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Signal Artifacts and Techniques for Artifacts and Noise Removal

Part of the Intelligent Systems Reference Library book series (ISRL,volume 192)


Biosignals have quite low signal-to-noise ratio and are often corrupted by different types of artifacts and noises originated from both external and internal sources. The presence of such artifacts and noises poses a great challenge in proper analysis of the recorded signals and thus useful information extraction or classification in the subsequent stages becomes erroneous. This eventually results either in a wrong diagnosis of the diseases or misleading the feedback associated with such biosignal-based systems. Brain-Computer Interfaces (BCIs) and neural prostheses are among the popular ones. There have been many signal processing-based algorithms proposed in the literature for reliable identification and removal of such artifacts from the biosignal recordings. The purpose of this chapter is to introduce different sources of artifacts and noises present in biosignal recordings, such as EEG, ECG, and EMG, describe how the artifact characteristics are different from signal-of-interest, and systematically analyze the state-of-the-art signal processing techniques for reliable identification of these offending artifacts and finally removing them from the raw recordings without distorting the signal-of-interest. The analysis of the biosignal recordings in time, frequency and tensor domains is of major interest. In addition, the impact of artifact and noise removal is examined for BCI and clinical diagnostic applications. Since most biosignals are recorded in low sampling rate, the noise removal algorithms can be often applied in real time. In the case of tensor domain systems, more care has to be taken to comply with real time applications. Therefore, in the final part of this chapter, both quantitative and qualitative measures are demonstrated in tables and the algorithms are assessed in terms of their computational complexity and cost. It is also shown that availability of some a priori clinical or statistical information can boost the algorithm performance in many cases.


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Automatic EEG artifact detector based on the joint use of spatial and temporal features


Least Mean Square


Adaptve Line Enhancer


Linear Time Invariant


Adaptive Noise Canceller


Multiple artifact rejection algorithm


Adaptive Neural Fuzzy Inference System


Morphological Component Analysis


Artificial Neural Network


Markov Chain Monte Carlo


Auto-Regressive Exogenous




Artifact Subspace Reconstruction


Motor Imagery


Additive White Gaussian Noise


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multichannel Wiener filter


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Naïve Bayes


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biomedical signal processing toolbox


Principal Component Analysis


Blind Source Separation




Canonical Correlation Analysis


Preprocessing Framework of Electrograms Intermittently Fiducialized from Experimental Recordings


Continuous Wavelet Transform


Power Line Interference


Discrete Wavelet Transform




Extracellular Action Potential


Polygraphic Recording Analyzer




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Recursive Least Square




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Second-Order Statistics




Signal Processing Techniques


Event Related Potential


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Fully Automated Statistical Thresholding for EEG Artifact Rejection


Steady State Visual Evoked Potential


Fast Fourier Transform


Short Time Fourier Transform


Finite Impulse Response


Singular Value Decomposition


Functional Link Neural Network


Support Vector Machine


Fourth-order Tensor method


Stationary Wavelet Transform


Fully online and automated artifact removal for BCI


Temporal De-correlation source SEParation


Human-Computer Interfacing


Variational Bayes Approximation


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Islam, M.K., Rastegarnia, A., Sanei, S. (2021). Signal Artifacts and Techniques for Artifacts and Noise Removal. In: Ahad, M.A.R., Ahmed, M.U. (eds) Signal Processing Techniques for Computational Health Informatics. Intelligent Systems Reference Library, vol 192. Springer, Cham.

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