Noncontact Current Source Reconstruction Based on Independent Component Analysis-A Simulation Study

2019 ◽  
Author(s):  
Ayambire Patrick Nyaaba ◽  
Qi Huang ◽  
Jian Li ◽  
Siming Pan ◽  
Bin Gao ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Simulation Study ◽  
Current Source ◽  
Component Analysis ◽  
Independent Component ◽  
Source Reconstruction

scholarly journals Robust Independent Component Analysis Based on Two Scatter Matrices

2016 ◽  
Vol 37 (1) ◽  
Author(s):  
Klaus Nordhausen ◽  
Hannu Oja ◽  
Esa Ollila
Keyword(s):  
Independent Component Analysis ◽  
Covariance Matrix ◽  
Simulation Study ◽  
Component Analysis ◽  
Independent Component ◽  
Scatter Matrix ◽  
Independent Components

Oja, Sirkiä, and Eriksson (2006) and Ollila, Oja, and Koivunen (2007) showed that, under general assumptions, any two scatter matrices with the so called independent components property can be used to estimate the unmixing matrix for the independent component analysis (ICA). The method is a generalization of Cardoso’s (Cardoso, 1989) FOBI estimate which uses the regular covariance matrix and a scatter matrix based on fourth moments. Different choices of the two scatter matrices are compared in a simulation study. Based on the study, we recommend always the use of two robust scatter matrices. For possible asymmetric independent components, symmetrized versions of the scatter matrix estimates should be used.

scholarly journals Independent Component Analysis for Source Localization of EEG Sleep Spindle Components

2010 ◽  
Vol 2010 ◽  
pp. 1-12 ◽  
Author(s):  
Erricos M. Ventouras ◽  
Periklis Y. Ktonas ◽  
Hara Tsekou ◽  
Thomas Paparrigopoulos ◽  
Ioannis Kalatzis ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Visual Analysis ◽  
Activity Patterns ◽  
Current Source ◽  
Component Analysis ◽  
Independent Component ◽  
Source Analysis ◽  
Sleep Spindles ◽  
Electroencephalogram Eeg ◽  
Selection Of

Sleep spindles are bursts of sleep electroencephalogram (EEG) quasirhythmic activity within the frequency band of 11–16 Hz, characterized by progressively increasing, then gradually decreasing amplitude. The purpose of the present study was to process sleep spindles with Independent Component Analysis (ICA) in order to investigate the possibility of extracting, through visual analysis of the spindle EEG and visual selection of Independent Components (ICs), spindle “components” (SCs) corresponding to separate EEG activity patterns during a spindle, and to investigate the intracranial current sources underlying these SCs. Current source analysis using Low-Resolution Brain Electromagnetic Tomography (LORETA) was applied to the original and the ICA-reconstructed EEGs. Results indicated that SCs can be extracted by reconstructing the EEG through back-projection of separate groups of ICs, based on a temporal and spectral analysis of ICs. The intracranial current sources related to the SCs were found to be spatially stable during the time evolution of the sleep spindles.

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