EEG signals may be affected by physiological and non-physiological artifacts hindering the analysis of brain activity. Blind source separation methods such as independent component (ICA) are effective ways improving signal quality removing components representing non-brain However, most ICA-based artifact removal strategies have limitations, individual differences in visual assessment component...

Principal component analysis (PCA) and independent component analysis (ICA) are both based on a linear model of multivariate data. They are often seen as complementary tools, PCA providing dimension reduction and ICA separating underlying components or sources. In practice, a two-stage approach is often followed, where first PCA and then ICA is applied. Here, we show how PCA and ICA can be seen...

We propose a new Single-Input Multiple-Output (SIMO)-modelbased ICA with information-geometric learning algorithm for highfidelity blind source separation. The SIMO-ICA consists of multiple ICAs and a fidelity controller, and each ICA runs in parallel under the fidelity control of the entire separation system. The SIMOICA can separate the mixed signals, not into monaural source signals but into...

In recent years, there has been an increasing interest in developing new algorithms for digital signal processing by applying and generalising existing numerical linear algebra tools. A recent result shows that the FastICA algorithm, a popular state-of-the-art method for linear Independent Component Analysis (ICA), shares a nice interpretation as a Newton type method with the Rayleigh Quotient ...

Independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) data reveals spatially independent patterns of functional activation. The purely datadriven approach of ICA makes statistical inference difficult. The purpose of this study was to develop a hybrid ICA in the frequency domain that enables statistical inference while preserving advantages of a data-driven ICA. Th...

This paper compares principal component analysis (PCA) and independent component analysis (ICA) in the context of a baseline face recognition system, a comparison motivated by contradictory claims in the literature. This paper shows how the relative performance of PCA and ICA depends on the task statement, the ICA architecture, the ICA algorithm, and (for PCA) the subspace distance metric. It t...

Internal carotid artery (ICA) stenosis often presents bilaterally and is a major cause of stroke. Carotid duplex ultrasound used for estimation via peak systolic velocity (PSV), end diastolic (EDV), ICA/common (CCA) ratio. endarterectomy (CEA) common surgery ICA carries high risk stroke mortality. In near occlusion with >90% stenosis, collateral flow through contralateral can compensate increas...

We propose a novel method using a perfectly local facial representation based on ICA. We named our method “LS-ICA method”. In the LS-ICA method, basis images are made from their corresponding ICA basis images simply by removing non-salient regions. The LS-ICA basis images display perfectly local characteristics because they contain only locally salient feature regions. This enables us to effect...

BACKGROUND The kinking of the internal carotid artery (ICA) after final closure in carotid endarterectomy (CEA) is thought to be uncommon. When it occurs, it is mandatory to reconstruct ICA to preserve normal blood flow. We herein present a case in which a fixation technique was applied to repair an ICA that became kinked during CEA. CASE DESCRIPTION A 68-year-old man presented with cerebral ...