Next Article in Journal
Inertial Sensor-Based Quantification of Movement Symmetry in Trotting Warmblood Show-Jumping Horses after “Limb-by-Limb” Re-Shoeing of Forelimbs with Rolled Rocker Shoes
Previous Article in Journal
Comparison of KF-Based Vehicle Sideslip Estimation Logics with Increasing Complexity for a Passenger Car
Previous Article in Special Issue
Non-Invasive Blood Pressure Sensing via Machine Learning
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Hybrid Integrated Wearable Patch for Brain EEG-fNIRS Monitoring

Key Laboratory of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang, College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Sensors 2024, 24(15), 4847; https://doi.org/10.3390/s24154847
Submission received: 8 May 2024 / Revised: 25 May 2024 / Accepted: 23 July 2024 / Published: 25 July 2024
(This article belongs to the Special Issue Sensors for Physiological Monitoring and Digital Health)

Abstract

Synchronous monitoring electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) have received significant attention in brain science research for their provision of more information on neuro-loop interactions. There is a need for an integrated hybrid EEG-fNIRS patch to synchronously monitor surface EEG and deep brain fNIRS signals. Here, we developed a hybrid EEG-fNIRS patch capable of acquiring high-quality, co-located EEG and fNIRS signals. This patch is wearable and provides easy cognition and emotion detection, while reducing the spatial interference and signal crosstalk by integration, which leads to high spatial–temporal correspondence and signal quality. The modular design of the EEG-fNIRS acquisition unit and optimized mechanical design enables the patch to obtain EEG and fNIRS signals at the same location and eliminates spatial interference. The EEG pre-amplifier on the electrode side effectively improves the acquisition of weak EEG signals and significantly reduces input noise to 0.9 μVrms, amplitude distortion to less than 2%, and frequency distortion to less than 1%. Detrending, motion correction algorithms, and band-pass filtering were used to remove physiological noise, baseline drift, and motion artifacts from the fNIRS signal. A high fNIRS source switching frequency configuration above 100 Hz improves crosstalk suppression between fNIRS and EEG signals. The Stroop task was carried out to verify its performance; the patch can acquire event-related potentials and hemodynamic information associated with cognition in the prefrontal area.
Keywords: co-located; EEG-fNIRS; noise suppression; crosstalk suppression; acquisition module design; acquisition module mechanical design co-located; EEG-fNIRS; noise suppression; crosstalk suppression; acquisition module design; acquisition module mechanical design

Share and Cite

MDPI and ACS Style

Li, B.; Li, M.; Xia, J.; Jin, H.; Dong, S.; Luo, J. Hybrid Integrated Wearable Patch for Brain EEG-fNIRS Monitoring. Sensors 2024, 24, 4847. https://doi.org/10.3390/s24154847

AMA Style

Li B, Li M, Xia J, Jin H, Dong S, Luo J. Hybrid Integrated Wearable Patch for Brain EEG-fNIRS Monitoring. Sensors. 2024; 24(15):4847. https://doi.org/10.3390/s24154847

Chicago/Turabian Style

Li, Boyu, Mingjie Li, Jie Xia, Hao Jin, Shurong Dong, and Jikui Luo. 2024. "Hybrid Integrated Wearable Patch for Brain EEG-fNIRS Monitoring" Sensors 24, no. 15: 4847. https://doi.org/10.3390/s24154847

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop