NiReject: toward automated bad channel detection in functional near-infrared spectroscopy.

ABSTRACT:

Significance

The increasing sample sizes and channel densities in functional near-infrared spectroscopy (fNIRS) necessitate precise and scalable identification of signals that do not permit reliable analysis to exclude them. Despite the relevance of detecting these "bad channels," little is known about the behavior of fNIRS detection methods, and the potential of unsupervised and semi-supervised machine learning remains unexplored.

Aim

We developed three novel machine learning-based detectors, unsupervised, semi-supervised, and hybrid NiReject, and compared them with existing approaches.

Approach

We conducted a systematic literature search and demonstrated the influence of bad channel detection. Based on 29,924 signals from two independently rated datasets and a simul

SUBMITTER: Gerloff C 

PROVIDER: S-EPMC11532795 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature