Independent component analysis (ICA) has become an increasingly utilized approach for analyzing brain imaging data. In contrast to the widely used general linear model (GLM) that requires the user to parameterize the data (e.g. the brain's response to stimuli), ICA, by relying upon a general assumption of independence, allows the user to be agnostic regarding the exact form of the response. In ...

BACKGROUND Although extracranial internal carotid artery (e-ICA) occlusion is a common pathology in patients undergoing intravenous thrombolysis for treatment of acute ischemic stroke, no data on e-ICA recanalization rate or potential effects on outcome are yet available. METHODS AND RESULTS This study included 52 consecutive patients with e-ICA occlusion and ischemic stroke undergoing standa...

BACKGROUND AND PURPOSE Internal carotid artery (ICA) aneurysms may present with cranial nerve dysfunction. Therapeutic ICA occlusion, when tolerated, is an effective treatment resulting in improvement or cure of symptoms in most patients. When ICA occlusion is not tolerated, selective endovascular aneurysm occlusion can be considered. We compare recovery of cranial nerve dysfunction in patients...

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...