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Correction for Model Selection Bias Using a Modified Model Averaging Approach for Supervised Learning Methods Applied to EEG Experiments

机译：使用修改模型选择偏置的模型选择偏差校正，用于监督的学习方法应用于EEG实验

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Kristien Wouters; José Cortiñas Abrahantes; Geert Molenberghs; Helena Geys; Abdellah Ahnaou; Wilhelmus H. I. M. Drinkenburg; Luc Bijnens;
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1. Correction for model selection bias using a modified model averaging approach for supervised learning methods applied to EEG experiments [J] . Wouters K., Abrahantes J.C., Molenberghs G., Journal of biopharmaceutical statistics . 2010,第4期

机译：使用改进的模型平均方法对适用于EEG实验的监督学习方法进行模型选择偏差的校正
2. Correction for Model Selection Bias Using a Modified Model Averaging Approach for Supervised Learning Methods Applied to EEG Experiments [J] . Kristien Woutersa* JosÃ© CortiÃ±as Abrahantesa Geert Molenberghsa Helena Geysab Abdellah Ahnaoub Wilhelmus H. I. M. Drinkenburgb, Luc Bijnensb Journal of Biopharmaceutical Statistics . 2010,第4期

机译：使用改进的模型平均方法对适用于EEG实验的监督学习方法进行模型选择偏差的校正
3. New Statistical Methods for Precipitation Bias Correction Applied to WRF Model Simulations in the Antisana Region, Ecuador [J] . Heredia Maria Belen, Junquas Clementine, Prieur Clementine, Journal of hydrometeorology . 2018,第12期

机译：厄瓜多尔抗苏拉纳地区WRF模型模拟的降水偏压校正的新统计方法
4. APPLICATION OF TIME-FREQUENCY ANALYSIS METHOD FOR BIAS CORRECTION OF DAILY PRECIPITATION OUTPUT FROM CLIMATE MODELS WITH COMPARIING VARIOUS BIAS CORRECTION METHODS [C] . 地球環境シンポジウム講演集 . 2016

机译：时频分析方法在气候模式日常降水量偏差校正中的应用及各种偏差校正方法的比较
5. Probabilistic modeling of decompression sickness, comparative hydrodynamics of cetacean flippers, optimization of CT/MRI protocols and evaluation of modified angiocatheters: Engineering methods applied to a diverse assemblage of projects. [D] . Weber, Paul William. 2010

机译：减压病的概率模型，鲸类鳍状肢的比较流体力学，CT / MRI方案的优化和改良血管导管的评估：工程方法应用于各种项目。
6. Supervised machine learning models applied to disease diagnosis and prognosis [O] . Maria C Mariani, Osei K Tweneboah, Md Al Masum Bhuiyan 2019

机译：有监督的机器学习模型应用于疾病的诊断和预后
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. Elements of a Pragmatic Approach for Dealing with Bias and Uncertainty in Experiments through Predictions: Experiment Design and Data Conditioning; 'Real Space' Model Validation and Conditioning; and Hierarchical Modeling and Extrapolative Prediction [R] . Romero, V. 2011

机译：通过预测处理实验中的偏差和不确定性的实用方法的要素：实验设计和数据调节; '真实空间'模型验证和调节;和层次建模与外推预测

Correction for Model Selection Bias Using a Modified Model Averaging Approach for Supervised Learning Methods Applied to EEG Experiments

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