sets demonstrate that the proposed method is superior to the traditional ELM and the regularized ELM both trained by the MSE criterion.

MSE superiority of Bayes and empirical Bayes estimators in two generalized seemingly unrelated regressions