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.

Keywords: EEG; diffuse optical tomography; fNIRS; integrated; multimodal; wearable.

Figure 3

(a) The wet AgCl ring electrodes used in the system developed by von Luhmann et al. This figure was taken with permission from [40]. (b) The custom-designed prototype of spring-loaded dry electrodes developed by Lee at al. Each electrode contains 18 spring-loaded probes. This figure was taken with permission from [43]. (c) A commercial dry electrode used in a single-modal EEG, described by Kam et al. This figure was taken with permission from [59]. (d–f) Examples of commercial dry fingered EEG electrodes: (d) wearable sensing [64], (e) CGX [65], (f) neuroelectrics [66]. These figures were taken with permission from [64], [65], and [66], respectively.

Figure 1

Examples of discrete components-based, integrated EEG–fNIRS systems. (a) The wireless, integrated EEG–fNIRS system described by Sawan et al. It consists of a helmet to house the front-end optical and electrical components, a distant control unit for system control and data transmission, and cabling. This figure was taken and modified with permission from [39]. (b) The system with 128 fNIRS channels (32 sources, 32 detectors) and 32 EEG channels described by Kassab et al. Extensive cabling was utilised in this multichannel system. This figure was taken with permission from [42]. (c) The modular, integrated EEG–fNIRS system described by von Luhmann et al. It is comprised of three individual modules, producing 13 fNIRS and 8 EEG channels. This figure was taken with permission from [40]. (d) The dry electrode-based, integrated EEG–fNIRS system described by Lee et al., consisting of a cap to position eight optodes (2 sources and 6 detectors), two custom-designed control boards, and cabling. This figure was taken with permission from [43].

Figure 2

Examples of microchip-based technologies. (a) The CMOS-based integrated EEG–fNIRS system developed by Chua et al. This system uses a bendable PCB to house 6 dual-wavelength LEDs and 12 detectors. This figure was taken with permission from [44]. (b) The SoC-based EEG–fNIRS ear-module system. The SoC, source, and electrode are embedded in an earpiece while the main photodiode, battery, and Bluetooth module are located at an ear hook. This figure was taken with permission from [45]. (c) The integrated EEG–fNIRS system for anaesthesia depth monitoring developed by Ha et al. The SoC with Bluetooth module and battery are embedded on a flexible PCB section and a polyethylene terephthalate (PET) film is fabricated to house the sources, detectors, EEG electrodes, and accessory components. This figure was taken and modified with permission from [46]. (d) The CMOS-based integrated multimodal EEG–fNIRS–EIT systems developed by Xu et al. A dual-wavelength LED is employed as an optical source, and a SiPM is utilised as an optical detector. This figure was taken with permission from [47].