Abstract
To effectively identify electroencephalogram (EEG) signals in multiple-source domains, a multiple-source transfer learning-based Takagi–Sugeno–Kang (TSK) fuzzy system (FS), called MST-TSK, is proposed, which combines multiple-source transfer learning and manifold regularization (MR) learning mechanisms together into the TSK-FS framework. Specifically, the advantages of MST-TSK include the following: (1) by evaluating the significance of each source domain (SD), a flexible domain entropy-weighting index is presented; (2) using the theory of sample transfer learning, a reweighting strategy is presented to weigh the prediction of unknown samples in the target domain (TD) and the output of the source prediction functions; (3) by taking into account the MR term, the manifold structure of the TD is effectively maintained in the proposed system; and (4) by inheriting the interpretability of TSK-FS, MST-TSK displays good interpretability in identifying EEG signals that are understandable by humans (domain experts). The effectiveness of the proposed FS is demonstrated in several EEG multiple-source transfer learning tasks.
- E. Kabir and Y. Zhang. 2016. Epileptic seizure detection from EEG signals using logistic model trees. Brain Informatics 3, 2 (2016), 93--100. DOI:10.1007/s40708-015-0030-2Google Scholar
Cross Ref
- V. Srinivasan, C. Eswaran, and N. Sriraam. 2005. Artificial neural network based epileptic detection using time-domain and frequency-domain features. Journal of Medical Systems 29, 6 (2005), 647--660. DOI:10.1007/s10916-005-6133-1Google Scholar
Digital Library
- Y. Jiang, D. Wu, Z. Deng, P. Qian, J. Wang, G. Wang, F. L. Chung, K. S. Choi, and S. Wang. 2017. Seizure classification from EEG signals using transfer learning, semi-supervised learning and TSK fuzzy system. IEEE Transactions on Neural Systems and Rehabilitation Engineering 25, 12 (2017), 2270--2284. DOI:10.1109/TNSRE.2017.2748388Google Scholar
Cross Ref
- A. T. Tzallas, M. G. Tsipouras, and D. I. Fotiadis. 2009. Epileptic seizure detection in EEGs using time--frequency analysis. IEEE Transactions on Information Technology in Biomedicine 13, 5 (2009), 703--710. DOI:10.1109/TITB.2009.2017939Google Scholar
Digital Library
- I. Guler and E. D. Ubeyli. 2007. Multiclass support vector machines for EEG-signals classification. IEEE Transactions on Information Technology in Biomedicine 11, 2 (2007), 117--126. DOI:10.1109/TITB.2006.879600Google Scholar
Digital Library
- Z. Iscan, Z. Dokur, and T. Demiralp. 2011. Classification of electroencephalogram signals with combined time and frequency features. Expert Systems with Applications 38, 8 (2011), 10499--10505. DOI:10.1016/j.eswa.2011.02.110Google Scholar
Digital Library
- A. Aarabi, R. Fazel-Rezai, and Y. Aghakhani. 2009. A fuzzy rule-based system for epileptic seizure detection in intracranial EEG. Clinical Neurophysiology 120, 9 (2009), 1648--1657. DOI:10.1016/j.clinph.2009.07.002Google Scholar
Cross Ref
- A. F. Rabbi and R. Fazel-Rezai. 2012. A fuzzy logic system for seizure onset detection in intracranial EEG. Computational Intelligence and Neuroscience 2012, Article 705140 (2012), 12 pages. DOI:10.1155/2012/705140Google Scholar
- Y. Liu, W. Zhou, Q. Yuan, and S. Chen. 2012. Automatic seizure detection using wavelet transform and SVM in long-term intracranial EEG. IEEE Transactions on Neural Systems and Rehabilitation Engineering 20, 6 (2012), 749--755. DOI:10.1109/TNSRE.2012.2206054Google Scholar
Cross Ref
- F. Qi, Y. Li, and W. Wu. 2015. RSTFC: A novel algorithm for spatio-temporal filtering and classification of single-trial EEG. IEEE Transactions on Neural Networks and Learning Systems 26, 12 (2015), 3070--3082. DOI:10.1109/TNNLS.2015. 2402694Google Scholar
Cross Ref
- L. Xie, Z. Deng, P. Xu, K. S. Choi, and S. Wang. 2018. Generalized hidden-mapping transductive transfer learning for recognition of epileptic electroencephalogram signals. IEEE Transactions on Cybernetics 49, 6 (2018), 2200--2214. DOI:10.1109/TCYB.2018.2821764Google Scholar
