Concurrent fNIRS and EEG for Brain Function Investigation: A Systematic, Methodology-Focused Review

Abstract

Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) stand as state-of-the-art techniques for non-invasive functional neuroimaging. On a unimodal basis, EEG has poor spatial resolution while presenting high temporal resolution. In contrast, fNIRS offers better spatial resolution, though it is constrained by its poor temporal resolution. One important merit shared by the EEG and fNIRS is that both modalities have favorable portability and could be integrated into a compatible experimental setup, providing a compelling ground for the development of a multimodal fNIRS-EEG integration analysis approach. Despite a growing number of studies using concurrent fNIRS-EEG designs reported in recent years, the methodological reference of past studies remains unclear. To fill this knowledge gap, this review critically summarizes the status of analysis methods currently used in concurrent fNIRS-EEG studies, providing an up-to-date overview and guideline for future projects to conduct concurrent fNIRS-EEG studies. A literature search was conducted using PubMed and Web of Science through 31 August 2021. After screening and qualification assessment, 92 studies involving concurrent fNIRS-EEG data recordings and analyses were included in the final methodological review. Specifically, three methodological categories of concurrent fNIRS-EEG data analyses, including EEG-informed fNIRS analyses, fNIRS-informed EEG analyses, and parallel fNIRS-EEG analyses, were identified and explained with detailed description. Finally, we highlighted current challenges and potential directions in concurrent fNIRS-EEG data analyses in future research.

Keywords: EEG; concurrent recording; functional NIRS; integrated analysis; multimodal neuroimaging.

Figure 1

Schematic demonstration: (A) fNIRS and (B) EEG measurement.

Figure 2

Demonstration of neurovascular coupling.

Figure 3

PRISMA flow diagram for the literature review and article selection.

Figure 4

Literature summary of concurrent EEG–fNIRS studies: (A) Yearly publications from 2012 to 2021 and (B) distribution of each type of concurrent fNIRS-EEG studies.

Figure 5

Basic preprocessing pipeline: (A) fNIRS raw data and (B) EEG raw data.

Figure 6

Basic principle of general linear model (GLM) in fNIRS analysis.

Figure 7

The conventional schematic of EEG-informed fNIRS GLM analysis framework.

Figure 8

Basic concepts of EEG source imaging and traditional pipeline of fNIRS-informed EEG source imaging analysis (adapted with permission from Ref. [27]. 2019, Li et al.