A Novel Algorithm for Independent Component Analysis with Reference and Methods for Its Applications

Abstract

This paper presents a stable and fast algorithm for independent component analysis with reference (ICA-R). This is a technique for incorporating available reference signals into the ICA contrast function so as to form an augmented Lagrangian function under the framework of constrained ICA (cICA). The previous ICA-R algorithm was constructed by solving the optimization problem via a Newton-like learning style. Unfortunately, the slow convergence and potential misconvergence limit the capability of ICA-R. This paper first investigates and probes the flaws of the previous algorithm and then introduces a new stable algorithm with a faster convergence speed. There are two other highlights in this paper: first, new approaches, including the reference deflation technique and a direct way of obtaining references, are introduced to facilitate the application of ICA-R; second, a new method is proposed that the new ICA-R is used to recover the complete underlying sources with new advantages compared with other classical ICA methods. Finally, the experiments on both synthetic and real-world data verify the better performance of the new algorithm over both previous ICA-R and other well-known methods.

Investigation Of Interaction Between Physiological Signals And fMRI Dynamic Functional Connectivity Using Independent Component Analysis

“…To do this, the spatial map, time course and power spectrum of the time course for each component were used. Subsequently, ICA-AROMA, which is a robust ICA-based strategy for removing motion artifacts from fMRI data was used [24].…”

A Penalized Semialgebraic Deflation ICA Algorithm for the Efficient Extraction of Interictal Epileptic Signals

“…This requires the exploitation of prior knowledge about the signals of interest. In [17], the constrained ICA (cICA) framework has been developped to this end and ICA methods that work with a reference signal, generally referred to as ICA-R, have been put forward [17], [18], [19] to extract the signals with the highest resemblance to the references. These methods are based on a Newton-like learning scheme to solve the constrained optimization problem.…”

A Fetal Electrocardiogram Signal Extraction Algorithm Based on Fast One-Unit Independent Component Analysis with Reference

“…Rodriguez et al [ 24 ] proposed a multiunit ICA-R method based on nonorthogonal decoupling of separated matrix. This method cannot be extended to one-unit ICA-R. Mi [ 25 ] proposed a strategy to detect future misconvergence to improve the probability of extracting the desired signal. Chen et al [ 26 ] proposed an ICA-R that can be used in single channel by discrete wavelet transform.…”

“…In Section 4 , we simplify the non-Gaussian measurement function based on the analysis in Section 3 and, then, derive an improved one-unit ICA-R with lower computation complexity. In Section 5 , we test Mi's method [ 25 ] and our proposed method on artificial ECG data and real-world ECG data to compare the performance of each method. Some conclusions are included in Section 6 .…”