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Published in: International Journal of Machine Learning and Cybernetics 4/2021

11-10-2020 | Original Article

Balanced Graph-based regularized semi-supervised extreme learning machine for EEG classification

Authors: Qingshan She, Jie Zou, Ming Meng, Yingle Fan, Zhizeng Luo

Published in: International Journal of Machine Learning and Cybernetics | Issue 4/2021

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Abstract

Machine learning algorithms play a critical role in electroencephalograpy (EEG)-based brain-computer interface (BCI) systems. However, collecting labeled samples for classifier training and calibration is still difficult and time-consuming, especially for patients. As a promising alternative way to address the problem, semi-supervised learning has attracted much attention by exploiting both labeled and unlabeled samples in the training process. Nowadays, semi-supervised extreme learning machine (SS-ELM) is widely used in EEG classification due to its fast training speed and good generalization performance. However, the classification performance of SS-ELM largely depends on the quality of sample graph. The graphs of most semi-supervised algorithms are constructed by the similarity between labeled and unlabeled data called manifold graph. The more similar the structural information between samples, the greater probability they belong to the same class. In this paper, the label-consistency graph (LCG) and sample-similarity graph (SSG) are combined to constrain the model output. When the SSG is not accurate enough, the weight of LCG needs to be increased, and vice versa. The weight ratio of two graphs is optimized to obtain an optimal adjacency graph, and finally the best output weight vector is achieved. To verify the effectiveness of the proposed algorithm, it was validated and compared with several existing methods on two real datasets: BCI Competition IV Dataset 2a and BCI Competition III Dataset 4a. Experimental results show that our algorithm has achieved the promising results, especially when the number of labeled samples is small.

