电子科技大学学报2025，Vol.54Issue(3)：321-332,12.DOI:10.12178/1001-0548.2024145
基于位串行卷积神经网络加速器的运动想象脑电信号识别系统
A motor imagery EEG signal recognition system based on a bit-serial convolutional neural network accelerator
程筱舒 1王忆文 1娄鸿飞 1丁玮然 1李平1
作者信息
- 1. 电子科技大学集成电路科学与工程学院(示范性微电子学院),成都 611731
- 折叠
摘要
Abstract
Accurate recognition of motor imagery electroencephalogram(EEG)signals is a significant challenge in neuroscience and biomedical engineering.This paper presents an EEG signal recognition system based on a bit-serial convolutional neural network(CNN)accelerator,leveraging its advantages of compact size,low power consumption,and high real-time performance.The software implementation includes the preprocessing,feature extraction,and classification of EEG data,and utilizes Gramian angular field(GAF)transformation to map one-dimensional signals into two-dimensional feature maps for network processing.On the hardware side,innovative methods such as column-buffering dataflow and fixed-multiplier bit-serial multiplication are proposed,and a prototype of the bit-serial CNN accelerator is successfully implemented on FPGA.The results show that the FPGA implementation of the bit-serial LeNet-5 accelerator achieves average classification accuracies of 95.68%and 97.32%on the BCI Competition IV datasets 2a and 2b,with kappa values of 0.942 and 0.946,respectively.These performances provide an efficient solution for the recognition of motor imagery EEG signals.关键词
脑机接口/运动想象/卷积神经网络/硬件加速器/格拉姆角场Key words
brain computer interface/motor imagery/convolutional neural network/hardware accelerator/Gramian angular field分类
电子信息工程引用本文复制引用
程筱舒,王忆文,娄鸿飞,丁玮然,李平..基于位串行卷积神经网络加速器的运动想象脑电信号识别系统[J].电子科技大学学报,2025,54(3):321-332,12.
