4.6 Article

M3BA: A Mobile, Modular, Multimodal Biosignal Acquisition Architecture for Miniaturized EEG-NIRS-Based Hybrid BCI and Monitoring

Journal

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
Volume 64, Issue 6, Pages 1199-1210

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TBME.2016.2594127

Keywords

Electroencephalography (EEG); hybrid brain-computer interface (BCI); mobile; modular; multimodal biosignal acquisition architecture (M3BA); multimodal; near-infrared spectroscopy (NIRS); wireless body area network (WBAN); wireless body sensor network (WBSN)

Funding

  1. BIMoS Graduate School, Technical University of Berlin
  2. National Research Foundation of Korea - Ministry of Education, Science, and Technology in the BK21 program
  3. German Research Foundation [DFG MU 987/6-1, SPP 1527, MU 987/14-1]

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Objective: For the further development of the fields of telemedicine, neurotechnology, and brain-computer interfaces, advances in hybrid multimodal signal acquisition and processing technology are invaluable. Currently, there are no commonly available hybrid devices combining bioelectrical and biooptical neurophysiological measurements [here electroencephalography (EEG) and functional near-infrared spectroscopy (NIRS)]. Our objective was to design such an instrument in a miniaturized, customizable, and wireless form. Methods: We present here the design and evaluation of a mobile, modular, multimodal biosignal acquisition architecture (M3BA) based on a high-performance analog front-end optimized for biopotential acquisition, a microcontroller, and our open-NIRS technology. Results: The designed M3BA modules are very small configurable high-precision and low-noise modules (EEG input referred noise @ 500 SPS 1.39 mu V-pp, NIRS noise equivalent power NEP750 nm = 5.92 pW(pp), and NEP850 nm = 4.77 pW(pp)) with full input linearity, Bluetooth, 3-D accelerometer, and low power consumption. They support flexible user-specified biopotential reference setups and wireless body area/sensor network scenarios. Conclusion: Performance characterization and in-vivo experiments confirmed functionality and quality of the designed architecture. Significance: Telemedicine and assistive neurotechnology scenarios will increasingly include wearable multimodal sensors in the future. The M3BA architecture can significantly facilitate future designs for research in these and other fields that rely on customized mobile hybrid biosignal acquisition hardware.

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