Cross Ref
- C. Yang, Z. Deng, K. S. Choi, Y. Jiang, and S. Wang. 2014. Transductive domain adaptive learning for epileptic electroencephalogram recognition. Artificial Intelligence in Medicine 62, 3 (2014), 165--177. DOI:10.1016/j.artmed.2014.10. 002Google Scholar
Cross Ref
- D. Wu, B. J. Lance, and T. D. 2013. Parsons. Collaborative filtering for brain-computer interaction using transfer learning and active class selection. PLoS ONE 8, 2 (2013), e56624. DOI:10.1371/journal.pone.0056624Google Scholar
Cross Ref
- Z. Deng, Y. Jiang, F. L. Chung, H. Ishibuchi, and S. Wang. 2013. Knowledge-leverage-based fuzzy system and its modeling. IEEE Transactions on Fuzzy Systems 21, 4 (2013), 597--609. DOI:10.1109/TFUZZ.2012.2212444Google Scholar
Digital Library
- F. Abid, A. Hassan, A. Abid, I. K. Niazi, and M. Jochumsen. 2017. Transfer learning for electroencephalogram signals. International Journal of Bioscience, Biochemistry and Bioinformatics 7, 3 (2017), 141--152. DOI:10.17706/ijbbb.2017.7.3. 143--152Google Scholar
Cross Ref
- V. Jayaram, M. Alamgir, Y. Altun, B. Scholkopf, and M. Grosse-Wentrup. 2016. Transfer learning in brain-computer interfaces. IEEE Computational Intelligence Magazine 11, 1 (2016), 20--31. DOI:10.1109/MCI.2015.2501545Google Scholar
Digital Library
- S. Sun, H. Shi, and Y. Wu. 2015. A survey of multi-source domain adaptation. Information Fusion 24 (2015), 84--92. DOI:10.1016/j.inffus.2014.12.003Google Scholar
Digital Library
- J. Yang, R. Yan, and A. G. Hauptmann. 2007. Cross-domain video concept detection using adaptive svms. In Proceedings of the 15th ACM International Conference on Multimedia. ACM, 188--197. DOI:10.1145/1291233.1291276Google Scholar
Digital Library
- G. Schweikert, C. Widmer, B. Schölkopf, and G. Rätsch. 2009. An empirical analysis of domain adaptation algorithms for genomic sequence analysis. In Proceedings of the 21st International Conference on Neural Information Processing Systems. ACM, 1433--1440.Google Scholar
- L. Duan, D. Xu, and I. W. H. Tsang. 2012. Domain adaptation from multiple sources: A domain-dependent regularization approach. IEEE Transactions on Neural Networks and Learning Systems 23, 3 (2012), 504--518. DOI:10.1109/TNNLS.2011.2178556Google Scholar
Cross Ref
- T. Takagi and M. Sugeno. 1985. Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics 15, 1 (1985), 116--132. DOI:10.1109/TSMC.1985.6313399Google Scholar
Cross Ref
- Z. Deng, K. S. Choi, F. L. Chung, and S. Wang. 2011. Scalable TSK fuzzy modeling for very large datasets using minimal-enclosing-ball approximation. IEEE Transactions on Fuzzy Systems 19, 2 (2011), 210--226. DOI:10.1109/TFUZZ.2010.2091961Google Scholar
Digital Library
- R. G. Andrzejak, K. Lehnertz, F. Mormann, C. Rieke, P. David, and C. E. Elger. 2001. Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state. Physical Review E 64, 6 (2001), 061907. DOI:10.1103/PhysRevE.64.061907Google Scholar
Cross Ref
- D. Wang, D. Miao, and G. Blohm. 2013. A new method for EEG-based concealed information test. IEEE Transactions on Information Forensics and Security 8, 3 (2013), 520--527. DOI:10.1109/TIFS.2013.2244884Google Scholar
Digital Library
- B. Schölkopf, A. Smola, and K. R. Müller. 1997. Kernel principal component analysis. In Proceedings of the 7th International Conference on Artificial Neural Networks. ICANN, 583--588.Google Scholar
- D. Griffin and J. Lim. 1984. Signal estimation from modified short-time Fourier transform. IEEE Transactions on Acoustics, Speech, and Signal Processing 32, 2 (1984), 236--243. DOI:10.1109/TASSP.1984.1164317Google Scholar
Cross Ref
- V. K. Harpale, V. K. Bairagi. 2016. Time and frequency domain analysis of EEG signals for seizure detection: A review. In Proceedings of the 2016 International Conference on Microelectronics, Computing and Communications (MicroCom). IEEE, 1--6.Google Scholar
