Research on Multi-parameter Visualization Technology of Brain Function Based on EEG

机译：基于脑电图的脑功能多参数可视化技术研究

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来源
《International Conference on Medical Imaging Physics and Engineering》|2019年|1-4|共4页
会议地点 Shenzhen(CN)
作者
Zhou Fang; Liuye Yao; Zhiyu Qian;
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作者单位

Nanjing University of Aeronautics and Astronautics Department of Biomedical Engineering Jiangsu Nanjing China 211106;

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原文格式 PDF
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关键词
Electroencephalography; Software; Data visualization; Brain; Matlab; Fatigue; Electrodes;

机译：脑电图;软件;数据可视化;脑; Matlab;疲劳;电极;

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专利

1. Graph Analysis and Visualization for Brain Function Characterization Using EEG Data [J] . Sakkalis Vangelis, Tsiaras Vasilis, Tollis Ioannis G. Journal of healthcare engineering. . 2010,第3期

机译：使用脑电图数据进行脑功能表征的图形分析和可视化
2. Shortest path based network analysis to characterize cognitive load states of human brain using EEG based functional brain networks [J] . Thilaga M., Ramasamy Vijayalakshmi, Nadarajan R., Journal of integrative neuroscience. . 2018,第2期

机译：基于脑电站的功能性脑网络的最短路径的网络分析来表征人脑的认知负荷状态
3. An EEG-based methodology for the estimation of functional brain connectivity networks: Application to the analysis of newborn EEG seizure [J] . Abbas Ali Kareem, Azemi Ghasem, Ravanshadi Samin, Biomedical signal processing and control . 2021,第Jana期

机译：基于EEG的归类方法，用于估计功能性脑连接网络：应用于新生EEG癫痫发作的分析
4. Research on Multi-parameter Visualization Technology of Brain Function Based on EEG [C] . Zhou Fang, Liuye Yao, Zhiyu Qian International Conference on Medical Imaging Physics and Engineering . 2019

机译：基于EEG的大脑功能多参数可视化技术研究
5. Methods for rapid EEG-based mapping of human brain functions. [D] . Barkan, Helen Irene. 2002

机译：基于快速脑电图绘制人脑功能的方法。
6. EEG-based classification of epilepsy and PNES: EEG microstate and functional brain network features [O] . Negar Ahmadi, Yulong Pei, Evelien Carrette, 2020

机译：基于脑电图的癫痫和PNES分类：脑电图的微状态和功能性大脑网络功能
7. EEG Signal Discrimination using Non-linear Dynamics in the EMD Domain S. M. Shafiul Alam,S. M. Shafiul Alam,Aurangozeb, and Syed TarekShahriar Abstract—An EMD-chaos based approach is proposed todiscriminate EEG signals corresponding to healthy persons,and epileptic patients during seizure-free intervals and seizureattacks. An electroencephalogram (EEG) is first empiricallydecomposed to intrinsic mode functions (IMFs). The nonlineardynamics of these IMFs are quantified in terms of the largestLyapunov exponent (LLE) and correlation dimension (CD).This chaotic analysis in EMD domain is applied to a large groupof EEG signals corresponding to healthy persons as well asepileptic patients (both with and without seizure attacks). It isshown that the values of the obtained LLE and CD exhibitfeatures by which EEG for seizure attacks can be clearlydistinguished from other EEG signals in the EMD domain.Thus, the proposed approach may aid researchers in developingeffective techniques to predict seizure activities. Index Terms—Electroencephalogram (EEG), empiricalmode decomposition (EMD), largest Lyapunov exponent (LLE),correlation dimension (CD), epileptic seizures. The Authors are with the Electrical and Electronic EngineeringDepartment, Bangladesh University of Engineering and Technology,Dhaka-1000, Bangladesh (e-mail: imamul@eee.buet.ac.bd) PDF Cite: S. M. Shafiul Alam,S. M. Shafiul Alam,Aurangozeb, and Syed Tarek Shahriar, "EEG Signal Discrimination using Non-linear Dynamics in the EMD Domain," International Journal of Computer and Electrical Engineering vol. 4, no. 3, pp. 326-330, 2012. PREVIOUS PAPER Perception of Emotions Using Constructive Learningthrough Speech NEXT PAPER Physical Layer Impairments Aware OVPN Connection Selection Mechanisms Copyright © 2008-2013. International Association of Computer Science and Information Technology Press (IACSIT Press) [O] . S. M. Shafiul Alam, Syed TarekShahriar 2012

机译：EEG信号在EMD域S. S. Shafiul Alam，S中的非线性动力学使用非线性动力学。 M. Shafiul Alam，Aurangozeb和Syed Tarekshahriar摘要 - 基于EMD Chaos的方法，提出了对应于健康人的EEG信号，癫痫发作期间的癫痫患者和Seizureattacks。脑电图（EEG）首先被凭经上分解为内在模式功能（IMF）。这些IMF的非线性动力学在最大范围的指数（LLE）和相关尺寸（CD）方面是量化的。本域中的混沌分析应用于与健康人相对应的大型脑电图（Asepileptic患者）（两者都有癫痫发作）。因此，所获得的LLE和CD表展的价值可以从EMD领域的其他EEG信号中清晰地区分脑电图的表达展示。本拟议的方法可以帮助研究人员以预测癫痫发作的癫痫发作技术。索引术语 - 脑电图（EEG），仿真态分解（EMD），最大的Lyapunov指数（LLE），相关维度（CD），癫痫发作。作者与电气电子和电子工程公司，孟加拉国工程和技术大学，孟加拉国达卡 - 1000（电子邮件：imamul@eee.buet.ac.bd）pdf cite：s. m. shafiul Alam，s。 M. Shafiul Alam，Aurangozeb和Syed Tarek Shahriar，“EEG信号歧视在EMD领域的非线性动态，”计算机电气工程卷国际杂志。 4，不。 3，pp。326-330,2012，上一篇论文对情绪的看法，使用建设性的学习言论下一篇论文物理层障碍意识到OVPN连接选择机制版权所有©2008-2013。国际计算机科学与信息技术协会出版社（IACSIT Press）
8. Investigation of the Correlation Between Neurocognitive Function with Advanced Magnetic Resonance Imaging (MRI), Electroencephalography (EEG) in Patients with Traumatic Brain Injury Exposure: Neurocognitive function and advanced MRI and EEG. [R] . A. Ptito B. Cheung J. Chen N. Gosselin S. Huntgeburth 2011

机译：神经认知功能与先进磁共振成像（mRI），脑电图（EEG）在创伤性脑损伤患者中的相关性研究：神经认知功能和先进的mRI和脑电图。

Research on Multi-parameter Visualization Technology of Brain Function Based on EEG

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