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• W4206821030 abstract "• In this research manuscript, we aim to remove the artifacts present in the acquired EEG signals. • We combine ICA with RMD-SVD which maps single channel signal into multivariate data. • Multi-view data analysis using EEG sigmoid function from source signal. • Our Novel method of RMD-SVD indicates increased noise and other artifact omitting efficiency. The EEG signals are regularly blended with sources like Electrooculogram, Electromyogram and few other artifacts caused by physical or signal interferences. The presence of artifacts induces inaccuracy in the examination of the signals acquired. Independent Component Analysis has been predominantly utilized towards these discrepancies by isolating the artifacts from the EEG signals. Direct utilization of ICA isn't conceivable with the frameworks that are outfitted with single or few EEG channels. Distinctly using ICA to eliminate artifacts on a single channel is harder. Therefore, we combine ICA with a proposed decomposition method called Regenerative Multi-Dimensional Singular Value Decomposition (RMD-SVD) which maps the acquired signals into multivariate data after which ICA is applied on it. In our proposed scheme, the pattern of a source signal is mimicked with frequency, phase and amplitude value of the input signal using EEG sigmoid function. Both the input signal and the constructed regenerative reference signals are decomposed and the most significant singular values can be observed with the help of ICA which are the values of the pure input signal. Performance measures such as SNR, PSNR, MSE etc., in our proposed systems are analyzed under different filters and it is noticed that our proposed method of RMD-SVD indicates increased noise omitting efficiency." @default.
• W4206821030 created "2022-01-26" @default.
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• W4206821030 date "2022-04-01" @default.
• W4206821030 modified "2023-10-02" @default.
• W4206821030 title "Artifact Removal from EEG signals using Regenerative Multi-Dimensional Singular Value Decomposition and Independent Component Analysis" @default.
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• W4206821030 doi "https://doi.org/10.1016/j.bspc.2021.103452" @default.
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