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CenEEGs: Valid EEG Selection for Classification

Authors:

Stefanie I. Becker,

Blake JohnsonAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 14, Issue 2

Article No.: 18, Pages 1 - 25

https://doi.org/10.1145/3371153

Published: 18 February 2020 Publication History

Abstract

This article explores valid brain electroencephalography (EEG) selection for EEG classification with different classifiers, which has been rarely addressed in previous studies and is mostly ignored by existing EEG processing methods and applications. Importantly, traditional selection methods are not able to select valid EEG signals for different classifiers. This article focuses on a source control-based valid EEG selection to reduce the impact of invalid EEG signals and aims to improve EEG-based classification performance for different classifiers. We propose a novel centroid-based EEG selection approach named CenEEGs, which uses a scale-and-shift-invariance similarity metric to measure similarities of EEG signals and then applies a globally optimal centroid strategy to select valid EEG signals with respect to a similarity threshold. A detailed comparison with several state-of-the-art time series selection methods by using standard criteria on 8 EEG datasets demonstrates the efficacy and superiority of CenEEGs for different classifiers.

References

[1]

Anna Aminov, Jeffrey M. Rogers, Stuart J. Johnstone, Sandy Middleton, and Peter H. Wilson. 2017. Acute single channel EEG predictors of cognitive function after stroke. PLOS One 12, 10 (2017), Article e0185841.

[2]

Joakim Andén and Stéphane Mallat. 2014. Deep scattering spectrum. IEEE Transactions on Signal Processing 62, 16 (2014), 4114--4128.

[3]

Kai Keng Ang and Cuntai Guan. 2017. EEG-based strategies to detect motor imagery for control and rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation on Engineering 25, 4 (2017), 392--401.

[4]

Álvar Arnaiz-González, José F. Díez-Pastor, Juan J. Rodríguez, and César Ignacio García-Osorio. 2016. Instance selection for regression by discretization. Expert Systems with Applications 54 (2016), 340--350.

Digital Library

[5]

Claudio Babiloni, Claudio Del Percio, Roberta Lizio, Giuseppe Noce, Susanna Lopez, Andrea Soricelli, Raffaele Ferri, Flavio Nobili, Dario Arnaldi, Francesco Fama, Dag Aarsland, Francesco Orzi, Carla Buttinelli, Franco Giubilei, Marco Onofrj, Fabrizio Stocchi, Paola Stirpe, Peter Fuhr, Ute Gschwandtne, jGerhard Ransmayr, Heinrich Garn, Lucia Fraioli, Michela Pievani, Giovanni B. Frisoni, Fabrizia D’Antonio, Carlo De Lena, Bahar Guntekin, Lutfu Hanoglu, Erol Basar, Gorsev Yener, Derya Durusu Emek-Savas, Antonio Ivano Triggiani, Raffaella Franciotti, John Paul Taylor, Laura Vacca, Maria Francesca De Pandis, and Laura Bonanni. 2018. Abnormalities of resting-state functional cortical connectivity in patients with dementia due to Alzheimer’s and Lewy body diseases: An EEG study. Neurobiology of Aging 65 (2018), 18--40.

[6]

Anthony Bagnall, Jason Lines, Jon Hills, and Aaron Bostrom. 2015. Time-series classification with COTE: The collective of transformation-based ensembles. IEEE Transactions on Knowledge and Data Engineering 27, 9 (2015), 2522--2535.

Digital Library

[7]

James C. Bezdek and Ludmila I. Kuncheva. 2001. Nearest prototype classifier designs: An experimental study. International Journal of Intelligent Systems 16, 12 (2001), 1445--1473.

[8]

Eric Billet, Andriy Fedorov, and Nikos Chrisochoides. 2008. The use of robust local Hausdorff distances in accuracy assessment for image alignment of brain MRI. The Insight Journal (2008). http://hdl.handle.net/1926/1354.

