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Adaptive filtering and analysis of EEG signals in the time-frequency domain based on the local entropy

机译：基于局部熵的时频域中EEG信号的自适应滤波和分析

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Guruprasad Madhale Jadav; Jonatan Lerga; Ivan Štajduhar;
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1. Adaptive filtering and analysis of EEG signals in the time-frequency domain based on the local entropy [J] . Madhale Jadav Guruprasad, Lerga Jonatan, ?tajduhar Ivan EURASIP journal on advances in signal processing . 2020,第a期

机译：基于局部熵的时频域中EEG信号的自适应滤波和分析
2. Adaptive Emotional Information Retrieval From EEG Signals in the Time-Frequency Domain [J] . Petrantonakis P.C. Signal Processing, IEEE Transactions on . 2012,第5期

机译：时频域中脑电信号的自适应情绪信息检索
3. Adaptive Emotional Information Retrieval From EEG Signals in the Time-Frequency Domain [J] . Petrantonakis P.C. Signal Processing, IEEE Transactions on . 2012,第5期

机译：自适应情感信息从时频域中的EEG信号检索
4. Nonstationary signals information content estimation based on the local Rényi entropy in the time-frequency domain [C] . Saulig N., Sucic V. 11th International Conference on Telecommunications . 2011

机译：基于时频局部Rényi熵的非平稳信号信息内容估计
5. Toward single-trial measurement of vibrotactile driving responses in EEG: New approaches based on multi-channel adaptive filtering. [D] . Xu, Min. 2003

机译：迈向EEG中的触觉驱动反应的单次试验测量：基于多通道自适应滤波的新方法。
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）
8. Time-Frequency Domain Analysis of Vibration Signals for MachineryDiagnostics.(4): Interpretation Using Image Processing Techniques [R] . Wang, W. J., Mcfadden, P. D. 1992

机译：机械诊断振动信号的时频域分析。（4）：利用图像处理技术进行解释

Adaptive filtering and analysis of EEG signals in the time-frequency domain based on the local entropy

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