Emerging Neuroimaging Approach of Hybrid EEG-fNIRS Recordings: Data Collection and Analysis Challenges

Marco Antonio Pinto-Orellana, Haroon Khan, Hernando Ombao*, Peyman Mirtaheri*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

The hybrid EEG-fNIRS (electroencephalogram - functional near-infrared spectroscopy) modality provides a comprehensive understanding of brain activity by simultaneously capturing electrical and hemodynamic responses. It takes advantage of the temporal resolution in EEG with the good spatial resolution of fNIRS. This hybrid system is non-invasive, portable, and relatively affordable compared to functional magnetic resonance imaging (fMRI). Despite its inherent limitations, EEG-fNIRS systems can be a valuable tool in neuroimaging, suitable for cognitive and clinical research that requires temporal and spatial information about brain function. They are well-suited in naturalistic experimental settings, brain-computer interface applications, and disease diagnosis. This paper also explores electro-metabolic interactions in the motor cortex during left- and right-hand grip tasks. We analyzed cross-frequency coupling (CFC) between delta rhythms (0–4Hz) and oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) concentrations calculated from fNIRS. CFC was estimated as the correlation between HbO, HbR, and the instantaneous amplitude of delta signals. CFC cannot be studied with non-synchronous measurements and is not directly related to activation amplitudes. Based on previous fMRI-EEG studies, some categories of oscillations denote a negligible correlation with fNIRS signals. However, our results showed a contra-lateral coupling between delta-EEG and HbO with significant pattern differences in the left motor cortex.

Original languageEnglish (US)
Article number2426785
JournalData Science in Science
Volume3
Issue number1
DOIs
StatePublished - 2024

Keywords

  • cross-frequency coupling
  • data pre-processing
  • electrical-hemodynamic coupling
  • Hybrid EEG-fNIRS
  • statistical modeling
  • synchronization

ASJC Scopus subject areas

  • Computational Mathematics
  • Statistics and Probability

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