Journal ArticleDOI
Blind noise reduction for multisensory signals using ICA and subspace filtering, with application to EEG analysis.
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TLDR
Simulations as well as real application results for EEG-signal noise elimination are included to show the validity and effectiveness of the proposed approach.Abstract:
In many applications of signal processing, especially in communications and biomedicine, preprocessing is necessary to remove noise from data recorded by multiple sensors. Typically, each sensor or electrode measures the noisy mixture of original source signals. In this paper a noise reduction technique using independent component analysis (ICA) and subspace filtering is presented. In this approach we apply subspace filtering not to the observed raw data but to a demixed version of these data obtained by ICA. Finite impulse response filters are employed whose vectors are parameters estimated based on signal subspace extraction. ICA allows us to filter independent components. After the noise is removed we reconstruct the enhanced independent components to obtain clean original signals; i.e., we project the data to sensor level. Simulations as well as real application results for EEG-signal noise elimination are included to show the validity and effectiveness of the proposed approach.read more
Citations
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Journal ArticleDOI
Mining event-related brain dynamics.
TL;DR: A new approach combines independent component analysis (ICA), time/frequency analysis, and trial-by-trial visualization that measures EEG source dynamics without requiring an explicit head model.
Journal ArticleDOI
FASTER: Fully Automated Statistical Thresholding for EEG artifact Rejection.
TL;DR: FASTER (Fully Automated Statistical Thresholding for EEG artifact Rejection) had >90% sensitivity and specificity for detection of contaminated channels, eye movement and EMG artifacts, linear trends and white noise, and aggregates the ERP across subject datasets, and detects outlier datasets.
Journal ArticleDOI
EEG artifact removal?state-of-the-art and guidelines
TL;DR: This paper presents an extensive review on the artifact removal algorithms used to remove the main sources of interference encountered in the electroencephalogram (EEG), specifically ocular, muscular and cardiac artifacts, and concludes that the safest approach is to correct the measured EEG using independent component analysis-to be precise, an algorithm based on second-order statistics such as second- order blind identification (SOBI).
Proceedings Article
Blind Source Separation and Independent Component Analysis: A Review
TL;DR: A review of BSS and ICA, including various algorithms for static and dynamic models and their applications, including several algorithms for dynamic models (convolutive mixtures) incorporating with sparseness or non-negativity constraints is presented.
Journal ArticleDOI
Optimization of an independent component analysis approach for artifact identification and removal in magnetoencephalographic signals.
TL;DR: The present noise reduction procedure, including ICA separation phase, automatic artifactual ICs selection and 'discrepancy' control cycle, showed good performances both on simulated and real MEG data and suggests the procedure to be able to separate different cerebral activity sources, even if characterized by very similar frequency contents.
References
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Book
Adaptive Filter Theory
TL;DR: In this paper, the authors propose a recursive least square adaptive filter (RLF) based on the Kalman filter, which is used as the unifying base for RLS Filters.
Book
Adaptive blind signal and image processing
Andrzej Cichocki,Shun-ichi Amari +1 more
TL;DR: Find the secret to improve the quality of life by reading this adaptive blind signal and image processing and make the words as your good value to your life.
Journal ArticleDOI
The Retrieval of Harmonics from a Covariance Function
TL;DR: In this paper, a new method for retrieving harmonics from a covariance function is introduced, based on a theorem of Caratheodory about the trigonometrical moment problem.
Book
Independent Component Analysis: Theory and Applications
TL;DR: This work presents a Unifying Information-Theoretic Framework for ICA, a novel and scalable framework for independent component analysis that combines supervised and unsupervised classification with ICA Mixture Models.
Journal ArticleDOI
Fractals and the analysis of waveforms
TL;DR: The fractal characterization may be especially useful for analyzing and comparing complex waveforms such as electroencephalograms (EEGs), where the x values increase monotonically.
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