An Investigation of Ensemble Methods to Classify Electroencephalogram Signaling Modes

机译：集合方法对脑电图信号传导模式进行分类的研究

获取原文

页面导航

摘要
著录项
相似文献
相关主题

摘要

This research focuses on the feasibility of synthetic algorithms, including Boosted Trees, Bagged Trees, Subspace KNN, Subspace Discriminant, RUSBoosted Trees for identifying brain wave signal patterns. With two datasets used, it is the one that measures the four types of human emotions (valence, arousal, dominance, like). The receiver consists of 11 states composed of the groups of facial, normal, and thinking signals. The research focuses on researching the above algorithms, using the wavelet transform to determine the signal's characteristics, then classifying, comparing the results, improving, and reaching a conclusion.

机译：本研究重点介绍了合成算法的可行性，包括升级树木，袋装树木，子空间knn，子空间判别，用于识别脑波信号模式的鲁布硬化树。使用两个数据集使用，它是衡量四种人类情绪的人（价值，唤醒，优势，喜欢）。接收器由11个州组成，由面部，正常和思维信号组组成。研究侧重于研究上述算法，使用小波变换来确定信号的特性，然后进行分类，比较结果，改进和达到结论。

著录项

来源
《NAFOSTED Conference on Information and Computer Science》|2020年|203-208|共6页
会议地点
作者
Hoang-Thuy-Tien Vo; Viet Quoc Huynh; Tuan Van Huynh;
展开▼
作者单位

展开▼
会议组织
原文格式 PDF
正文语种
中图分类
关键词
Wavelet transforms; Solid modeling; Brain modeling; Solids; Electroencephalography; Data models; Testing;

机译：小波变换;实体建模;脑建模;固体;脑电图;数据模型;测试;

相似文献

外文文献
中文文献
专利

1. Classifying Electroencephalogram (EEG) Signals Using BAT-SVM Classifier for Detecting Epilepsy [J] . Manal Tantawi, Aya Naser, Howida Shedeed, International Journal of Service Science, Management, Engineering, and Technology . 2021,第3期

机译：使用BAT-SVM分类器进行分类脑电图（EEG）信号进行检测癫痫
2. A Framework for Epilepsy Classification Using Modified Sparse Representation Classifiers and Naive Bayesian Classifier from Electroencephalogram Signals [J] . Rajaguru Harikumar, Prabhakar Sunil Kumar Journal of Medical Imaging and Health Informatics . 2016,第8期

机译：使用来自脑电图信号的修改稀疏表示分类器和朴素贝叶斯分类器的癫痫分类框架
3. Adaptive filtering of electroencephalogram signals using the empirical-modes method [J] . Grubov V. V., Runnova A. E., Koronovskii A. A., Technical physics letters: Letters to the Russian journal of applied physics . 2017,第7期

机译：使用经验模式法的脑电图信号自适应滤波
4. Ensemble Learning Methods for Classifying EEG Signals [C] . Shiliang Sun International Workshop on Multiple Classifier Systems(MCS 2007); 20070523-25; Prague(CZ) . 2007

机译：综合学习方法对脑电信号进行分类
5. Detecting Prominent Features and Classifying Network Traffic for Securing Internet of Things Based on Ensemble Methods [D] . Ponneganti, Ramu. 2019

机译：检测突出的特征和分类网络流量，用于基于集合方法保护内容互联网
6. A Novel Method for Differential Prognosis of Brain Degenerative Diseases Using Radiomics-Based Textural Analysis and Ensemble Learning Classifiers [O] . Manju Jain, C. S. Rai, Jai Jain 2021

机译：基于辐射族的纹理分析和集合学习分类的脑退行性疾病差异预后的一种新方法
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）

An Investigation of Ensemble Methods to Classify Electroencephalogram Signaling Modes

摘要

著录项

相似文献

相关主题

期刊订阅