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Clustering of Spectral Patterns based on EMD Components of EEG Channels with Applications to Neurophysiological Signals Separation

机译：基于EEG通道的EMD分量的谱模式聚类及其在神经生理信号分离中的应用

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Tomasz M. Rutkowski; Andrzej Cichocki; Toshihisa Tanaka; Anca L. Ralescu; Danilo P. M;
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年度 2012
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1. EMD APPROACH TO MULTICHANNEL EEG DATA - THE AMPLITUDE AND PHASE COMPONENTS CLUSTERING ANALYSIS [J] . TOMASZ M. RUTKOWSKI, DANILO P. MANDIC, ANDRZEJ CICHOCKI, Journal of circuits, systems and computers . 2010,第1期

机译：多通道脑电数据的EMD方法-幅度和相位分量聚类分析
2. EMD-Based Temporal and Spectral Features for the Classification of EEG Signals Using Supervised Learning [J] . Riaz Farhan, Hassan Ali, Rehman Saad, Neural Systems and Rehabilitation Engineering, IEEE Transactions on . 2016,第1期

机译：基于EMD的时空和频谱特征用于监督学习的EEG信号分类
3. Research on removal algorithm of EOG artifacts in single-channel EEG signals based on CEEMDAN-BD [J] . Wu Quanyu, Zhang WenQiang, Wang Ye, Computer methods in biomechanics and biomedical engineering . 2021,第9a12期

机译：基于CeeMDAN-BD的单通道EEG信号中EOG伪影的移除算法研究
4. Clustering of Spectral Patterns Based on EMD Components of EEG Channels with Applications to Neurophysiological Signals Separation [C] . Tomasz M. Rutkowski, Andrzej Cichocki, Toshihisa Tanaka, ICONIP 2008;International conference on advances in neuro-information processing . 2009

机译：基于EEG通道EMD分量的谱模式聚类及其在神经生理信号分离中的应用
5. Separable spatio-spectral patterns in EEG signals during motor-imagery tasks. [D] . Shokouh Aghaei, Amirhossein. 2014

机译：运动图像任务期间，EEG信号中的空间光谱谱可分离。
6. Clinical Applications of Computer Analysis of EEG and other Neurophysiological Signals [O] . D K Prasher 1988

机译：脑电图和其他神经生理信号的计算机分析的临床应用
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）

Clustering of Spectral Patterns based on EMD Components of EEG Channels with Applications to Neurophysiological Signals Separation

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