Joint Blind Source Separation Method Research Articles

A Scalable Approach to Independent Vector Analysis by Shared Subspace Separation for Multi-Subject fMRI Analysis.

Joint blind source separation (JBSS) has wide applications in modeling latent structures across multiple related datasets. However, JBSS is computationally prohibitive with high-dimensional data, limiting the number of datasets that can be included in a tractable analysis. Furthermore, JBSS may not be effective if the data's true latent dimensionality is not adequately modeled, where severe overparameterization may lead to poor separation and time performance. In this paper, we propose a scalable JBSS method by modeling and separating the "shared" subspace from the data. The shared subspace is defined as the subset of latent sources that exists across all datasets, represented by groups of sources that collectively form a low-rank structure. Our method first provides the efficient initialization of the independent vector analysis (IVA) with a multivariate Gaussian source prior (IVA-G) specifically designed to estimate the shared sources. Estimated sources are then evaluated regarding whether they are shared, upon which further JBSS is applied separately to the shared and non-shared sources. This provides an effective means to reduce the dimensionality of the problem, improving analyses with larger numbers of datasets. We apply our method to resting-state fMRI datasets, demonstrating that our method can achieve an excellent estimation performance with significantly reduced computational costs.

Mainlobe jamming suppression for distributed radar via joint blind source separation

In the presence of the mainlobe jamming, the performance of the modern radar system would degrade significantly, i.e. increasing probabilities of false alarm and loss detection. In this study, the authors consider the jamming suppression problem in noise environment for the distributed radar with single transmitter and multiple receivers, where the multiple jammings enter into all the receivers through the main beam of the antennas. A framework based on joint blind source separation (JBSS) is proposed. First, the signal model accounting for both the target and jammings is developed. Second, the target and the jamming signals are separated by exploiting the generalised non-orthogonal joint diagonalisation JBSS method. Then, the separated target signals are employed to find the target location with the elliptic location method. Finally, they assess the performance in noise environment by numerical simulations, and the results show that the proposed algorithm can suppress successfully quite a lot different kinds of jamming signals, e.g. the noise-modulated and the digital radio-frequency memory jammings.

Underdetermined Joint Blind Source Separation of Multiple Datasets

In this paper, we tackle the problem of jointly separating instantaneous linear underdetermined mixtures of latent sources from multiple data sets, where the number of sources exceeds that of observations in each data set. Currently available blind source separation (BSS) methods, including joint BSS (JBSS) and underdetermined BSS (UBSS), cannot address this underdetermined problem effectively. We exploit the second-order statistics of observations, and present a novel BSS method, referred to as underdetermined joint BSS for multiple data sets (UJBSS-m), as a generalization of our previous work on two data sets. In this paper, the cross correlation between each pair of data sets is modeled by a third-order tensor in which a set of spatial covariance matrices corresponding to different time delays are stacked. Considering the latent common structure of these constructed tensors, the mixing matrices are jointly estimated via joint canonical polyadic decomposition of these specialized tensors. Furthermore, we recover the sources from each data set separately based on the estimated mixing matrices. Simulation results demonstrate that the proposed UJBSS-m method yields superior performances when compared with commonly used single-set UBSS and JBSS methods.

Double Coupled Canonical Polyadic Decomposition for Joint Blind Source Separation

Joint blind source separation (J-BSS) is an emerging data-driven technique for multi-set data-fusion. In this paper, J-BSS is addressed from a tensorial perspective. We show how, by using second-order multi-set statistics in J-BSS, a specific double coupled canonical polyadic decomposition (DC-CPD) problem can be formulated. We propose an algebraic DC-CPD algorithm based on a coupled rank-1 detection mapping. This algorithm converts a possibly underdetermined DC-CPD to a set of overdetermined CPDs. The latter can be solved algebraically via a generalized eigenvalue decomposition based scheme. Therefore, this algorithm is deterministic and returns the exact solution in the noiseless case. In the noisy case, it can be used to effectively initialize optimization based DC-CPD algorithms. In addition, we obtain the determini- stic and generic uniqueness conditions for DC-CPD, which are shown to be more relaxed than their CPD counterpart. Experiment results are given to illustrate the superiority of DC-CPD over standard CPD based BSS methods and several existing J-BSS methods, with regards to uniqueness and accuracy.

