CereBridge: An Efficient, FPGA-based Real-Time Processing Platform for True Mobile Brain-Computer Interfaces

Publikation: Beitrag in Buch/Bericht/Sammelwerk/KonferenzbandAufsatz in KonferenzbandForschungPeer-Review

Autoren

Organisationseinheiten

Forschungs-netzwerk anzeigen

Details

OriginalspracheEnglisch
Titel des Sammelwerks42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society
UntertitelEnabling Innovative Technologies for Global Healthcare, EMBC 2020
Seiten4046-4050
Seitenumfang5
ISBN (elektronisch)9781728119908
PublikationsstatusVeröffentlicht - 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

Zitieren

CereBridge: An Efficient, FPGA-based Real-Time Processing Platform for True Mobile Brain-Computer Interfaces. / Wahalla, Marc-Nils; Paya Vaya, Guillermo; Blume, Holger.
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/KonferenzbandAufsatz in KonferenzbandForschungPeer-Review

Wahalla, M-N, Paya Vaya, G & Blume, H 2020, CereBridge: An Efficient, FPGA-based Real-Time Processing Platform for True Mobile Brain-Computer Interfaces. in 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society: Enabling Innovative Technologies for Global Healthcare, EMBC 2020., 9175623, S. 4046-4050. https://doi.org/10.1109/embc44109.2020.9175623
Wahalla, M.-N., Paya Vaya, G., & Blume, H. (2020). CereBridge: An Efficient, FPGA-based Real-Time Processing Platform for True Mobile Brain-Computer Interfaces. In 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society: Enabling Innovative Technologies for Global Healthcare, EMBC 2020 (S. 4046-4050). Artikel 9175623 https://doi.org/10.1109/embc44109.2020.9175623
Wahalla MN, Paya Vaya G, Blume H. CereBridge: An Efficient, FPGA-based Real-Time Processing Platform for True Mobile Brain-Computer Interfaces. in 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 doi: 10.1109/embc44109.2020.9175623
Wahalla, Marc-Nils ; Paya Vaya, Guillermo ; Blume, Holger. / CereBridge : An Efficient, FPGA-based Real-Time Processing Platform for True Mobile Brain-Computer Interfaces. 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
Download
@inproceedings{94f82f5d507e473f9a12d19232cec28f,
title = "CereBridge: An Efficient, FPGA-based Real-Time Processing Platform for True Mobile Brain-Computer Interfaces",
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.",
keywords = "Algorithms, Brain-Computer Interfaces, Computers, Electric Power Supplies, Signal Processing, Computer-Assisted",
author = "Marc-Nils Wahalla and {Paya Vaya}, Guillermo and Holger Blume",
year = "2020",
doi = "10.1109/embc44109.2020.9175623",
language = "English",
isbn = "978-1-7281-1991-5",
pages = "4046--4050",
booktitle = "42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society",

}

Download

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 -

Von denselben Autoren