Extraction of Feature Information in EEG Signal by Virtual EEG Instrument with the Functions of Time-Frequency Analysis

机译：具有时频分析功能的虚拟脑电仪提取脑电信号特征信息

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To extract the feature information in EEG signals efficiently, various time-frequency analysis methods for EEG signals analysis have been discussed in theory. However, it is reported little in literature that how to make these theoretical research productions be useful algorithms in practice and integrate them into EEG detection and analysis instrument. Based on virtual instrument technology, the time-frequency analysis methods have been further discussed from theory, then the concrete algorithms of the time-frequency analysis methods used for the extraction of EEG feature rhythms have been established and integrated into the virtual EEG instrument. By this way, the time-frequency analysis methods to be used to detect and extract the feature information in EEG signals automatically can be realized in clinical.

机译：为了有效地提取脑电信号中的特征信息，理论上讨论了各种时频分析方法进行脑电信号分析。但是，在文献中很少报道如何使这些理论研究成果在实践中成为有用的算法，并将其集成到脑电图检测和分析仪器中。在虚拟仪器技术的基础上，从理论上进一步探讨了时频分析方法，然后建立了用于提取脑电特征节律的时频分析方法的具体算法，并将其集成到虚拟脑电仪器中。这样，可以在临床上实现自动检测和提取脑电信号特征信息的时频分析方法。

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《World congress on medical physics and biomedical engineering;International congress of the IUPESM》|2011年|p.1748-1751|共4页
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作者
Z. Ji and C.L. Peng;
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中图分类医用物理学;
关键词
EEG signals; feature information; time-frequency; virtual instrument;

机译：脑电信号功能信息;时间频率虚拟仪器;

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1. Principles of time-frequency feature extraction for change detection in non-stationary signals: Applications to newborn EEG abnormality detection [J] . Boashash Boualem, Azemi Ghasem, Khan Nabeel Ali Pattern Recognition: The Journal of the Pattern Recognition Society . 2015,第3期

机译：非静止信号变化检测的时频特征提取原理：对新生EEG异常检测的应用
2. New feature extraction approach for epileptic EEG signal detection using time-frequency distributions [J] . Carlos Guerrero-Mosquera, Armando Malanda Trigueros, Jorge Iriarte Franco, Medical and Biological Engineering and Computing . 2010,第4期

机译：利用时频分布检测癫痫脑电信号的新特征提取方法
3. New feature extraction approach for epileptic EEG signal detection using time-frequency distributions. [J] . Guerrero Mosquera C, Trigueros AM, Franco JI, Medical and Biological Engineering and Computing: Journal of the International Federation for Medical and Biological Engineering . 2010,第4期

机译：使用时频分布的癫痫性脑电信号检测的新特征提取方法。
4. Extraction of feature information in EEG signal by virtual EEG instrument with the functions of time-frequency analysis [C] . Shuren Qin, Zhong Ji Image and Signal Processing and Analysis, 2009. ISPA 2009 . 2009

机译：具有时频分析功能的虚拟脑电仪提取脑电信号特征信息
5. EEG Data Analysis, Feature Extraction and Classifiers. [D] . Zhou, Jing. 2011

机译：脑电数据分析，特征提取和分类器。
6. Methods of EEG Signal Features Extraction Using Linear Analysis in Frequency and Time-Frequency Domains [O] . Amjed S. Al-Fahoum, Ausilah A. Al-Fraihat 2014

机译：频域和时域线性分析的脑电信号特征提取方法
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）

Extraction of Feature Information in EEG Signal by Virtual EEG Instrument with the Functions of Time-Frequency Analysis

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