| Zugriffsnummer | 37110 |
| Dokumenttyp | Zeitschriftenartikel |
| Peer Review | mit Peer Review |
| Sprache | Englisch |
| Titel | M3BA: A Mobile, Modular, Multimodal Biosignal Acquisition architecture for miniaturized EEG-NIRS based hybrid BCI and monitoring |
| Autor(in); Institution |
Lühmann, Alexander von; Machine Learning Dept., Computer Science, Berlin Institute of Technology, Berlin, GERMANY
Wabnitz, Heidrun; 8.3, Biomedizinische Optik, PTB-Berlin
Sander-Thömmes, Tilmann; 8.2, Biosignale, PTB-Berlin
Müller, Klaus-Robert; Machine Learning Dept., Computer Science, Berlin Institute of Technology, Berlin, GERMANY
|
| Quelle/Jahr | IEEE Transactions on Biomedical Engineering: 64 (2017), 6, 1199 - 1210 |
| ISSN | 0018-9294 (PRINT) ; 1558-2531 (ONLINE) |
| DOI | |
| Verlag | New York, NY: IEEE |
| Freie Schlagworte | 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) |
| Zusammenfassung | Objective: For the further development of the fields of telemedicine, neurotechnology and Brain-Computer Interfaces (BCI), advances in hybrid multimodal signal acquisition and processing technology are invaluable. Currently, there are no commonly available hybrid devices combining bio-electrical and bio-optical neurophysiological measurements (here Electroencephalography (EEG) and functional Near Infrared Spectroscopy (fNIRS)). Our objective was the design of such an instrument, and that 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 bio-potential acquisition, a microcontroller, and our openNIRS technology. Results: The designed M3BA modules are very small configurable high precision and low-noise modules (EEG input referred noise @ 500 SP S 1.39 µVpp, NIRS noise equivalent power NEP 750nm = 5.92 pWpp, NEP 850nm = 4.77 pWpp) with full input linearity, Bluetooth, 3D accelerometer and low- power consumption. They support flexible, user-specified bio- potential reference setups, and Wireless Body Area/Sensor Network (WBAN/WBSN) 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. The work of A. von Lühmann was supported by the BIMoS Graduate School, Technical University of Berlin. The work of K.-R. Müller was partially supported by the National Research Foundation of Korea funded by the Ministry of Education, Science, and Technology in the BK21 program and by the German Research Foundation under Grant DFG MU 987/6-1, Grant SPP 1527, and Grant MU 987/14-1. |
| Kostenfreier Zugang | Open Access Hybrid |
Zitierung
Lühmann, A. V., Wabnitz, H., Sander-Thömmes, T., & Müller, K.-R. (2017). M3BA: A Mobile, Modular, Multimodal Biosignal Acquisition architecture for miniaturized EEG-NIRS based hybrid BCI and monitoring. IEEE Transactions on Biomedical Engineering, 64(6), 1199–1210. https://doi.org/10.1109/tbme.2016.2594127