<p>Multi-modal signal measurement is widely used in health monitoring and disease recognition, such as electroencephalography (EEG)-functional magnetic resonance imaging (fMRI), EEG-functional near-infrared spectroscopy (fNIRS), EEG-electrocardiograph (ECG), etc. The concurrent recording of EEG–fNIRS not only measures concentration changes of oxyhemoglobin (HbO<sub>2</sub>) and deoxyhemoglobin (HbR) of the cortical microcirculation blood vessels but also records electrical activities of the cerebral cortex in a relatively portable setting compared with EEG-fMRI and other patterns. This protocol proposes a standard operating procedure for the joint EEG–fNIRS measurement, including electrode placement, hardware, and software. In the signal process, artifacts' removal, EEG–fNIRS phenotypes extraction by software and self-developed algorithm ways, and data storage are involved. In addition, solutions to problems that may be encountered in this protocol are also presented. This protocol is expected to broaden the application of joint EEG–fNIRS measurement and gain a deeper understanding of the underlying neural activation pattern and mechanism of the brain.</p>

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Protocol for Simultaneous EEG and fNIRS Measurement in Characterizing Brain State

  • Ligang Zhou,
  • Fang Xiao,
  • Ning Shen,
  • Yalin Wang,
  • Jinbu Tang,
  • Jingchun Luo,
  • Wei Chen,
  • Chen Chen

摘要

Multi-modal signal measurement is widely used in health monitoring and disease recognition, such as electroencephalography (EEG)-functional magnetic resonance imaging (fMRI), EEG-functional near-infrared spectroscopy (fNIRS), EEG-electrocardiograph (ECG), etc. The concurrent recording of EEG–fNIRS not only measures concentration changes of oxyhemoglobin (HbO2) and deoxyhemoglobin (HbR) of the cortical microcirculation blood vessels but also records electrical activities of the cerebral cortex in a relatively portable setting compared with EEG-fMRI and other patterns. This protocol proposes a standard operating procedure for the joint EEG–fNIRS measurement, including electrode placement, hardware, and software. In the signal process, artifacts' removal, EEG–fNIRS phenotypes extraction by software and self-developed algorithm ways, and data storage are involved. In addition, solutions to problems that may be encountered in this protocol are also presented. This protocol is expected to broaden the application of joint EEG–fNIRS measurement and gain a deeper understanding of the underlying neural activation pattern and mechanism of the brain.