ica technique (Swedish Orphan Biovitrum)
90
Structured Review
Swedish Orphan Biovitrum
ica technique
Ica Technique, supplied by Swedish Orphan Biovitrum, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/ica+technique/ica+technique/pm33905835-172-17-24
Average 90 stars, based on 1 article reviews
Ica Technique, supplied by Swedish Orphan Biovitrum, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/ica+technique/ica+technique/pm33905835-172-17-24
Average 90 stars, based on 1 article reviews
ica technique - by Bioz Stars,
2026-10
90/100 stars
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other:Article Title: The removal of ocular artifacts from EEG signals using adaptive filters based on ocular source components. Article Snippet: Ocular artifacts are the most important form of interference in electroencephalogram (EEG) signals.. An adaptive filter based on reference signals from an electrooculogram (EOG) can reduce ocular interference, but collecting EOG signals during a long-term EEG recording is inconvenient and uncomfortable for the patient.. In contrast, blind source separation (BSS) is a method of decomposing multiple EEG channels into an equal number of source components (SCs) by independent component analysis. Article Title: Unsupervised detection and removal of muscle artifacts from scalp EEG recordings using canonical correlation analysis, wavelets and random forests. Article Snippet: Objective: This paper proposes supervised and unsupervised algorithms for automatic muscle artifact detection and removal from long-term EEG recordings, which combine canonical correlation analysis (CCA) and wavelets with random forests (RF).. Methods: The proposed algorithms first perform CCA and continuous wavelet transform of the canonical components to generate a number of features which include component autocorrelation values and wavelet coefficient magnitude values.. A subset of the most important features is subsequently selected using RF and labelled observations (supervised case) or synthetic data constructed from the original observations (unsupervised case). Article Title: Cardiac self-gating using blind source separation for 2D cine cardiovascular magnetic resonance imaging. Article Snippet: Purpose: To develop and validate a new cardiac self-gating algorithm using blind source separation for 2D cine steady-state free precession (SSFP) imaging.. Methods: A standard cine SSFP sequence was modified so that the center point of k-space was sampled with each excitation.. The center points of k-space were processed by 4 blind source separation methods, and used to detect heartbeats and assign k-space data to appropriate time points in the cardiac cycle. Article Title: Signal-to-noise ratio of the MEG signal after preprocessing. Article Snippet: The improvement is clearly higher, around 35%, for |