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Literature
1.
go back to reference Brunner C, Birbaumer N, Blankertz B et al (2015) BNCI Horizon 2020: towards a roadmap for the BCI community. Brain-Comput Interfaces 2(1):1–10CrossRef Brunner C, Birbaumer N, Blankertz B et al (2015) BNCI Horizon 2020: towards a roadmap for the BCI community. Brain-Comput Interfaces 2(1):1–10CrossRef
2.
go back to reference Lotte F, Bougrain L, Cichocki A, Clerc M, Congedo M, Rakotomamonjy A et al (2018) A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update. J Neural Eng 15(3):031005CrossRef Lotte F, Bougrain L, Cichocki A, Clerc M, Congedo M, Rakotomamonjy A et al (2018) A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update. J Neural Eng 15(3):031005CrossRef
3.
go back to reference Tu WT, Sun SL et al (2013) Semi-supervised feature extraction for EEG classification. Pattern Anal Appl 16(2):213–222MathSciNetCrossRef Tu WT, Sun SL et al (2013) Semi-supervised feature extraction for EEG classification. Pattern Anal Appl 16(2):213–222MathSciNetCrossRef
6.
go back to reference Li RH, Potter T, Huang WT, Zhang YC et al (2017) Enhancing performance of a hybrid EEG-FNIRS system using channel selection and early temporal features. Front Hum Neurosci 11:462CrossRef Li RH, Potter T, Huang WT, Zhang YC et al (2017) Enhancing performance of a hybrid EEG-FNIRS system using channel selection and early temporal features. Front Hum Neurosci 11:462CrossRef
7.
go back to reference Liang NY, Saratchandran P, Huang GB et al (2006) Classification of mental tasks from EEG signals using extreme learning machine. Int J Neural Syst 16(01):29–38CrossRef Liang NY, Saratchandran P, Huang GB et al (2006) Classification of mental tasks from EEG signals using extreme learning machine. Int J Neural Syst 16(01):29–38CrossRef
8.
go back to reference Huang GB, Zhu QY, Siew CK et al (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1):489–501CrossRef Huang GB, Zhu QY, Siew CK et al (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1):489–501CrossRef
9.
go back to reference Huang GB, Zhou H, Ding X et al (2012) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern Part B (Cybernetics) 42(2):513–529CrossRef Huang GB, Zhou H, Ding X et al (2012) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern Part B (Cybernetics) 42(2):513–529CrossRef
10.
go back to reference Gu Z, Yu Z, Shen Z et al (2013) An online semi-supervised brain-computer interface. IEEE Trans Biomed Eng 60(9):2614–2623CrossRef Gu Z, Yu Z, Shen Z et al (2013) An online semi-supervised brain-computer interface. IEEE Trans Biomed Eng 60(9):2614–2623CrossRef
11.
go back to reference Zhu X, Goldberg AB (2009) Introduction to semi-supervised learning. Synth Lect Artif Intell Mach Learn 3(1):130MATH Zhu X, Goldberg AB (2009) Introduction to semi-supervised learning. Synth Lect Artif Intell Mach Learn 3(1):130MATH
12.
go back to reference Nicolas-Alonso LF, Corralejo R, Gomez-Pilar J et al (2015) Adaptive semi-supervised classification to reduce intersession non-stationarity in multiclass motor imagery-based brain-computer interfaces. Neurocomputing 159:186–196CrossRef Nicolas-Alonso LF, Corralejo R, Gomez-Pilar J et al (2015) Adaptive semi-supervised classification to reduce intersession non-stationarity in multiclass motor imagery-based brain-computer interfaces. Neurocomputing 159:186–196CrossRef
13.
go back to reference Tian X, Gasso G, Canu S et al (2012) A multiple kernel framework for inductive semi-supervised SVM learning. Neurocomputing 90:46–58CrossRef Tian X, Gasso G, Canu S et al (2012) A multiple kernel framework for inductive semi-supervised SVM learning. Neurocomputing 90:46–58CrossRef
14.
go back to reference Xu H, Plataniotis KN et al (2017) Affective states classification using EEG and semi-supervised deep learning approaches. IEEE International Workshop on Multimedia Signal Processing, 7813351 Xu H, Plataniotis KN et al (2017) Affective states classification using EEG and semi-supervised deep learning approaches. IEEE International Workshop on Multimedia Signal Processing, 7813351
15.
go back to reference Culp M, Michailidis G et al (2008) Graph-based semi-supervised learning. IEEE Trans Pattern Anal Mach Intell 30(1):174–179CrossRef Culp M, Michailidis G et al (2008) Graph-based semi-supervised learning. IEEE Trans Pattern Anal Mach Intell 30(1):174–179CrossRef
16.
go back to reference Liu W, Wang J, Chang SF et al (2012) Robust and scalable graph-based semisupervised learning. Proc IEEE 100(9):2624–2638CrossRef Liu W, Wang J, Chang SF et al (2012) Robust and scalable graph-based semisupervised learning. Proc IEEE 100(9):2624–2638CrossRef
17.
go back to reference Peng Y, Wang S, Long X et al (2015) Discriminative graph regularized extreme learning machine and its application to face recognition. Neurocomputing 149:340–353CrossRef Peng Y, Wang S, Long X et al (2015) Discriminative graph regularized extreme learning machine and its application to face recognition. Neurocomputing 149:340–353CrossRef
