Signal Processing in fNIRS: A Case for the Removal of Systemic Activity for Single Trial Data.

ABSTRACT: Researchers using functional near infrared spectroscopy (fNIRS) are increasingly aware of the problem that conventional filtering methods do not eliminate systemic noise at frequencies overlapping with the task frequency. This is a problem when signals are averaged for analysis, even more so when single trial data are used as in online neurofeedback or BCI applications where insufficiently preprocessed data means feeding back noise instead of brain activity or when looking for brain-behavior relationships on a trial-by-trial basis. For removing this task-related noise statistical approaches have been proposed. Yet as evidence is lacking on how these approaches perform on independent data, choosing one approach over another can be difficult. Here signal quality at the single trial level was

SUBMITTER: Klein F 

PROVIDER: S-EPMC6769087 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature