Toward Mobile Neuroimaging: Design of a Multi-Modal EEG/fNIRS Instrument for Real-Time Use

TL;DR

This paper introduces a portable, wireless EEG/fNIRS device for real-time brain monitoring, combining high signal quality with low power use for mobile neuroimaging.

Contribution

The novel contribution is a compact, multi-modal EEG/fNIRS system with real-time wireless data streaming and support for future on-device machine learning.

Findings

The device successfully demonstrated 32-channel synchronized EEG/fNIRS acquisition with stable optical and electrical signals.

Real-time data streaming via Wi-Fi was validated using Lab Streaming Layer for mobile phone and PC collection.

The system supports future embedded edge-machine-learning functions for signal quality assessment and artefact rejection.

Abstract

In this study, we present the design and development of a mobile, multi-modal electroencephalography and functional near-infrared spectroscopy (EEG/fNIRS) device for wireless neurophysiological monitoring. The system was engineered to achieve high signal fidelity, low power consumption, and a fully untethered operation suitable for ambulatory brain research. The device integrates four Texas Instruments ADS1299 24-bit biopotential amplifiers, providing up to 32 simultaneous acquisition channels. Signal control, processing, and local storage via an SD card are managed by an STM32H7 microcontroller, while an ESP32-S2 module handles Wi-Fi communication. Dual-wavelength light-emitting diodes and OPT101 photodiodes form the optical front-end, driven by digitally controlled constant-current sources for stable illumination. The design employs galvanic isolation, multi-rail power management, and a four-layer PCB layout to minimise interference between analogue, power, and digital domains. Data are captured by a deterministic, clock-driven STM32 acquisition loop and forwarded to the ESP32, which operates under an RTOS and streams packets over Wi-Fi for collection on a mobile phone or PC using the Lab Streaming Layer (LSL) framework. The STM32H7 architecture was chosen for its capability to support future embedded edge-machine-learning functions, enabling on-device signal quality assessment and artefact rejection. Validation demonstrations include 32-channel synchronised acquisition using the ADS1299 internal test signal, eyes-open/eyes-closed alpha modulation visualised in EEGLAB, a forehead fNIRS breath-hold response with physiological spectral content, and real-time ECG/optical pulse streaming via LSL. The resulting system provides a compact platform with explicitly defined acquisition and data interfaces for synchronised EEG/fNIRS acquisition, enabling scalable, low-cost mobile neuroimaging research.

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