[9]

J. Caicedo-Acosta, D. Cárdenas-Pena, D. Collazos-Huertas, J. I. Padilla-Buritica, G. Castano-Duque, and G. Castellanos-Dominguez. 2019. Multiple-instance lasso regularization via embedded instance selection for emotion recognition. In Proceedings of International Work-Conference on the Interplay Between Natural and Artificial Computation (IWINAC’19). Springer, Almería, Spain, 244--251.

[10]

Julian Caicedo-Acosta, Luisa Velasquez-Martinez, David Cárdenas-Pena, and Germán Castellanos-Dominguez. 2018. Multiple instance learning selecting time-frequency features for Brain Computing Interfaces. In Proceedings of International Workshop on Artificial Intelligence and Pattern Recognition (IWAIPR’18). Springer, Havana, Cuba, 326--333.

[11]

James F. Cavanagh, Praveen Kumar, Andrea A. Mueller, Sarah Pirio Richardson, and Abdullah Mueen. 2018. Diminished EEG habituation to novel events effectively classifies Parkinson’s patients. Clinical Neurophysiology 129, 2 (2018), 409--418.

[12]

Chih-Chung Chang and Chih-Jen Lin. 2011. LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2, 3 (2011), Article 27.

Digital Library

[13]

Kelvin Kam Wing Chu and Man Hon Wong. 1999. Fast time-series searching with scaling and shifting. In Proceedings of the 18th ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems. ACM, Philadelphia, PA, 237--248.

[14]

Václav Chudác̆ek, Joakim Andén, Stéphane Mallat, Patrice Abry, and Muriel Doret. 2014. Scattering transform for intrapartum fetal heart rate variability fractal analysis: A case-control study. IEEE Transactions on Biomedical Engineering 61, 4 (2014), 1100--1108.

[15]

Chenglong Dai, Dechang Pi, Lin Cui, and Yanlong Zhu. 2018. MTEEGC: A novel approach for multi-trial EEG clustering. Applied Soft Computing 71 (2018), 255--267.

[16]

Chenglong Dai, Jia Wu, Dechang Pi, and Lin Cui. 2018. Brain EEG time series selection: A novel graph-based approach for classification. In Proceedings of SIAM International Conference on Data Ming (SDM’18). SIAM, San Diego, CA, 558--566.

[17]

Anne Driemel, Amer Krivos̎ija, and Christian Sohler. 2016. Clustering time series under the Fréchet distance. In Proceedings of the 27th Annual ACM-SIAM Symposium on Discrete algorithms (SODA’16). SIAM, Arlington, Virginia, 766--785.

[18]

Christos Faloutsos, M. Ranganathan, and Yannis Manolopoulos. 1994. Fast sub-sequence matching in time-series databases. In Proceedings of the 1994 ACM SIGMOD International Conference on Management of Data (SIGMOD’94). ACM, Minneapolis, Minnesota, 419--429.

Digital Library

[19]

Matteo Fraschini, Matteo Demuru, Arjan Hillebrand, Lorenza Cuccu, Silvia Porcu, Francesca Di Stefano, Monica Puligheddu, Gianluca Floris, Giuseppe Borghero, and Francesco Marrosu. 2016. EEG functional network topology is associated with disability in patients with amyotrophic lateral sclerosis. Scientific Reports 6 (2016), Article 38653.

[20]

Salvador Garcia, Joaquin Derrac, Jose Cano, and Francisco Herrera. 2012. Prototype selection for nearest neighbor classification: Taxonomy and empirical study. IEEE Transactions on Pattern Analysis and Machine Intelligence 34, 3 (2012), 417--435.

Digital Library

[21]

Matthew F. Glasser, Timothy S. Coalson, Emma C. Robinson, Carl D. Hacker, John Harwell, Essa Yacoub, Kamil Ugurbil, Jesper Andersson, Christian F. Beckmann, Mark Jenkinson, Stephen M. Smith, and David C. Van Essen. 2016. A multi-modal parcellation of human cerebral cortex. Nature 536 (2016), 171--178.