Video-based human heart rate measurement using joint blind source separation

Abstract Remote (non-contact) measurements of human cardiopulmonary physiological parameters based on photoplethysmography (PPG) can lead to efficient and comfortable medical assessment, which is important in human healthcare. It was shown that human facial blood volume variation during cardiac cycle can be indirectly captured by common Red–Green–Blue (RGB) cameras. In this paper, we show that it is promising to incorporate data from different facial sub-regions to improve remote measurement performance. We propose a novel method for non-contact video-based human heart rate (HR) measurement by exploring correlations among facial sub-regions via joint blind source separation (J-BSS). To our knowledge, this is the first time that J-BSS approaches, instead of prevailing BSS techniques such as independent component analysis (ICA), is successfully applied in non-contact physiological parameter measurement. We test the proposed method on a large public database, which provides the subjects’ left-thumb plethysmograph signals as ground truth. Experimental results show that the proposed J-BSS method outperforms previous ICA-based methodologies.

Joint Blind Source Separation for Neurophysiological Data Analysis: Multiset and multimodal methods

Conventional blind source separation (BSS) methods have become widely adopted tools for neurophysiological data analysis. However, the increasing availability of multiset and multimodal neurophysiological data has posed new challenges for BSS methods originally designed to analyze one data set at a time. Concomitantly, there is growing recognition that joint analysis of neurophysiological data has the potential to substantially enhance our understanding of brain function by extracting information from complementary modalities and synergistically combining the results. Therefore, joint data analysis methods represent both a challenge and an opportunity for the neurophysiological signal processing community that attempts to enhance understanding of normal brain function and the pathophysiology of many brain diseases. Over the past decade, various joint blind source separation (JBSS) methods have been proposed to simultaneously accommodate multiple data sets. In this article, we provide an overview and taxonomy of representative JBSS methods. We show, through illustrative numerical simulations, that different statistical assumptions and tradeoffs underlie different JBSS methods, affecting which method should be ideally chosen for a given application. We then discuss several real-world neurophysiological applications from both multiset and multimodal perspectives, highlighting the benefits of the JBSS methods as effective and promising tools for neurophysiological data analysis. Finally, we discuss remaining challenges for future JBSS development.

Underdetermined Joint Blind Source Separation for Two Datasets Based on Tensor Decomposition

In this letter, we aim to jointly separate the underdetermined mixtures of latent sources from two datasets, where the number of sources exceeds the number of observations in each dataset. Currently available blind source separation (BSS) methods, including joint blind source separation (JBSS) and underdetermined blind source separation (UBSS), cannot address this underdetermined problem effectively. We exploit the second-order statistics of observations and introduce a novel BSS method, termed as underdetermined joint blind source separation (UJBSS). Considering the dependence information between two datasets, the problem of jointly estimating the mixing matrices is tackled via canonical polyadic (CP) decomposition of a specialized tensor in which a set of spatial covariance matrices are stacked. Furthermore, the estimated mixing matrices are used to recover the sources from each dataset separately. Numerical results demonstrate the competitive performance of the proposed method when compared to a commonly used JBSS method, multiset canonical correlation analysis (MCCA), and the single-set UBSS method, UBSS with free active sources (UBSS-FAS).

Joint Blind Source Separation by Multiset Canonical Correlation Analysis

In this work, we introduce a simple and effective scheme to achieve joint blind source separation (BSS) of multiple datasets using multi-set canonical correlation analysis (M-CCA) [1]. We first propose a generative model of joint BSS based on the correlation of latent sources within and between datasets. We specify source separability conditions, and show that, when the conditions are satisfied, the group of corresponding sources from each dataset can be jointly extracted by M-CCA through maximization of correlation among the extracted sources. We compare source separation performance of the M-CCA scheme with other joint BSS methods and demonstrate the superior performance of the M-CCA scheme in achieving joint BSS for a large number of datasets, group of corresponding sources with heterogeneous correlation values, and complex-valued sources with circular and non-circular distributions. We apply M-CCA to analysis of functional magnetic resonance imaging (fMRI) data from multiple subjects and show its utility in estimating meaningful brain activations from a visuomotor task.