title: Separating heart and brain: on the reduction of physiological noise from multichannel functional near-infrared spectroscopy (fNIRS) signals.
abstract: OBJECTIVE
  Functional near-infrared spectroscopy (fNIRS) is an emerging technique for the in vivo assessment of functional activity of the cerebral cortex as well as in the field of brain-computer interface (BCI) research. A common challenge for the utilization of fNIRS in these areas is a stable and reliable investigation of the spatio-temporal hemodynamic patterns. However, the recorded patterns may be influenced and superimposed by signals generated from physiological processes, resulting in an inaccurate estimation of the cortical activity. Up to now only a few studies have investigated these influences, and still less has been attempted to remove/reduce these influences. The present study aims to gain insights into the reduction of physiological rhythms in hemodynamic signals (oxygenated hemoglobin (oxy-Hb), deoxygenated hemoglobin (deoxy-Hb)).
  
  
  METHODS
  We introduce the use of three different signal processing approaches (spatial filtering, a common average reference (CAR) method; independent component analysis (ICA); and transfer function (TF) models) to reduce the influence of respiratory and blood pressure (BP) rhythms on the hemodynamic responses.
  
  
  RESULTS
  All approaches produce large reductions in BP and respiration influences on the oxy-Hb signals and, therefore, improve the contrast-to-noise ratio (CNR). In contrast, for deoxy-Hb signals CAR and ICA did not improve the CNR. However, for the TF approach, a CNR-improvement in deoxy-Hb can also be found.
  
  
  CONCLUSIONS
  The present study investigates the application of different signal processing approaches to reduce the influences of physiological rhythms on the hemodynamic responses. In addition to the identification of the best signal processing method, we also show the importance of noise reduction in fNIRS data.
authors.affiliation: Institute for Knowledge Discovery, Graz University of Technology, A-8010 Graz, Austria.
authors.forename: G
authors.initials: G
authors.lastname: Bauernfeind
authors.forename: S C
authors.initials: SC
authors.lastname: Wriessnegger
authors.forename: I
authors.initials: I
authors.lastname: Daly
authors.forename: G R
authors.initials: GR
authors.lastname: Müller-Putz
mesh_terms: D000328:Adult; D000465:Algorithms; D016477:Artifacts; D001931:Brain Mapping / Q000379:methods; D019054:Evoked Potentials, Motor / Q000502:physiology*; D005260:Female; D006801:Humans; D008297:Male; D009044:Motor Cortex / Q000502:physiology*; D009068:Movement / Q000502:physiology*; D010100:Oxygen / Q000097:blood*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D059629:Signal-To-Noise Ratio; D019265:Spectroscopy, Near-Infrared / Q000379:methods*; D055815:Young Adult
chemical_list: D010100:Oxygen