Cross Ref
- T. Wu, G. Z. Yan, B. H. Yang, and S. Hong. 2008. EEG feature extraction based on wavelet packet decomposition for brain computer interface. Measurement 41, 6 (2008), 618--625. DOI:10.1016/j.measurement.2007.07.007Google Scholar
Cross Ref
- E. H. Mamdani. 1977. Application of fuzzy logic to approximate reasoning using linguistic synthesis. IEEE Transactions on Computers 26, 12 (1977), 1182--1191. DOI:10.1109/TC.1977.1674779Google Scholar
Digital Library
- Z. Deng, K. S. Choi, Y. Jiang, and S. Wang. 2014. Generalized hidden-mapping ridge regression, knowledge-leveraged inductive transfer learning for neural networks, fuzzy systems and kernel methods. IEEE Transactions on Cybernetics 44, 12 (2014), 2585--2599. DOI:10.1109/TCYB.2014.2311014Google Scholar
Cross Ref
- S. J. Pan and Q. Yang. 2010. A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering 22, 10 (2010), 1345--1359. DOI:10.1109/TKDE.2009.191Google Scholar
Digital Library
- Z. Xu, I. King, M. R. T. Lyu, and R. Jin. 2010. Discriminative semi-supervised feature selection via manifold regularization. IEEE Transactions on Neural Networks 21, 7 (2010), 1033--1047. DOI:10.1109/TNN.2010.2047114Google Scholar
Digital Library
- Y. Jiang, Z. Deng, F. L. Chung, G. Wang, P. Qian, K. S. Choi, and S. Wang. 2017. Recognition of epileptic EEG signals using a novel multiview TSK fuzzy system. IEEE Transactions on Fuzzy Systems 25, 1 (2017), 3--20. DOI: 10.1109/TFUZZ.2016.2637405Google Scholar
Cross Ref
- C. C. Chang and C. J. Lin. 2011. LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2, 3 (2011), 1--27. DOI:10.1145/1961189.1961199Google Scholar
Digital Library
- M. Long, J. Wang, G. Ding, S. J. Pan, and S. Y. Philip. 2014. Adaptation regularization: A general framework for transfer learning. IEEE Transactions on Knowledge and Data Engineering 26, 5 (2014), 1076--1089. DOI:10.1109/TKDE.2013.111Google Scholar
Digital Library
- M. Long, J. Wang, G. Ding, J. Sun, and P. S. Yu. 2014. Transfer joint matching for unsupervised domain adaptation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 1410--1417.Google Scholar
- L. Duan, D. Xu, and S. F. Chang. 2012. Exploiting web images for event recognition in consumer videos: A multiple source domain adaptation approach. In Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 1338--1345.Google Scholar
- A. Gretton, K. M. Borgwardt, M. J. Rasch, B. Schölkopf, and A. Smola. 2012. A kernel two-sample test. Journal of Machine Learning Research 13 (2012), 723--773.Google Scholar
Digital Library
- J. Demšar. 2006. Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research 7 (2006), 1--30.Google Scholar
Digital Library
- S. García and F. Herrera. 2008. An extension on “statistical comparisons of classifiers over multiple data sets” for all pairwise comparisons. Journal of Machine Learning Research 9 (2008), 2677--2694.Google Scholar
- W. Pedrycz and F. Gomide. 2007. Fuzzy Systems Engineering: Toward Human-Centric Computing. John Wiley 8 Sons.Google Scholar
- E. Lughofer. 2011. Evolving Fuzzy Systems-Methodologies, Advanced Concepts and Applications. Springer.Google Scholar
- M. Pratama, S. G. Anavatti, and E. Lughofer. 2014. GENEFIS: Toward an effective localist network. IEEE Transactions on Fuzzy Systems 22, 3 (2014), 547--562. DOI:10.1109/TFUZZ.2013.2264938Google Scholar
Digital Library
- J. Abonyi. 2003. Fuzzy model identification. In Fuzzy Model Identification for Control, J. Abonyi (Ed.). Birkhäuser, 87--164.Google Scholar
- N. B. Karayiannis. 1994. MECA: maximum entropy clustering algorithm. Fuzzy Systems. In Proceedings of the 3rd IEEE Conference on IEEE World Congress on Computational Intelligence, 630--635.Google Scholar
Cross Ref
Index Terms
Smart Diagnosis: A Multiple-Source Transfer TSK Fuzzy System for EEG Seizure Identification
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