[22]

Gene H. Golub and Charles F. Van Loan. 1996. Matrix Computations (3rd ed.). Johns Hopkins University Press, Baltimore, Maryland.

Digital Library

[23]

Guoliang He, Yong Duan, Yifei Li, Tieyun Qian, Jinrong He, and Xiangyang Jia. 2015. Active learning for multivariate time series classification with positive unlabeled data. In Proceedings of 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI’16). IEEE, Vietri sul Mare, Italy, 178--185.

Digital Library

[24]

Ramy Hussein, Mohamed Elgendi, Z. Jane Wang, and Rabab K. Ward. 2018. Robust detection of epileptic seizures based on L1-penalized robust regression of EEG signals. Expert Systems with Applications 104 (2018), 153--167.

[25]

Norbert Jankowski and Marek Grochowski. 2004. Comparison of instances selection Algorithms I. Algorithms survey. In Proceedings of Artificial Intelligence and Soft Computing (ICAISC’04). Springer, Zakopane, Poland, 598--603.

[26]

Liangxiao Jiang. 2012. Learning instance weighted naive Bayes from labeled and unlabeled data. Journal of Intelligent Information Systems 38 (2012), 257--268.

Digital Library

[27]

Eamonn Keogh and Chotirat Ann Ratanamahatana. 2005. Exact indexing of dynamic time warping. Knowledge and Information Systems 7, 3 (2005), 358--386.

Digital Library

[28]

Ji-Hyun Kim. 2009. Estimating classification error rate: Repeated cross-validation, repeated hold-out and bootstrap. Computational Statistics and Data Analysis 53, 11 (2009), 3735--3745.

Digital Library

[29]

Wai Lam, Chi-Kin Keung, and Danyu Liu. 2002. Discovering useful concept prototypes for classification based on filtering and abstraction. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 8 (2002), 1075--1090.

Digital Library

[30]

Jessica Lin, Eamonn Keogh, Li Wei, and Stefano Lonardi. 2007. Experiencing sax: A novel symbolic representation of time series. Data Mining and Knowledge Discovery 15, 2 (2007), 107--144.

Digital Library

[31]

Jessica Lin, Michail Vlachos, Eamonn Keogh, and Dimitrios Gunopulos. 2004. Iterative incremental clustering of time series. In Proceedings of International Conference on Extending Database Technology (EDBT’04). Springer, Heraklion, Crete, Greece, 106--122.

[32]

Jason Lines, Luke M. Davis, Jon Hills, and Anthony Bagnall. 2012. A shapelet transform for time series classification. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’12). ACM, Beijing, China, 289--297.

Digital Library

[33]

Jason Lines, Sarah Taylor, and Anthony Bagnall. 2018. Time series classification with HIVE-COTE: The hierachical vote collective of transformation-based ensembles. ACM Transactions on Knowledge Discovery from Data 12, 5 (2018), Article 52.

Digital Library

[34]

Chuan Liu, Wenyong Wang, Meng Wang, Fengmao Lv, and Martin Konan. 2017. An efficient instance selection algorithm to reconstruct training set for support vector machine. Knowledge-Based Systems 116 (2017), 58--73.

Digital Library

[35]

Li Liu, Jiasong Wu, Dengwang Li, Lotfi Senhadji, and Huazhong Shu. 2019. Fractional wavelet scattering network and applications. IEEE Transactions on Biomedical Engineering 66, 2 (2019), 553--563.

[36]

Eduardo Zárate Max, Ricardo Marcondes Marcacini, and Edson Takashi Matsubara. 2018. Improving instance selection via metric learning. In Proceedings of 2018 International Joint Conference on Neural Networks (IJCNN’18). IEEE, Rio de Janeiro, Brazil, 1--6.

[37]

Thach Le Nguyen, Severin Gsponer, and Georgiana Ifrim. 2017. Time series classification by sequence learning in all-subsequence space. In Proceedings of 2017 IEEE 33rd International Conference on Data Engineering (ICDE’17). IEEE, San Diego, CA, 947--958.

