[1]张泽瑞,殷跃红.基于异步脑机接口系统的远程低时延控制研究[J].机械与电子,2024,42(10):42-47.
　ZHANG Zerui,YIN Yuehong.Research on Remote Low-latency Control Based on Asynchronous Brain-computer Interface System[J].Machinery & Electronics,2024,42(10):42-47.
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基于异步脑机接口系统的远程低时延控制研究()
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《机械与电子》[ISSN:1001-2257/CN:52-1052/TH]

卷:
42
期数:
2024年10期
页码:
42-47
栏目:
自动控制与检测
出版日期:
2024-10-30

文章信息/Info

Title:
Research on Remote Low-latency Control Based on Asynchronous Brain-computer Interface System
文章编号:
1001-2257 ( 2024 ) 10-0042-07
作者:
张泽瑞; 殷跃红
上海交通大学机械与动力工程学院,上海 200240
Author(s):
ZHANG Zerui ;  YIN Yuehong
( School of Mechanical Engineering , Shanghai Jiao Tong University , Shanghai 200240 , China )
关键词:
异步脑机接口系统; 半同步半异步线程池; 低时延; 控制系统
Keywords:
asynchronous brain-computer interface system ;  semi synchronous and semi asynchronous thread pools ;  low delay ;  control system
分类号:
TN911.7 ; TP242
文献标志码:
A
摘要:
针对脑机接口目前应用在控制领域存在着自主性较低、实时性较差等问题,展开了基于异步脑机接口系统的远程低时延控制的研究。异步脑机接口系统采用滤波器组典型相关分析算法实现特征识别,并通过检测连续滑动窗口阈值实现状态识别;在此基础上,搭建两级 C / S 架构解耦原始系统,通过分析传统控制流程存在的时间和性能损耗,设计了基于半同步半异步线程池的新型控制系统架构。通过自研移动采样机器人进行了实验验证,实验结果表明,该系统具备可远程、低时延、高稳定性和高识别率的特点。
Abstract:
In view of the problems of low autonomy and poor real-time performance in the current application of brain-computer interface in the control field , research on remote low-latency control based on asynchronous brain-computer interface systems is carried out.The asynchronous brain-computer interface system uses the filter bank typical correlation analysis algorithm to realize feature recognition , and realizes state recognition by detecting continuous sliding window thresholds.On this basis , a two-level C / S architecture is built to decouple the original system.By analyzing the time and performance losses of traditional control processes , a new control system architecture based on semi-synchronous and semi-asynchronous thread pools is designed.Experimental verification is carried out through a self-developed mobile sampling robot.The experimental results show that the system has the characteristics of remote control , low delay , high stability and high recognition rate.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期: 2023-12-15
作者简介:张泽瑞 ( 1999- ),男,河北衡水人,硕士研究生,研究方向为机器人智能控制;殷跃红 ( 1968- ),男,江苏泰州人,教授,研究方向为人工智能、机器视觉与力觉力控制、纳米机器人(分子马达)与骨骼肌生物力学原理、智能机器人、康复外骨骼机器人、超精密智能制造。
更新日期/Last Update: 2024-10-31