5 accepted papers
Identification of subgroups of subjects homogeneous functional networks is a key step for precision medicine. Independent vector analysis (IVA) is shown to be effective for this task, however, it has a substantial computing cost. We propose a constrained independent component analysis algorithm base…
Independent component analysis (ICA) of multi-subject functional magnetic resonance imaging (fMRI) data has proven useful in providing a fully multivariate summary that can be used for multiple purposes. ICA can identify patterns that can discriminate between healthy controls (HC) and patients with…
Identification of homogeneous subgroups of subjects plays a key role in the study of precision medicine. While there are a number of approaches based on the clustering of low-level features such as behavioral variables, work that makes use of fully multivariate nature of medical imaging data is very…
In this paper, we are interested in optimal sensor placement for signal extraction. Recently, a new criterion based on output signal to noise ratio has been proposed for sensor placement. However, to solve the optimization problem, a greedy approach is used over a grid, which is not optimal. To impr…
This paper focuses on the optimal sensor placement problem with the purpose of signal extraction in an underdetermined noisy setting. Assuming prior information on the spatial gain of the measured signal and on the spatial noise correlation, we propose a sensor placement criterion based on the maxim…