[38]

John Paparrizos and Luis Gravano. 2016. k-Shape: Efficient and accurate clustering of time series. ACM SIGMOD Record 45, 1 (2016), 69--76.

Digital Library

[39]

Thanawin Rakthanmanon, Bilson Campana, Abdullah Mueen, Gustavo Batista, Brandon Westover, Qiang Zhu, Jesin Zakaria, and Eamonn Keogh. 2013. Addressing big data time series: Mining trillions of time series subsequences under dynamic time warping. ACM Transactions on Knowledge Discovery from Data 7, 3 (SI) (2013), Article 10.

[40]

Diego F. Silva, Viníus M. A. De Souza, and Gustavo E. A. P. A. Batista. 2013. Time series classification using compression distance of recurrence plots. In Proceedings of 2013 IEEE 13th International Conference on Data Mining (ICDM’13). IEEE, Dallas, TX, 687--696.

[41]

Siuly and Yan Li. 2014. A novel statistical algorithm for multiclass EEG signal classification. Engineering Applications of Artificial Intelligence 34 (2014), 154--167.

[42]

Otis Smart and Lauren Burrell. 2015. Genetic programming and frequent itemset mining to identify feature selection patterns of iEEG and fMRI epilepsy data. Engineering Applications of Artificial Intelligence 39 (2015), 198--214.

[43]

Yunsheng Song, Jiye Liang, Jing Lu, and Xingwang Zhao. 2017. An efficient instance selection algorithm for k-nearest neighbor regression. Neurocomputing 251 (2017), 26--34.

Digital Library

[44]

Milos B. Stojanović, Milos M. Bozić, Milena M. Stanković, and Zoran P. Stajić. 2014. A methodology for training set instance selection using mutual information in time series prediction. Neurocomputing 141, SI (2014), 236--245.

[45]

Abdel Aziz Taha and Allan Hanbury. 2015. An efficient algorithm for calculating the exact Hausdorff distance. IEEE Transactions on Pattern Analysis and Machine Intelligence 37, 11 (2015), 2153--2163.

Digital Library

[46]

Anthoula C. Tsolaki, Vasiliki Kosmidou, Ioannis (Yiannis) Kompatsiaris, Chrysa Papadaniil, Leontios Hadjileontiadis, Aikaterini Adam, and Magda Tsolaki. 2017. Brain source localization of MMN and P300 ERPs in mild cognitive impairment and Alzheimer’s disease: A high-density EEG approach. Neurobiology of Aging 55 (2017), 190--201.

[47]

D. Randall Wilson and Tony R. Martinez. 2000. Reduction techniques for instance-based learning algorithms. Machine Learning 38, 3 (2000), 257--286.

Digital Library

[48]

Jia Wu, Shirui Pan, Zhihua Cai, Xingquan Zhu, and Chengqi Zhang. 2014. Dual instance and attribute weighting for naive Bayes classification. In Proceedings of 2014 International Joint Conference on Neural Networks (IJCNN’14). IEEE, Beijing, China, 1675--1679.

[49]

Jaewon Yang and Jure Leskovec. 2011. Patterns of temporal variation in online media. In Proceedings of the 4th ACM International Conference on Web Search and Data Mining (WSDM’11). ACM, Hong Kong, China, 177--186.

Digital Library

[50]

Lijun Yang, Qingsheng Zhu, Jinlong Huang, Donggong Cheng, Quanwang Wu, and Xiaolu Hong. 2018. Natural neighborhood graph-based instance reduction algorithm without parameters. Applied Soft Computing 70 (2018), 279--287.

[51]

Zhang Yin, Yongxiong Wang, Li Liu, Wei Zhang, and Jianhua Zhang. 2017. Cross-subject EEG feature selection for emotion recognition using transfer recursive feature elimination. Frontiers in Neurorobotics 11 (2017), Article 19.

[52]

Tingting Zhai and Zhenfeng He. 2013. Instance selection for time series classification based on immune binary particle swarm optimization. Knowledge-Based Systems 49 (2013), 106--115.

