Using the General Linear Model to Improve Performance in fNIRS Single Trial Analysis and Classification: A Perspective.

ABSTRACT: Within a decade, single trial analysis of functional Near Infrared Spectroscopy (fNIRS) signals has gained significant momentum, and fNIRS joined the set of modalities frequently used for active and passive Brain Computer Interfaces (BCI). A great variety of methods for feature extraction and classification have been explored using state-of-the-art Machine Learning methods. In contrast, signal preprocessing and cleaning pipelines for fNIRS often follow simple recipes and so far rarely incorporate the available state-of-the-art in adjacent fields. In neuroscience, where fMRI and fNIRS are established neuroimaging tools, evoked hemodynamic brain activity is typically estimated across multiple trials using a General Linear Model (GLM). With the help of the GLM, subject, channel, and task spec

SUBMITTER: von Luhmann A 

PROVIDER: S-EPMC7040364 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature