journal article·2026-07

A Wearable Prefrontal Headband Measuring Cerebral Hemoglobin Concentration for Brain‐Computer Interfaces

Feiyang Zhang, Ying Sun, Shuailei Zhang, Dequan Fang, Kang Ma, Huaizhi Tang, Shiji Yuan, Qiaoyi Wang, Xiao Liang, Hao Zhang, Rui Na, Shangchun Fan, La Li, Dezhi Zheng, Guozhen Shen

Advanced Materials Technologies· Volume 11· Issue 14
Publisher page

Abstract

ABSTRACT Non‐invasive brain‐computer interfaces (BCIs) that monitor brain activity via hemodynamic signals enable motor and language rehabilitation for individuals with disabilities and provide multiple communication capabilities, such as sleep monitoring and fatigue alerts, for healthy individuals. Here, we propose a wearable prefrontal headband with an embedded array of 4 photodetectors and 4 micro‐LEDs to form a fiberless functional near‐infrared spectroscopy (fNIRS) system measuring the change in oxyhemoglobin and deoxyhemoglobin concentrations, thus indirectly reflecting the cognitive functions of the brain and constructing BCI. The utilization of dual short‐channel measurements and long‐short channel multiplexing in the fNIRS system enables all 10 optodes to contribute to both long‐ and short‐channel acquisitions, achieving a long‐short‐channel optode proportion of 100% with improvement in removal of superficial physiological noise. The average signal‐to‐noise ratio of long channels reaches 64 dB, and the minimum dynamic optical range of short channels is 122 dB due to the time‐division measurement scheme for two types of channels, demonstrating the feasibility of the proposed channel configuration. In simulated driving tasks, the headband captured discriminable physiological features to distinguish mental‐fatigue states, indicating its potential for real‐world BCI applications.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Optical Imaging and Spectroscopy Techniques · EEG and Brain-Computer Interfaces · Non-Invasive Vital Sign Monitoring

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

School of Instrumentation and Optoelectronic Engineering Beihang University Beijing China

School of Interdisciplinary Sciences Beijing Institute of Technology Beijing China

College of Computing and Data Science Nanyang Technological University Singapore Singapore

School of Interdisciplinary Sciences Beijing Institute of Technology China & Yangtze Delta Region Academy of Beijing Institute of Technology Jiaxing China

School of Integrated Circuits and Electronics Beijing Institute of Technology Beijing China

MIIT Key Laboratory of Complex‐Field Intelligent Sensing Beijing Institute of Technology State Key Laboratory of Environment Characteristics and Effects for Near‐Space Beijing Institute of Technology Beijing China

Funding

National Natural Science Foundation of China · Natural Science Foundation of Beijing Municipality

References (42)

Show source-provided references
1. 10.1016/j.scib.2025.12.0410.1016/j.scib.2025.12.04 ↗
2. 10.1109/RBME.2024.344979010.1109/RBME.2024.3449790 ↗
3. 10.1109/TNSRE.2022.320915510.1109/TNSRE.2022.3209155 ↗
4. 10.1038/s41398‐025‐03224‐710.1038/s41398‐025‐03224‐7 ↗
5. 10.1038/s41467‐023‐39814‐610.1038/s41467‐023‐39814‐6 ↗
6. 10.1038/s41539-024-00216-y10.1038/s41539-024-00216-y ↗
7. 10.1109/JIOT.2021.311391010.1109/JIOT.2021.3113910 ↗
8. 10.1109/TITS.2022.318934610.1109/TITS.2022.3189346 ↗
9. 10.1038/s41467‐024‐48682‐710.1038/s41467‐024‐48682‐7 ↗
10. 10.1126/sciadv.abo320910.1126/sciadv.abo3209 ↗
11. 10.1038/s41928‐024‐01161‐910.1038/s41928‐024‐01161‐9 ↗
12. 10.1109/TITS.2023.334851710.1109/TITS.2023.3348517 ↗
13. 10.1109/JBHI.2023.324089110.1109/JBHI.2023.3240891 ↗
14. 10.1098/rstb.2023.008710.1098/rstb.2023.0087 ↗
15. 10.4103/1673‐5374.38797010.4103/1673‐5374.387970 ↗
16. 10.1213/ANE.000000000000689110.1213/ANE.0000000000006891 ↗
17. 10.1109/TIM.2025.355238710.1109/TIM.2025.3552387 ↗
18. 10.1117/1.NPh.4.4.04141310.1117/1.NPh.4.4.041413 ↗
19. 10.1117/1.NPh.4.3.03500210.1117/1.NPh.4.3.035002 ↗
20. 10.3390/s2122770310.3390/s21227703 ↗
21. 10.1117/1.NPh.11.3.03500210.1117/1.NPh.11.3.035002 ↗
22. 10.1109/TBCAS.2022.314976610.1109/TBCAS.2022.3149766 ↗
23. 10.1117/1.NPh.2.2.02500510.1117/1.NPh.2.2.025005 ↗
24. 10.1371/journal.pone.006631910.1371/journal.pone.0066319 ↗
25. 10.3390/bios1110038910.3390/bios11100389 ↗
26. 10.1117/1.NPh.7.3.03500910.1117/1.NPh.7.3.035009 ↗
27. 10.1117/1.NPh.11.4.04500410.1117/1.NPh.11.4.045004 ↗
28. 10.1117/1.NPh.10.1.01350310.1117/1.NPh.10.1.013503 ↗
29. 10.1117/1.NPh.9.1.01500410.1117/1.NPh.9.1.015004 ↗
30. 10.1117/1.NPh.7.3.03501110.1117/1.NPh.7.3.035011 ↗
31. 10.1016/j.neuroimage.2013.01.07310.1016/j.neuroimage.2013.01.073 ↗
32. 10.1002/adfm.20242558610.1002/adfm.202425586 ↗
33. 10.1016/j.matt.2024.06.02410.1016/j.matt.2024.06.024 ↗
34. 10.1364/JOSAA.22.00187410.1364/JOSAA.22.001874 ↗
35. 10.1117/1.JBO.29.11.11700110.1117/1.JBO.29.11.117001 ↗
36. 10.1038/s41598‐023‐31264‐w10.1038/s41598‐023‐31264‐w ↗
37. 10.1109/TITS.2018.288996210.1109/TITS.2018.2889962 ↗
38. 10.1111/psyp.1474710.1111/psyp.14747 ↗
39. X.Jiao J.Bai S.Chen andQ.Li “Research on Mental Fatigue Based on Entropy Changes in Space Environment ” inProceeding of the 11th World Congress on Intelligent Control and Automation(IEEE 2012):74–77 https://doi.org/10.1109/VECIMS.2012.6273197.10.1109/VECIMS.2012.6273197 ↗
40. 10.3390/s2211401010.3390/s22114010 ↗
41. E.Jeong M.Seo andK.‐S.Kim “Guide for Wavelength Selection of LEDs for fNIRS Systems ” in22nd International Conference on Control Automation and Systems (ICCAS)(IEEE 2022):1819–1822 https://doi.org/10.23919/ICCAS55662.2022.10003745.10.23919/ICCAS55662.2022.10003745 ↗
42. 10.1007/s12541-024-01199-x10.1007/s12541-024-01199-x ↗