Authors:
Hendrik Wöhrle
1
;
Johannes Teiwes
2
;
Marc Tabie
3
;
Anett Seeland
1
;
Elsa Andrea Kirchner
3
and
Frank Kirchner
3
Affiliations:
1
German Research Center for Artificial Intelligence (DFKI), Germany
;
2
University of Bremen, Germany
;
3
German Research Center for Artificial Intelligence (DFKI) and University of Bremen, Germany
Keyword(s):
Brain Computer Interfaces, Mobile Systems, FPGA, Hardware Accelerator.
Related
Ontology
Subjects/Areas/Topics:
Biomedical Engineering
;
Biomedical Instruments and Devices
;
Brain-Computer Interfaces
;
Devices
;
Distributed and Mobile Software Systems
;
EMG Signal Processing and Applications
;
Health Engineering and Technology Applications
;
Health Information Systems
;
Human-Computer Interaction
;
Mobile and Embedded Devices
;
Mobile Technologies
;
Mobile Technologies for Healthcare Applications
;
Neural Rehabilitation
;
NeuroSensing and Diagnosis
;
Neurotechnology, Electronics and Informatics
;
Physiological Computing Systems
;
Software Engineering
Abstract:
Brain Computer Interfaces (BCIs) allow to use psychophysiological data for a large range of innovative applications. One interesting application for rehabilitation robotics is to modulate exoskeleton controls by predicting movements of a human user before they are actually performed. However, usually BCIs are used mainly in artificial and stationary experimental setups. Reasons for this are, among others, the immobility of the utilized hardware for data acquisition, but also the size of the computing devices that are required for the analysis ofthe human electroencephalogram. Therefore, mobile processing devices need to be developed. A problem is often the limited processing power of these devices, especially if there are firm time constraints as in thecase of movement prediction. Field programmable gate array (FPGA)-based application-specific dataflow accelerators are a possible solution here. In this paper we present the first FPGA-based processing system that is able to predict up
coming movements by analyzing the human electroencephalogram. We evaluate the system regarding computation time and classification performance and show that it can compete with a standard desktop computer.
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