Deep.Neural.Signal。Pre-Processor -迈向ai增强型端到端BCIs

Leo Buron, Andreas Erbslöh, Karsten Seidl, Gregor Schiele, Karsten Seidl, Zia Ur-Rehman, Christian Klaes
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引用次数: 0

摘要

本文提出了一个基于软件的Python框架,用于开发未来ai增强的端到端脑机接口(BCI)。该框架包含来自仿真模拟前端和用于侵入性神经应用的神经信号预处理的模块。这些模块可以组装成几个管道版本,以进行评估和基准测试。该框架的目的是通过系统范围的优化来加速bci的开发,以便在没有基于准确性(召回率和精度)和延迟的先验知识的情况下设置硬件开发的需求。在下一步,流水线可以优化芯片上和嵌入式执行。
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Deep.Neural.Signal.Pre-Processor - Towards Development of AI-enhanced End-to-End BCIs
Abstract This paper presents a software-based Python framework 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.
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来源期刊
Current Directions in Biomedical Engineering
Current Directions in Biomedical Engineering Engineering-Biomedical Engineering
CiteScore
0.90
自引率
0.00%
发文量
239
审稿时长
14 weeks
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