Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society |
| Untertitel | Enabling Innovative Technologies for Global Healthcare, EMBC 2020 |
| Seiten | 4046-4050 |
| Seitenumfang | 5 |
| ISBN (elektronisch) | 9781728119908 |
| Publikationsstatus | Veröffentlicht - 2020 |
Abstract
In general, the signal chain in modern mobile Brain-Computer Interfaces (BCIs) is subdivided into at least two blocks. These are usually wirelessly connected with digital signal processing part implemented separately and often stationary. This causes a limited mobility and results in an additional, although avoidable, latency due to the wireless transmission channel. Therefore, a novel, entirely mobile FPGA-based platform for BCIs has been designed and implemented. While featuring highly efficient adaptability to targeted algorithms due to the ultra low power Flash-based FPGA, the stackable system design and the configurable hardware ensure flexibility for the use in different application scenarios. Powered through a single Li-ion battery, the miniaturized system area of half the size of a credit card leads to high mobility and thus allow for real-world scenario applicability. A Bluetooth Low Energy extension can be connected without any significant area cost, if a wireless data or control signal transmission channel is required. The resulting system is capable of acquiring and fully processing of up to 32 EEG channels with 24 bit precision each and a sampling rate of 250-16k samples per second with a total weight less than 60 g.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Signalverarbeitung
- Medizin (insg.)
- Gesundheitsinformatik
- Informatik (insg.)
- Maschinelles Sehen und Mustererkennung
- Ingenieurwesen (insg.)
- Biomedizintechnik
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42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society: Enabling Innovative Technologies for Global Healthcare, EMBC 2020. 2020. S. 4046-4050 9175623.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - CereBridge
T2 - An Efficient, FPGA-based Real-Time Processing Platform for True Mobile Brain-Computer Interfaces
AU - Wahalla, Marc-Nils
AU - Paya Vaya, Guillermo
AU - Blume, Holger
PY - 2020
Y1 - 2020
N2 - In general, the signal chain in modern mobile Brain-Computer Interfaces (BCIs) is subdivided into at least two blocks. These are usually wirelessly connected with digital signal processing part implemented separately and often stationary. This causes a limited mobility and results in an additional, although avoidable, latency due to the wireless transmission channel. Therefore, a novel, entirely mobile FPGA-based platform for BCIs has been designed and implemented. While featuring highly efficient adaptability to targeted algorithms due to the ultra low power Flash-based FPGA, the stackable system design and the configurable hardware ensure flexibility for the use in different application scenarios. Powered through a single Li-ion battery, the miniaturized system area of half the size of a credit card leads to high mobility and thus allow for real-world scenario applicability. A Bluetooth Low Energy extension can be connected without any significant area cost, if a wireless data or control signal transmission channel is required. The resulting system is capable of acquiring and fully processing of up to 32 EEG channels with 24 bit precision each and a sampling rate of 250-16k samples per second with a total weight less than 60 g.
AB - In general, the signal chain in modern mobile Brain-Computer Interfaces (BCIs) is subdivided into at least two blocks. These are usually wirelessly connected with digital signal processing part implemented separately and often stationary. This causes a limited mobility and results in an additional, although avoidable, latency due to the wireless transmission channel. Therefore, a novel, entirely mobile FPGA-based platform for BCIs has been designed and implemented. While featuring highly efficient adaptability to targeted algorithms due to the ultra low power Flash-based FPGA, the stackable system design and the configurable hardware ensure flexibility for the use in different application scenarios. Powered through a single Li-ion battery, the miniaturized system area of half the size of a credit card leads to high mobility and thus allow for real-world scenario applicability. A Bluetooth Low Energy extension can be connected without any significant area cost, if a wireless data or control signal transmission channel is required. The resulting system is capable of acquiring and fully processing of up to 32 EEG channels with 24 bit precision each and a sampling rate of 250-16k samples per second with a total weight less than 60 g.
KW - Algorithms
KW - Brain-Computer Interfaces
KW - Computers
KW - Electric Power Supplies
KW - Signal Processing, Computer-Assisted
UR - http://www.scopus.com/inward/record.url?scp=85091019683&partnerID=8YFLogxK
U2 - 10.1109/embc44109.2020.9175623
DO - 10.1109/embc44109.2020.9175623
M3 - Conference contribution
C2 - 33018887
SN - 978-1-7281-1991-5
SP - 4046
EP - 4050
BT - 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society
ER -