To study functional connectivity using magnetoencephalographic (MEG) data, the high-quality source-level reconstruction of brain activity constitutes a critical element. MEG resting-state networks (RSNs) have been documented by means of a dedicated processing pipeline: MEG recordings are decomposed by independent component analysis (ICA) into artifact and brain components (ICs); next, the chann...

Visual sensor networks (VSNs) that employ content-rich 2-D images or image sequences as the basic media have been evolving rapidly in recent years. Besides the critical resource constraints that are already inherent in any micro-sensor networks, the development of VSNs also faces challenges from device design, image transmission, and onboard image processing, among which efficient onboard proce...

The conventional detection process of direct sequence code division multiple access (DS-CDMA) is limited by multiple access interference (MAI) due to loss of orthogonality between spreading waveforms in multipath propagation channel environment.. In this paper RADICAL Independent Component Analysis (ICA) algorithm is proposed for detection of DS-CDMA signals and compared with FastICA, JADE ICA ...

Consider a situation in which we have a number of sources emitting signals which are interfering with one another. Familiar situations in which this occurs are a crowded room with many people speaking at the same time, interfering electromagnetic waves from mobile phones or crosstalk from brain waves originating from different areas of the brain. In each of these situations the mixed signals ar...

The famous example to illustrate ICA method is so-called cocktail party. Imagine two people are talking in a party and two different microphones are recording. Then the two records X1(t), X2(t) from those microphones are both the mixtures of the speech signal S1(t), S2(t) from the two speakers. Let us assume that only additive mixed effect exists, at this time, we can use the following equation...

We study independent component analysis with noisy observations. We present, for the first time in the literature, consistent, polynomial-time algorithms to recover non-Gaussian source signals and the mixing matrix with a reconstruction error that vanishes at a 1/ √ T rate using T observations and scales only polynomially with the natural parameters of the problem. Our algorithms and analysis a...

pervasive problem in neuroscience is A determining which regions of the brain are active, given voltage measurements at the scalp. If accurate solutions to such problems could be obtained, neurologists would gain noninvasive access to patient-specific cortical activity. Access to such data would ultimately increase the number of patients who could be effectively treated for neural pathologies s...

Delayed mixing is a problem of theoretical interest and practical importance, e.g., in speech processing, bio-medical signal analysis and nancial data modelling. Most previous analyses have been based on models with integer shifts, i.e., shifts by a number of samples, and have often been carried out using time-domain representation. Here, we explore the fact that a shift in the time domain corr...

Independent Component Analysis (ICA) (Comon, 1994) was originally proposed to solve the blind source separation problem of recovering independent source signals (e.g., different voice, music, or noise sources) after they are linearly mixed by an unknown matrix, A (cf. Figure 1). Nothing is known about the sources or the mixing process except that there are N different recorded mixtures. The tas...