Improved physiological noise regression in fNIRS: A multimodal extension of the General Linear Model using temporally embedded Canonical Correlation Analysis

Alexander von Lühmann, Xinge Li, Klaus Robert Müller, David A. Boas, Meryem A. Yücel

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59 Citations (Scopus)


For the robust estimation of evoked brain activity from functional Near-Infrared Spectroscopy (fNIRS) signals, it is crucial to reduce nuisance signals from systemic physiology and motion. The current best practice incorporates short-separation (SS) fNIRS measurements as regressors in a General Linear Model (GLM). However, several challenging signal characteristics such as non-instantaneous and non-constant coupling are not yet addressed by this approach and additional auxiliary signals are not optimally exploited. We have recently introduced a new methodological framework for the unsupervised multivariate analysis of fNIRS signals using Blind Source Separation (BSS) methods. Building onto the framework, in this manuscript we show how to incorporate the advantages of regularized temporally embedded Canonical Correlation Analysis (tCCA) into the supervised GLM. This approach allows flexible integration of any number of auxiliary modalities and signals. We provide guidance for the selection of optimal parameters and auxiliary signals for the proposed GLM extension. Its performance in the recovery of evoked HRFs is then evaluated using both simulated ground truth data and real experimental data and compared with the GLM with short-separation regression. Our results show that the GLM with tCCA significantly improves upon the current best practice, yielding significantly better results across all applied metrics: Correlation (HbO max. +45%), Root Mean Squared Error (HbO max. −55%), F-Score (HbO up to 3.25-fold) and p-value as well as power spectral density of the noise floor. The proposed method can be incorporated into the GLM in an easily applicable way that flexibly combines any available auxiliary signals into optimal nuisance regressors. This work has potential significance both for conventional neuroscientific fNIRS experiments as well as for emerging applications of fNIRS in everyday environments, medicine and BCI, where high Contrast to Noise Ratio is of importance for single trial analysis.

Original languageEnglish
Article number116472
Publication statusPublished - 2020 Mar

Bibliographical note

Funding Information:
This work was funded by a research contract under Facebook’s Sponsored Academic Research Agreement and in part by NIH R24NS104096. K.-R.M.’s work was supported by the German Ministry for Education and Research (BMBF) under Grants 01IS14013A-E , 01GQ1115 and 01GQ0850 ; the German Research Foundation (DFG) under Grant Math+, EXC 2046/1 , Project ID 390685689 and by the Institute for Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (No. 2017-0-00451 ).

Publisher Copyright:
© 2019


  • Canonical correlation analysis
  • Functional near-infrared spectroscopy
  • General linear model
  • Multimodality
  • Physiological noise/nuisance regression
  • Temporal embedding

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience


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