Signal regression in frequency-domain diffuse optical tomography to remove superficial signal contamination

Joshua Deepak Veesa, Hamid Dehghani

Research output: Contribution to journalArticlepeer-review

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Significance: Signal contamination is a major hurdle in functional near-infrared spectroscopy (fNIRS) of the human head, as the NIR signal is contaminated with the changes corresponding to superficial tissue and therefore occlude the functional information originating from the cerebral region. For Continuous Wave (CW), this is generally handled through linear regression of shortest source-detector distance intensity measurement from all the signals. Although phase measurements utilizing Frequency Domain (FD) provide deeper tissue sampling, the use of the shortest source-detector distance phase measurement for regression of superficial signal contamination can lead to misleading results, therefore suppressing cortical signals.

Aim: An approach for FD fNIRS is proposed which utilizes short-separation intensity signal directly to regress both intensity and phase measurements, providing a better regression of superficial signal contamination from both data-types.

Approach: Simulated data from realistic models of the human head are used and signal regression using both intensity and phase based components of the FD fNIRS have been evaluated.

Results: Intensity based phase regression achieves a suppression of superficial signal contamination by 68% whereas phase based phase regression only by 13%. Phase based phase regression is also shown to generate false-positive signals from the cortex which are not desirable.

Conclusions: Intensity based phase regression provides a better methodology for minimizing superficial signal contamination in FD fNIRS.
Original languageEnglish
Article number015013
Number of pages17
Issue number1
Early online date31 Mar 2021
Publication statusE-pub ahead of print - 31 Mar 2021

Bibliographical note

Acknowledgments: This project has received funding from the European Union's Horizon 2020 Marie Sklodowska-Curie Innovative Training Networks (ITN-ETN) programme, under grant agreement no. 675332, BitMap.


  • frequency domain
  • functional near-infrared imaging
  • high density diffuse optical tomography
  • signal regression
  • superficial signal contamination


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