Generation And Reconstruction Of Eeg Signals Using Matlab

Dr.T.Saravanan

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Generation And Reconstruction Of Eeg Signals Using Matlab

机译：用Matlab生成和重构脑电信号

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Biomedical signal compression has been increasing interest due to the need of storing or transmitting large amount of multichannel data. Wavelets and wavelet packets have recently emerged as powerful tools for signal compression. A novel scheme for signal

机译：由于需要存储或传输大量的多通道数据，因此生物医学信号压缩引起了越来越多的关注。小波和小波包最近已成为强大的信号压缩工具。一种新颖的信号方案

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《International Journal of Engineering Trends and Technology 》 |2011年第1期| 共页
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Dr.T.Saravanan;
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1. Analysis and simulation of brain signal data by EEG signal processing technique using MATLAB [J] . Sasikumar Gurumurthy, Vudi Sai Mahit, Rittwika Ghosh International Journal of Engineering and Technology . 2013 ,第3期

机译：MATLAB脑电图信号处理技术的脑信号数据分析与仿真
2. Matlab FE_Toolbox - an universal utility for feature extraction of EEG signals for BCI realization [J] . Marcin KOLODZIEJ, Andrzej MAJKOWSKI, Remigiusz J. RAK Przeglad Elektrotechniczny . 2010 ,第1期

机译：Matlab FE_Toolbox-用于提取脑电信号以实现BCI的通用实用程序
3. Depression recognition based on the reconstruction of phase space of EEG signals and geometrical features [J] . Akbari Hesam, Sadiq Muhammad Tariq, Rehman Ateeq Ur, Applied Acoustics . 2021 ,第Auga期

机译：基于EEG信号的相位空间的重建和几何特征的抑郁识别
4. A realistic spatiotemporal source model for EEG activity: Application to the reconstruction of epileptic depth-EEG signals [C] . Cosandier-Rimele, D., Badier, Engineering in Medicine and Biology Society, 2006 Annual International Conference of the IEEE . 2006

机译：脑电活动的现实时空源模型：在癫痫深度脑电信号的重建中的应用。
5. A wavelet-based matlab suite for the analysis of EEG recordings. [D] . Clemans, Zachary Adam. 2013

机译：基于小波的matlab套件，用于脑电图记录分析。
6. DETECT: A MATLAB Toolbox for Event Detection and Identification in Time Series with Applications to Artifact Detection in EEG Signals [O] . Vernon Lawhern, W. David Hairston, Kay Robbins -1

机译：检测：用于时间序列中事件检测和识别的MATLAB工具箱及其在EEG信号中的伪像检测中的应用
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. DETECT: A MATLAB Toolbox for Event Detection and Identification in Time Series, with Applications to Artifact Detection in EEG Signals. [R] . Lawhern, V., Hairston, W. D., Robbins, K. 2013

机译：DETECT：用于时间序列中事件检测和识别的maTLaB工具箱，以及EEG信号中的伪影检测应用。

Generation And Reconstruction Of Eeg Signals Using Matlab

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