Wearable, Integrated EEG–fNIRS Technologies: A Review

Abstract

There has been considerable interest in applying electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) simultaneously for multimodal assessment of brain function. EEG–fNIRS can provide a comprehensive picture of brain electrical and hemodynamic function and has been applied across various fields of brain science. The development of wearable, mechanically and electrically integrated EEG–fNIRS technology is a critical next step in the evolution of this field. A suitable system design could significantly increase the data/image quality, the wearability, patient/subject comfort, and capability for long-term monitoring. Here, we present a concise, yet comprehensive, review of the progress that has been made toward achieving a wearable, integrated EEG–fNIRS system. Significant marks of progress include the development of both discrete component-based and microchip-based EEG–fNIRS technologies; modular systems; miniaturized, lightweight form factors; wireless capabilities; and shared analogue-to-digital converter (ADC) architecture between fNIRS and EEG data acquisitions. In describing the attributes, advantages, and disadvantages of current technologies, this review aims to provide a roadmap toward the next generation of wearable, integrated EEG–fNIRS systems.

Intelligent Classification Technique of Hand Motor Imagery Using EEG Beta Rebound Follow-Up Pattern

“…Other modalities have been explored, e.g., functional magnetic resonance imaging (fMRI) [ 37 ], functional near-infrared spectroscopy (fNIRS) [ 38 ], magnetoencephalography (MEG) [ 39 ], and electrical impedance tomography (EIT) [ 40 ]. Due to portability, non-invasiveness, and cost-effectiveness, EEG and fNIRS have an advantage in natural environment applications [ 41 ]. In terms of classification accuracy, EEG-based BMI outperforms fNIRS-based BMI [ 24 ].…”

Brain–Computer Interfacing Using Functional Near-Infrared Spectroscopy (fNIRS)

“…Currently, the machines are not reliable enough to be used for real applications. Current research is looking at dual fNIRS and EEG in data acquisition for BCI [65]. It has been reported that these hybrid systems are more accurate than EEG alone or fNIRS alone.…”

Development of an Integrated EEG/fNIRS Brain Function Monitoring System

“…As the proposed system combines two electrically separate systems (EEG and fNIRS), electrical crosstalk between these systems may occur due to field coupling between the switching current in the fNIRS optode wires and the EEG electrodes [5,28,29]. To assess the impact of the LED switching current in EEG, a resistive network phantom with electrodes positioned according to 10-20 system positions was used following the method of a study carried out in 2017 by von Lühmann, et al [28], which specified the phantom details and the experimental conditions that should be followed to assess the crosstalk in EEG in hybrid systems.…”

“…The main components of fNIRS systems are sources (such as LEDs, laser diodes, and VCSELs) with a driving circuit, detectors such as photodiodes (PDs) and silicon photomultipliers (SiMP) for detecting light, amplifiers, filters to reduce noise, and a recording system to display and store the fNIRS signal. The advantages of EEG and fNIRS make them the most suitable techniques for multimodal studies [5].…”