18.
go back to reference Chang YC et al (2018) Graph-based data augmentation approach for electroencephalogram analysis. Int J Multidiscip Res Stud 1(3):298–307 Chang YC et al (2018) Graph-based data augmentation approach for electroencephalogram analysis. Int J Multidiscip Res Stud 1(3):298–307
19.
go back to reference Guan G et al (2013) Joint Rayleigh coefficient maximization and graph based semi-supervised for the classification of motor imagery EEG. IEEE International Conference on Information and Automation, pp 379–383 Guan G et al (2013) Joint Rayleigh coefficient maximization and graph based semi-supervised for the classification of motor imagery EEG. IEEE International Conference on Information and Automation, pp 379–383
20.
go back to reference Zhong JY, Xu L, Yao DZ et al (2009) Semi-supervised learning based on manifold in BCI. J Electron Sci Technol 7(1):22–26 Zhong JY, Xu L, Yao DZ et al (2009) Semi-supervised learning based on manifold in BCI. J Electron Sci Technol 7(1):22–26
21.
go back to reference Li YF, Wang SB, Zhou ZH et al (2016) Graph quality judgement: a large margin expedition. International joint conference on artificial intelligence AAAI Press, pp 9–15 Li YF, Wang SB, Zhou ZH et al (2016) Graph quality judgement: a large margin expedition. International joint conference on artificial intelligence AAAI Press, pp 9–15
22.
go back to reference Wang H, Wang SB, Li YF et al (2016) Instance selection method for improving graph-based semi-supervised learning. Proceedings of the 14th Pacific Rim international conference on artificial intelligence, pp 565–573 Wang H, Wang SB, Li YF et al (2016) Instance selection method for improving graph-based semi-supervised learning. Proceedings of the 14th Pacific Rim international conference on artificial intelligence, pp 565–573
23.
go back to reference Yi Y, Qiao S, Zhou W et al (2018) Adaptive multiple graph regularized semi-supervised extreme learning machine. Soft Comput 22(11):3545–3562CrossRef Yi Y, Qiao S, Zhou W et al (2018) Adaptive multiple graph regularized semi-supervised extreme learning machine. Soft Comput 22(11):3545–3562CrossRef
24.
go back to reference Huang G, Song S, Gupta JND et al (2014) Semi-supervised and unsupervised extreme learning machines. IEEE Trans Cybern 44(12):2405–2417CrossRef Huang G, Song S, Gupta JND et al (2014) Semi-supervised and unsupervised extreme learning machines. IEEE Trans Cybern 44(12):2405–2417CrossRef
25.
go back to reference Zhou Y, Liu B, Xia S et al (2015) Semi-supervised extreme learning machine with manifold and pairwise constraints regularization. Neurocomputing 149:180–186CrossRef Zhou Y, Liu B, Xia S et al (2015) Semi-supervised extreme learning machine with manifold and pairwise constraints regularization. Neurocomputing 149:180–186CrossRef
26.
go back to reference She Q, Hu B, Gan H et al (2018) Safe semi-supervised extreme learning machine for EEG signal classification. IEEE Access 6:49399–49407CrossRef She Q, Hu B, Gan H et al (2018) Safe semi-supervised extreme learning machine for EEG signal classification. IEEE Access 6:49399–49407CrossRef
27.
go back to reference Melacci S, Belkin M et al (2011) Laplacian support vector machines trained in the primal. J Mach Learn Res 12(5):1149–1184MathSciNetMATH Melacci S, Belkin M et al (2011) Laplacian support vector machines trained in the primal. J Mach Learn Res 12(5):1149–1184MathSciNetMATH
28.
go back to reference Gan H, Sang N, Huang R et al (2015) Manifold regularized semi-supervised gaussian mixture model. J Opt Soc Am A 32:566–575CrossRef Gan H, Sang N, Huang R et al (2015) Manifold regularized semi-supervised gaussian mixture model. J Opt Soc Am A 32:566–575CrossRef
29.
go back to reference Jebara T, Jun W, Shih-Fu C et al (2009) Graph construction and b-matching for semi-supervised learning. Proceedings of the 26th annual international conference on machine learning, pp 441–448 Jebara T, Jun W, Shih-Fu C et al (2009) Graph construction and b-matching for semi-supervised learning. Proceedings of the 26th annual international conference on machine learning, pp 441–448
30.
go back to reference Gan H, Li Z, Wu W et al (2018) Safety-aware graph-based semi-supervised learning. Expert Syst Appl 107:243–254CrossRef Gan H, Li Z, Wu W et al (2018) Safety-aware graph-based semi-supervised learning. Expert Syst Appl 107:243–254CrossRef
31.
go back to reference Zhou YH, Zhou ZH et al (2016) Large margin distribution learning with cost interval and unlabeled data. IEEE Trans Knowl Data Eng 28:1749–1763CrossRef Zhou YH, Zhou ZH et al (2016) Large margin distribution learning with cost interval and unlabeled data. IEEE Trans Knowl Data Eng 28:1749–1763CrossRef
32.
