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一个高性能异质硬件架构用于大脑计算机接口

Zhengbo Cai¹, Penghai Li¹, Longlong Cheng²

¹School of Integrated Circuit Science and Engineering, Tianjin University of Technology, Tianjin, 300384 People's Republic of China.

Biomedical engineering letters

|January 9, 2025

概括

这项研究介绍了一种用于嵌入式设备的新型异构的大脑计算机接口 (BCI) 架构. 该系统实现了高精度和高速度的脑电图 (EEG) 信号处理,克服了资源限制.

科学领域:

神经科学是一个神经科学.
计算机工程计算机工程
人工智能的人工智能

背景情况:

大脑-计算机接口 (BCI) 对人机交互至关重要,人工智能提高了性能.
将BCI转换为嵌入式设备提供了更低的功率和大小,但面临着复杂算法的资源和速度限制.

研究的目的:

为嵌入式系统优化提出一个异质的BCI架构.
为了使资源有限的设备上的脑电图 (EEG) 信号能够实时处理.

主要方法:

开发了一个ARM+FPGA异质BCI架构.
使用数据量化,层融合和数据增强优化了EEGNet模型.
为神经网络加速设计专用硬件引擎.

主要成果:

实现了稳定状态视觉唤起潜力 (SSVEP) 信号的93.3%的分类准确度.
实现了每次试验的0.2ms的低时间延迟和1.91W的功耗.
与传统处理器相比,显示了31.5倍的加速,精度下降最小 (0.7%).

结论:

提出的异质BCI架构对于嵌入式系统来说是实用的.
这种方法显著提高了EEG信号处理速度和效率.
该研究强调了硬件和软件共同设计用于先进的BCI应用程序的潜力.

关键词:

大脑与计算机接口 (BCI)电脑电图 (EEG) 是一个电脑电图.现场可编程门阵列 (FPGA)硬件加速器是一个硬件加速器.

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