Key result
This review provides an overview of the most common sources of noise and methods for the prevention and removal of artifacts in EEG recordings.
Adaptive filtering using LMS and NLMS algorithms provides an effective method for removing baseline and powerline artifacts from EEG signals while preserving clinical information.
Artifact control is essential for valid clinical EEG; leaves open standardized method comparisons in future research.
Electroencephalographic (EEG) recordings are often contaminated with several artifacts.Powerline interference and baseline noise is always present in EEG response of every patient. A number of strategies are available to deal with noise effectively both at the time of EEG recording as well as during preprocessing of recorded data. The aim of the paper is to give an overview of the most common sources of noise and review methods for prevention and removal of noise in EEG recording ,including elimination of noise sources.
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GuruvaReddy et al. (2013) conducted a review in EEG artifacts. Artifact removal methods was evaluated. This review provides an overview of the most common sources of noise and methods for the prevention and removal of artifacts in EEG recordings.
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