Clustering of EEG occipital signals using k-means

机译：使用k均值的脑电枕信号聚类

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Recent studies show that it is feasible to use electrical signals from Electro-encephalography (EEG) to control devices or prostheses, these signals are provided by the body and can be measured on the scalp to determine the intent of the person when it is observing a visual stimulus frequency range detectable by the human eye. This group of signals are very susceptible to noise due to voltage levels that are able to acquire. Therefore, in this work we propose a statistical analysis of the distribution of normal EEG signals in order to determine the need of a pre-processing to remove noise components from electrical grids or other possible sources. This preprocessing includes the design and use of a filter that will eliminate any signal component that is not in the operating frequency range of the EEG occipital area of the brain. Finally, we will proceed to use the k-means algorithm to cluster with signals according to their frequency and temporal characteristics.

机译：最近的研究表明，使用来自脑电图（EEG）的电信号来控制设备或假体是可行的，这些信号由身体提供，可以在头皮上进行测量，以确定人在观察人体时的意图。人眼可以检测到的视觉刺激频率范围。由于能够采集的电压电平，这组信号非常容易受到噪声的影响。因此，在这项工作中，我们建议对正常EEG信号的分布进行统计分析，以确定需要进行预处理以消除电网或其他可能来源的噪声成分的需求。这种预处理包括滤波器的设计和使用，该滤波器将消除不在脑EEG枕骨区域的工作频率范围内的任何信号分量。最后，我们将根据信号的频率和时间特性，使用k-means算法对信号进行聚类。

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来源
《2016 IEEE Ecuador Technical Chapters Meeting》|2016年|1-5|共5页
会议地点 Guayaquil(EC)
作者
Víctor Asanza; Kerly Ochoa; Christian Sacarelo; Carlos Salazar; Francis Loayza; Carmen Vaca; Enrique Peláez;
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作者单位

Escuela Superior Politécnica del Litoral, ESPOL, Facultad de Ingeniería en Electricidad y Computación, Centro de Tecnología de información, Campus Gustavo Galindo Km 30.5 Vía Perimetral, P.O. Box 09-01-5863, Guayaquil, Ecuador;

Escuela Superior Politécnica del Litoral, ESPOL, Facultad de Ingeniería en Electricidad y Computación, Centro de Tecnología de información, Campus Gustavo Galindo Km 30.5 Vía Perimetral, P.O. Box 09-01-5863, Guayaquil, Ecuador;

Escuela Superior Politécnica del Litoral, ESPOL, Facultad de Ingeniería en Electricidad y Computación, Centro de Tecnología de información, Campus Gustavo Galindo Km 30.5 Vía Perimetral, P.O. Box 09-01-5863, Guayaquil, Ecuador;

Escuela Superior Politécnica del Litoral, ESPOL, Facultad de Ingeniería en Electricidad y Computación, Centro de Tecnología de información, Campus Gustavo Galindo Km 30.5 Vía Perimetral, P.O. Box 09-01-5863, Guayaquil, Ecuador;

Escuela Superior Politécnica del Litoral, ESPOL, Facultad de Ingeniería en Electricidad y Computación, Centro de Tecnología de información, Campus Gustavo Galindo Km 30.5 Vía Perimetral, P.O. Box 09-01-5863, Guayaquil, Ecuador;

Escuela Superior Politécnica del Litoral, ESPOL, Facultad de Ingeniería en Electricidad y Computación, Centro de Tecnología de información, Campus Gustavo Galindo Km 30.5 Vía Perimetral, P.O. Box 09-01-5863, Guayaquil, Ecuador;

Escuela Superior Politécnica del Litoral, ESPOL, Facultad de Ingeniería en Electricidad y Computación, Centro de Tecnología de información, Campus Gustavo Galindo Km 30.5 Vía Perimetral, P.O. Box 09-01-5863, Guayaquil, Ecuador;

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原文格式 PDF
正文语种 eng
中图分类
关键词
Electroencephalography; Electrodes; Visualization; Gaussian distribution; Time-frequency analysis; Clustering algorithms; Scalp;

机译：脑电图;电极;可视化;高斯分布;时频分析;聚类算法;头皮;

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2. Smart System to Recognize EEG Signal for Finding Brain Diseases Using K-Means Clustering [J] . K.Gomathi, D.Leela, S.Prasad International Journal of Advanced Computer Research . 2013,第13期

机译：使用K均值聚类的智能系统识别脑电信号以查找脑部疾病
3. Classifying Epileptic EEG Signals with Delay Permutation Entropy and Multi-scale K-Means [J] . Guohun Zhu, Yan Li, Peng (Paul) Wen, Advances in Experimental Medicine and Biology . 2015,第Null期

机译：用延迟排列熵和多尺度K均值对癫痫性脑电信号进行分类
4. Classifying Epileptic EEG Signals with Delay Permutation Entropy and Multi-scale K-Means [J] . Guohun Zhu, Yan Li, Peng (Paul) Wen, Advances in Experimental Medicine and Biology . 2015,第Null期

机译：使用延迟置换熵和多尺度K均值进行分类癫痫脑电图信号
5. Clustering of EEG occipital signals using k-means [C] . Víctor Asanza, Kerly Ochoa, Christian Sacarelo, IEEE Ecuador Technical Chapters Meeting . 2016

机译：使用K-Means的EEG枕部信号聚类
6. Automated Parsing of Flexible Molecular Systems Using Principal Component Analysis and K-Means Clustering Techniques [D] . Nwerem, Matthew Jonathan Chukwunenye. 2021

机译：使用主成分分析和K-Means聚类技术自动解析灵活分子系统
7. Bilinguals Have More Complex EEG Brain Signals in Occipital Regions than Monolinguals [O] . John G. Grundy, John A. E. Anderson, Ellen Bialystok -1

机译：双语者在枕叶区域比单语者具有更复杂的脑电图脑信号
8. 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 EEG occipital signals using k-means

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