Classification of Motor Imaginary Signals for Machine Commmunication - A Novel Approach for Brain Machine Interface Design

机译：用于机器通信的电机虚信号分类-一种用于脑机接口设计的新方法

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A great interest for brain machine interface (BMI) is in the arising nowadays, instigated by several promising scientific and technological outcomes. The methods of measuring, processing and classifying brain activities, in order to interpret neuronal signals into machine control is often regarded as a challenging possibility as the expected beneficiaries are patients affected by motor disorders and paralyses. Aiming at the usage of motor imaginary signals for machine control, the paper presents a BMI architecture with the combination of OPC (OLE process control), a standard communication protocol for the automation and machine control industry. We also analyzed the principle of real-time communication between simulated actuators and MATLAB which was used for signal processing and. Adaptive recursive bandpass filters and SFAM were used for the signals feature extraction and classifications respectively, whereas the communication with simulated actuators were realized with the usage of the Matricon OPC Server and MATLAB OPC Client. Test results also indicate the feasibility and effectiveness of implementing the proposed method in real time applications with reference of the control response time.

机译：对脑机接口（BMI）的极大兴趣是当今的兴起，其原因是一些有前途的科学技术成果。为了将神经元信号解释为机器控制，测量，处理和分类大脑活动的方法通常被视为具有挑战性的可能性，因为预期的受益者是运动障碍和麻痹症患者。针对将电机虚信号用于机器控制，本文提出了一种结合了OPC（OLE过程控制）的BMI架构，这是自动化和机器控制行业的标准通信协议。我们还分析了模拟执行器和MATLAB之间用于信号处理的实时通信原理。自适应递归带通滤波器和SFAM分别用于信号特征提取和分类，而通过使用Matricon OPC Server和MATLAB OPC Client实现与仿真执行器的通信。测试结果还表明参考控制响应时间在实时应用中实施该方法的可行性和有效性。

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来源
《Signal Acquisition and Processing, 2009. ICSAP 2009》|2009年|34-38|共5页
会议地点 Kuala Lumpur(MY);Kuala Lumpur(MY)
作者
Jeyabalan V.; Samraj A.; Loo Chu Kiong;
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Fac. of Eng. Sci. Technol., Multimedia Univ., Melaka, Malaysia;

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关键词
band-pass filters; brain-computer interfaces; feature extraction; machine control; medical signal processing; neurophysiology; process control; protocols; recursive filters; signal classification; OLE process control; SFAM; adaptive recursive bandpass filters; brain machine interface; machine communication; motor imaginary signal classification; neuronal signals; standard communication protocol; Adaptive filters; BMI; Motor Imaginary; OPC;

机译：带通滤波器;脑机接口;特征提取;机器控制;医学信号处理;神经生理学;过程控制;协议;递归滤波器;信号分类; OLE过程控制; SFAM;自适应递归带通滤波器;脑机接口;机器通信电机虚信号分类神经信号标准通信协议自适应滤波器BMI电机虚像OPC;

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1. Motor imaginary-based brain-machine interface design using programmable logic controllers for the disabled [J] . Vickneswaran Jeyabalan, Andrews Samraj, Chu Kiong Loo Computer methods in biomechanics and biomedical engineering . 2010,第5期

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2. Improving the Separability of Motor Imagery EEG Signals Using a Cross Correlation-Based Least Square Support Vector Machine for Brain–Computer Interface [J] . Siuly Siuly, Yan Li Neural Systems and Rehabilitation Engineering, IEEE Transactions on . 2012,第4期

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3. Ensemble recordings of human subcortical neurons as a source of motor control signals for a brain-machine interface. [J] . Patil PG, Carmena JM, Nicolelis MA, Neurosurgery . 2004,第1期

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4. Classification of Motor Imaginary Signals for Machine Communication - A Novel Approach for Brain Machine Interface Design [C] . Vickneswaran Jeyabalan, Andrews Samraj, Loo Chu Kiong International Conference on Signal Acquisition and Processing . 2009

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5. Finding Structure in Electrocorticographic Neural Signals for Brain-Machine Interface Applications. [D] . Omigbodun, Akinyinka Oreoluwa. 2017

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6. A Study on the Effect of Electrical Stimulation as a User Stimuli for Motor Imagery Classification in Brain-Machine Interface [O] . Saugat Bhattacharyya, Maureen Clerc, Mitsuhiro Hayashibe 2016

机译：电刺激作为用户刺激对脑机界面运动图像分类的影响研究
7. Motor imagery classification in Brain computer interface (BCI) based on EEG signal by using machine learning technique [O] . N. E. Md Isa, A. Amir, M. Z. Ilyas, 2019

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Classification of Motor Imaginary Signals for Machine Commmunication - A Novel Approach for Brain Machine Interface Design

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