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  4. Deep.Neural.Signal.Pre-Processor - Towards Development of AI-enhanced End-to-End BCIs
 
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September 20, 2023
Journal Article
Title

Deep.Neural.Signal.Pre-Processor - Towards Development of AI-enhanced End-to-End BCIs

Abstract
This paper presents a software-based Pythonframework for developing future AI-enhanced end-to-end Brain-Computer-Interfaces (BCI). This framework contains modules from the emulated analogue front-end and from neural signal pre-processing for invasive neural applications. These modules can be assembled into several pipeline versions for evaluation and benchmarking. The aim of this framework is to accelerate the development of BCIs due to system-wide optimizations in order to set the requirements for hardware development without prior knowledge on the basis of accuracy (recall and precision) and latency. In the next step, the pipeline can be optimised for on-chip and embedded execution.
Author(s)
Buron, Leo
Universität Duisburg-Essen  
Erbslöh, Andreas
Universität Duisburg-Essen  
Ur-Rehman, Zia
Ruhr-Universität Bochum  
Klaes, Christian
Ruhr-Universität Bochum  
Seidl, Karsten  
Universität Duisburg-Essen, EBS
Schiele, Gregor
Universität Duisburg-Essen  
Journal
Current directions in biomedical engineering  
Conference
German Society of Biomedical Engineering (BMT Annual Meeting) 2023  
Open Access
File(s)
Download (2.56 MB)
Rights
CC BY 4.0: Creative Commons Attribution
DOI
10.1515/cdbme-2023-1118
10.24406/publica-1924
Additional link
Full text
Language
English
Fraunhofer-Institut für Mikroelektronische Schaltungen und Systeme IMS  
Keyword(s)
  • neural signal processing

  • extracellular recordings

  • artificial intelligence

  • embedded computing

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