Digital Library

[53]

Shichao Zhang, Xuelong Li, Min Zong, Xiaofeng Zhu, and Debo Cheng. 2017. Learning k for kNN classification. ACM Transactions on Intelligent Systems and Technology 8, 3 (SI) (2017), Article 43.

Digital Library

Cited By

Luo T(2024)Selective multi–view time–frequency decomposed spatial feature matrix for motor imagery EEG classificationExpert Systems with Applications10.1016/j.eswa.2024.123239247(123239)Online publication date: Aug-2024
https://doi.org/10.1016/j.eswa.2024.123239
Abdullah Faye IYusoff MBelhaouari S(2024)G-EEGCS: Graph-based optimum electroencephalogram channel selectionBiomedical Signal Processing and Control10.1016/j.bspc.2024.10676398(106763)Online publication date: Dec-2024
https://doi.org/10.1016/j.bspc.2024.106763
Miranda-Pascual ÀGuerra-Balboa PParra-Arnau JForné JStrufe T(2024)An overview of proposals towards the privacy-preserving publication of trajectory dataInternational Journal of Information Security10.1007/s10207-024-00894-023:6(3711-3747)Online publication date: 4-Sep-2024
https://dl.acm.org/doi/10.1007/s10207-024-00894-0
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Index Terms

CenEEGs: Valid EEG Selection for Classification
1. Applied computing
  1. Life and medical sciences
    1. Bioinformatics
2. Computing methodologies
  1. Machine learning
    1. Machine learning algorithms
      1. Feature selection
    2. Machine learning approaches
      1. Classification and regression trees
      2. Instance-based learning

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 14, Issue 2

April 2020

322 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3382774

Editors:
Charu Aggarwal
IBM T. J. Watson Research, USA
,
Xindong Wu
Minginglamp Academy of Sciences, China

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Copyright © 2020 ACM.

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Publication History

Published: 18 February 2020

Accepted: 01 November 2019

Revised: 01 September 2019

Received: 01 February 2019

Published in TKDD Volume 14, Issue 2

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National Natural Science Foundation of China
Fundamental Research Funds for the Central Universities
Funding of Jiangsu Innovation Program for Graduate Education

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Cited By

Luo T(2024)Selective multi–view time–frequency decomposed spatial feature matrix for motor imagery EEG classificationExpert Systems with Applications10.1016/j.eswa.2024.123239247(123239)Online publication date: Aug-2024
https://doi.org/10.1016/j.eswa.2024.123239
Abdullah Faye IYusoff MBelhaouari S(2024)G-EEGCS: Graph-based optimum electroencephalogram channel selectionBiomedical Signal Processing and Control10.1016/j.bspc.2024.10676398(106763)Online publication date: Dec-2024
https://doi.org/10.1016/j.bspc.2024.106763
Miranda-Pascual ÀGuerra-Balboa PParra-Arnau JForné JStrufe T(2024)An overview of proposals towards the privacy-preserving publication of trajectory dataInternational Journal of Information Security10.1007/s10207-024-00894-023:6(3711-3747)Online publication date: 4-Sep-2024
https://dl.acm.org/doi/10.1007/s10207-024-00894-0
Ge YBertino EWang HCao JZhang Y(2023)Distributed Cooperative Coevolution of Data Publishing Privacy and TransparencyACM Transactions on Knowledge Discovery from Data10.1145/361396218:1(1-23)Online publication date: 6-Sep-2023
https://dl.acm.org/doi/10.1145/3613962
Dai CWu JPi DCui LJohnson BBecker S(2022)Electroencephalogram Signal Clustering With Convex Cooperative GamesIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.306074234:12(5755-5769)Online publication date: 1-Dec-2022
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Wang SNiu XFournier-Viger PZhou DMin F(2022)A graph based approach for mining significant places in trajectory dataInformation Sciences: an International Journal10.1016/j.ins.2022.07.046609:C(172-194)Online publication date: 1-Sep-2022
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