go back to reference Chen X, Wang T (2017) Combining active learning and semi-supervised learning by using selective label spreading. IEEE international conference on data mining workshops, New Orleans, USA, 17448855 Chen X, Wang T (2017) Combining active learning and semi-supervised learning by using selective label spreading. IEEE international conference on data mining workshops, New Orleans, USA, 17448855
33.
go back to reference Nie F, Li J, Li X et al (2016) Parameter-free auto-weighted multiple graph learning: a framework for multiview clustering and semi-supervised classification. International joint conference on artificial intelligence AAAI Press, New York, USA, pp 1881–1887 Nie F, Li J, Li X et al (2016) Parameter-free auto-weighted multiple graph learning: a framework for multiview clustering and semi-supervised classification. International joint conference on artificial intelligence AAAI Press, New York, USA, pp 1881–1887
34.
go back to reference Song J, Gao L et al (2016) Optimized graph learning using partial tags and multiple features for image and video annotation. IEEE Trans Image Process 25(11):4999–5011MathSciNetCrossRef Song J, Gao L et al (2016) Optimized graph learning using partial tags and multiple features for image and video annotation. IEEE Trans Image Process 25(11):4999–5011MathSciNetCrossRef
35.
go back to reference Biggs N et al (1998) Spectral graph theory. Bull Lond Math Soc 30(2):196–223CrossRef Biggs N et al (1998) Spectral graph theory. Bull Lond Math Soc 30(2):196–223CrossRef
36.
go back to reference Joachims T et al (1999) Transductive inference for text classification using support vector machines. Proceedings of the 16th international conference on machine learning, San Francisco, USA, 99: 200–209 Joachims T et al (1999) Transductive inference for text classification using support vector machines. Proceedings of the 16th international conference on machine learning, San Francisco, USA, 99: 200–209
37.
go back to reference Tangermann M, Muller KR, Aertsen A, Birbaumer N, Braun C, Brunner C et al (2012) Review of the BCI competition IV. Front Neurosci 6:55CrossRef Tangermann M, Muller KR, Aertsen A, Birbaumer N, Braun C, Brunner C et al (2012) Review of the BCI competition IV. Front Neurosci 6:55CrossRef
38.
go back to reference Ang KK, Chin ZY, Wang CC, Guan CT, Zhang HH et al (2012) Filter bank common spatial pattern algorithm on BCI competition IV datasets 2a and 2b. Front Neurosci 6:39CrossRef Ang KK, Chin ZY, Wang CC, Guan CT, Zhang HH et al (2012) Filter bank common spatial pattern algorithm on BCI competition IV datasets 2a and 2b. Front Neurosci 6:39CrossRef
39.
go back to reference Meng M, Zhu JQ, She QS, Ma YL et al (2016) Two-level feature extraction method for multi-class motor imagery EEG. Acta Automatica Sinica 42:1915–1922 Meng M, Zhu JQ, She QS, Ma YL et al (2016) Two-level feature extraction method for multi-class motor imagery EEG. Acta Automatica Sinica 42:1915–1922
40.
go back to reference Blankertz B, Klaus-Robert M, Krusienski DJ et al (2006) The BCI competition III: validating alternative approaches to actual BCI problems. IEEE Trans Neural Syst Rehabil Eng 14(2):153–159CrossRef Blankertz B, Klaus-Robert M, Krusienski DJ et al (2006) The BCI competition III: validating alternative approaches to actual BCI problems. IEEE Trans Neural Syst Rehabil Eng 14(2):153–159CrossRef
41.
go back to reference Gan HT, Sang N, Huang R et al (2015) Manifold regularized semi-supervised gaussian mixture model. J Opt Soc Am A-Opt Image Sci Vis 32:566–575CrossRef Gan HT, Sang N, Huang R et al (2015) Manifold regularized semi-supervised gaussian mixture model. J Opt Soc Am A-Opt Image Sci Vis 32:566–575CrossRef
42.
go back to reference Gan HT, Luo ZZ, Meng M, Ma YL, She QS et al (2016) A risk degree-based safe semi-supervised learning algorithm. Int J Mach Learn Cybern 7:85–94CrossRef Gan HT, Luo ZZ, Meng M, Ma YL, She QS et al (2016) A risk degree-based safe semi-supervised learning algorithm. Int J Mach Learn Cybern 7:85–94CrossRef
43.
go back to reference Hamilton W L, Ying R, Leskovec J et al (2017) Inductive representation learning on large graphs. Proceedings of the 31th Conference on Neural Information Processing Systems, Long Beach, CA, USA, pp 1024–1034 Hamilton W L, Ying R, Leskovec J et al (2017) Inductive representation learning on large graphs. Proceedings of the 31th Conference on Neural Information Processing Systems, Long Beach, CA, USA, pp 1024–1034
Metadata
Title
Balanced Graph-based regularized semi-supervised extreme learning machine for EEG classification
Authors
Qingshan She
Jie Zou
Ming Meng
Yingle Fan
Zhizeng Luo
Publication date
11-10-2020
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 4/2021
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-020-01209-0

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