Parallel factor analysis for multidimensional decomposition of functional near-infrared spectroscopy data.

ABSTRACT:

Significance

Current techniques for data analysis in functional near-infrared spectroscopy (fNIRS), such as artifact correction, do not allow to integrate the information originating from both wavelengths, considering only temporal and spatial dimensions of the signal's structure. Parallel factor analysis (PARAFAC) has previously been validated as a multidimensional decomposition technique in other neuroimaging fields.

Aim

We aimed to introduce and validate the use of PARAFAC for the analysis of fNIRS data, which is inherently multidimensional (time, space, and wavelength).

Approach

We used data acquired in 17 healthy adults during a verbal fluency task to compare the efficacy of PARAFAC for motion artifact correction to traditional two-dimensional decomposition techn

SUBMITTER: Husser A 

PROVIDER: S-EPMC9665873 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature