Enhancing ICA Performance by Exploiting Sparsity: Application to FMRI Analysis

TL;DR

This paper introduces a new ICA variant called SparseICA-EBM that leverages data sparsity to improve source separation, especially in fMRI analysis, by relaxing the independence assumption.

Contribution

The paper proposes a novel, parameter-free ICA algorithm that directly exploits sparsity, enhancing separation performance over traditional methods.

Findings

SparseICA-EBM outperforms standard ICA in synthetic data tests.

Improves source separation in fMRI data analysis.

Demonstrates synergy between independence and sparsity.

Abstract

Independent component analysis (ICA) is a powerful method for blind source separation based on the assumption that sources are statistically independent. Though ICA has proven useful and has been employed in many applications, complete statistical independence can be too restrictive an assumption in practice. Additionally, important prior information about the data, such as sparsity, is usually available. Sparsity is a natural property of the data, a form of diversity, which, if incorporated into the ICA model, can relax the independence assumption, resulting in an improvement in the overall separation performance. In this work, we propose a new variant of ICA by entropy bound minimization (ICA-EBM)-a flexible, yet parameter-free algorithm-through the direct exploitation of sparsity. Using this new SparseICA-EBM algorithm, we study the synergy of independence and sparsity through simulations on synthetic as well as functional magnetic resonance imaging (fMRI)-like data.