Recently, direction-of-arrival (DOA) and position estimation for acoustic signals have been studied intensively and many different algorithms have been proposed. Among different approaches for multiple sources, independent component analysis (ICA) based methods have drawn much attention. In this paper, we study the effects of permutation ambiguity, source scaling ambiguity and sensor gain misma...

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...

Principal Component Analysis (PCA) is a classical technique in statistical data analysis, feature extraction and data reduction, aiming at explaining observed signals as a linear combination of orthogonal principal components. Independent Component Analysis (ICA) is a technique of array processing and data analysis, aiming at recovering unobserved signals or 'sources' from observed mixtures, ex...

HiPerSAT, a C++ library and associated tools, processes large EEG data sets with statistical data whitening and ICA (Independent Component Analysis) methods. The library uses BLAS, LAPACK, MPI and OpenMP to achieve a high performance solution that exploits available parallel hardware. ICA is a class of methods for analyzing a large set of data samples and deducing the independent components res...

The detection of transient events related to slow earthquakes in GNSS positional time series is key understanding seismogenic processes subduction zones. Here, we present a novel Principal and Independent Components Correlation Analysis (PICCA) method that allows for the temporal spatial signals. PICCA based on an optimal combination principal (PCA) independent component analysis (ICA) network....

Electroencephalogram (EEG) is susceptible to various nonneural physiological artifacts. Automatic artifact removal from EEG data remains a key challenge for extracting relevant information from brain activities. To adapt to variable subjects and EEG acquisition environments, this paper presents an automatic online artifact removal method based on a priori artifact information. The combination o...

In this paper, we propose a new population optimization algorithm called Univariate Marginal Distribution Algorithm with Independent Component Analysis(UMDA/ICA). Our main idea is to incorporate ICA into UMDA algorithm in order to tackle the interrelations among variables. We demonstrate that UMDA/ICA performs better than UMDA for a test function with highly correlated variables.

Independent Component Analysis (ICA) is a technique that attempts to separate data into maximally independent groups. Achieving maximal independence in space or time yields two varieties of ICA meaningful for functional MRI (fMRI) applications: spatial-ICA (SICA) and temporal-ICA (TICA). SICA has so far dominated the application of ICA to fMRI. The objective of these experiments was to study IC...

The process of analysing unaveraged interictal epileptic magnetoencephalographic (MEG) data with Magnetic Field Tomography (MFT) is discusses focusing on three of its associated image processing issues are considered, namely, multimodality image registration, averaging of images corresponding to single epoch recordings, and independent component analysis applied on MFT image sequences. A robust...

We propose the kernel-based nonlinear independent component analysis (ICA) method, which consists of two separate steps. First, we map the data to a high-dimensional feature space and perform dimension reduction to extract the effective subspace, which was achieved by kernel principal component analysis (PCA) and can be considered as a pre-processing step. Second, we need to adjust a linear tra...