Multi-source online transfer algorithm based on source domain selection for EEG classification

Zizhuo Wu; Qingshan She; Zhelong Hou; Zhenyu Li; Kun Tian; Yuliang Ma; Zizhuo Wu; Qingshan She; Zhelong Hou; Zhenyu Li; Kun Tian; Yuliang Ma

doi:10.3934/mbe.2023211

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 3: 4560-4573. doi: 10.3934/mbe.2023211

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Multi-source online transfer algorithm based on source domain selection for EEG classification

1.
School of Automation, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China
2.
Zhejiang Kende Mechanical & Electrical Corporation

Academic Editor: Guenther Palm

Received: 08 October 2022 Revised: 10 December 2022 Accepted: 19 December 2022 Published: 26 December 2022

The non-stationary nature of electroencephalography (EEG) signals and individual variability makes it challenging to obtain EEG signals from users by utilizing brain-computer interface techniques. Most of the existing transfer learning methods are based on batch learning in offline mode, which cannot adapt well to the changes generated by EEG signals in the online situation. To address this problem, a multi-source online migrating EEG classification algorithm based on source domain selection is proposed in this paper. By utilizing a small number of labeled samples from the target domain, the source domain selection method selects the source domain data similar to the target data from multiple source domains. After training a classifier for each source domain, the proposed method adjusts the weight coefficients of each classifier according to the prediction results to avoid the negative transfer problem. This algorithm was applied to two publicly available motor imagery EEG datasets, namely, BCI Competition Ⅳ Dataset Ⅱa and BNCI Horizon 2020 Dataset 2, and it achieved average accuracies of 79.29 and 70.86%, respectively, which are superior to those of several multi-source online transfer algorithms, confirming the effectiveness of the proposed algorithm.
- online transfer learning,
- brain-computer interface,
- motor imagery,
- source domain selection,
- data alignment
Citation: Zizhuo Wu, Qingshan She, Zhelong Hou, Zhenyu Li, Kun Tian, Yuliang Ma. Multi-source online transfer algorithm based on source domain selection for EEG classification[J]. Mathematical Biosciences and Engineering, 2023, 20(3): 4560-4573. doi: 10.3934/mbe.2023211

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Abstract

The non-stationary nature of electroencephalography (EEG) signals and individual variability makes it challenging to obtain EEG signals from users by utilizing brain-computer interface techniques. Most of the existing transfer learning methods are based on batch learning in offline mode, which cannot adapt well to the changes generated by EEG signals in the online situation. To address this problem, a multi-source online migrating EEG classification algorithm based on source domain selection is proposed in this paper. By utilizing a small number of labeled samples from the target domain, the source domain selection method selects the source domain data similar to the target data from multiple source domains. After training a classifier for each source domain, the proposed method adjusts the weight coefficients of each classifier according to the prediction results to avoid the negative transfer problem. This algorithm was applied to two publicly available motor imagery EEG datasets, namely, BCI Competition Ⅳ Dataset Ⅱa and BNCI Horizon 2020 Dataset 2, and it achieved average accuracies of 79.29 and 70.86%, respectively, which are superior to those of several multi-source online transfer algorithms, confirming the effectiveness of the proposed algorithm.

References

[1]	M. Shanechi, Brain-machine interfaces from motor to mood, Nat. Neurosci., 22 (2019), 1554–1564. https://doi.org/10.1038/s41593-019-0488-y doi: 10.1038/s41593-019-0488-y
[2]	B. J. Lance, S. E. Kerick, A. J. Ries, K. S. Oie, K. McDowell, Brain-computer interface technologies in the coming decades, in Proceedings of the IEEE, 100 (2012), 1585–1599. https://doi.org/10.1109/JPROC.2012.2184830
[3]	S. Aggarwal, N. Chugh, Review of machine learning techniques for EEG based brain computer interface, Arch. Comput. Methods Eng., 29 (2022), 3001–3020. https://doi.org/10.1007/s11831-021-09684-6 doi: 10.1007/s11831-021-09684-6
[4]	D. Marshall, D. Coyle, S. Wilson, M. Callaghan, Games, gameplay, and BCI: The state of the art, IEEE Trans. Comput. Intell. AI Games, 5 (2013), 82–99, https://doi.org/10.1109/TCIAIG.2013.2263555 doi: 10.1109/TCIAIG.2013.2263555
[5]	D. Wu, Y. Xu, B. Lu, Transfer learning for EEG-based brain-computer interfaces: A review of progress made since 2016, IEEE Trans. Cognit. Dev. Syst., 14 (2022), 4–19, https://doi.org/10.1109/TCDS.2020.3007453 doi: 10.1109/TCDS.2020.3007453
[6]	Y. Zhang, G. Zhou, J. Jin, Q. Zhao, X. Wang, A. Cichocki, Sparse bayesian classification of EEG for brain-computer interface, IEEE Trans. Neural Networks Learn. Syst., 27 (2016), 2256–2267, https://doi.org/10.1109/TNNLS.2015.2476656 doi: 10.1109/TNNLS.2015.2476656
[7]	M. Krell, N. Wilshusen, A. Seeland, S. K. Kim, Classifier transfer with data selection strategies for online support vector machine classification with class imbalance, J. Neural Eng., 14 (2017), 025003. https://doi.org/10.1088/1741-2552/aa5166. doi: 10.1088/1741-2552/aa5166
[8]	R. Fu, Y. Tian, T. Bao, Z. Meng, P. Shi, Improvement motor imagery EEG classification based on regularized linear discriminant analysis, J. Med. Syst., 43 (2019), 1–13. https://doi.org/10.1007/s10916-019-1270-0. doi: 10.1007/s10916-018-1115-2
[9]	F. Fahimi, S. Dosen, K. Ang, N. Mrachacz-Kersting, C. Guan, Generative adversarial networks-based data augmentation for brain-computer interface, IEEE Trans. Neural Networks Learn. Syst., 32 (2021), 4039–4051, https://doi.org/10.1109/TNNLS.2020.3016666. doi: 10.1109/TNNLS.2020.3016666
[10]	V. Jayaram, M. Alamgir, Y. Altun, B. Scholkopf, M. Grosse-Wentrup, Transfer learning in brain-computer interfaces, IEEE Comput. Intell. Mag., 11 (2016), 20–31. https://doi.org/10.1109/MCI.2015.2501545 doi: 10.1109/MCI.2015.2501545
[11]	H. He, D. Wu, Transfer learning for brain-computer interfaces: A Euclidean space data alignment approach, IEEE Trans. Biomed. Eng., 67 (2021), 399–410. https://doi.org/10.1109/TBME.2019.2913914 doi: 10.1109/TBME.2019.2913914
[12]	L. Xu, M. Xu, Y. Ke, X. An, S. Liu, D. Ming, Cross-dataset variability problem in EEG decoding with deep learning, Front. Hum. Neurosci., 14 (2020), 103–113. https://doi.org/10.3389/fnhum.2020.00103 doi: 10.3389/fnhum.2020.00103
[13]	S. J. Pan, Q. Yang, A survey on transfer learning, IEEE Trans. Knowl. Data Eng., 22 (2010), 1345–1359. https://doi.org/10.1109/TKDE.2009.191 doi: 10.1109/TKDE.2009.191
[14]	M. Long, J. Wang, G. Ding, S. J. Pan, P. S. Yu, Adaptation regularization: A general framework for transfer learning, IEEE Trans. Knowl. Data Eng., 26 (2014), 1076–1089. https://doi.org/10.1109/TKDE.2013.111 doi: 10.1109/TKDE.2013.111
[15]	X. Zhong, S. Guo, H. Shan, L. Gao, D. Xue, N. Zhao, Feature-based transfer learning based on distribution similarity, IEEE Access, 6 (2018), 35550–35557. https://doi.org/10.1109/ACCESS.2018.2843773 doi: 10.1109/ACCESS.2018.2843773
[16]	M. Jiang, W. Huang, Z. Huang, G. G. Yen, Integration of global and local metrics for domain adaptation learning via dimensionality reduction, IEEE Trans. Cybern., 47 (2017), 38–51. https://doi.org/10.1109/TCYB.2015.2502483 doi: 10.1109/TCYB.2015.2502483
[17]	Q. Wu, H. Wu, X. Zhou, M. Tan, Y. Xu, Y. Yan, et al., Online transfer learning with multiple homogeneous or heterogeneous sources, IEEE Trans. Knowl. Data Eng., 29 (2017), 1494–1507. https://doi.org/10.1109/TKDE.2017.2685597 doi: 10.1109/TKDE.2017.2685597
[18]	J. Wang, P. Zhao, S. C. H. Hoi, R. Jin, Online feature selection and its applications, IEEE Trans. Knowl. Data Eng., 26 (2013), 698–710. https://doi.org/10.1109/TKDE.2013.32 doi: 10.1109/TKDE.2013.32
[19]	P. Zhao, C. Steven, OTL: A framework of online transfer learning, in Proceedings of the 27th International Conference on Machine Learning, Haifa, Israel, (2010), 1231–1238.
[20]	P. Zhao, S. Hoi, J. Wang, B. Li, Online transfer learning, Artif. Intell., 216 (2014), 76–102. https://doi.org/10.1016/j.artint.2014.06.003 doi: 10.1016/j.artint.2014.06.003
[21]	Z. Kang, B. Yang, Z. Li, P. Wang, OTLAMC: An online transfer learning algorithm for multi-class classification, Knowl.-Based Syst., 176 (2019), 133–146. https://doi.org/10.1016/j.knosys.2019.03.024 doi: 10.1016/j.knosys.2019.03.024
[22]	L. Ge, J. Gao, A. Zhang, Oms-tl: A framework of online multiple source transfer learning, in Proceedings of ACM International Conference on Information & Knowledge Management, ACM, (2013), 2423–2428. https://doi.org/10.1145/2505515.2505603.
[23]	H. Zhou, K. Wang, J. Tian, Online transfer learning for differential diagnosis of benign and malignant thyroid nodules with ultrasound images, IEEE Trans. Biomed. Eng., 67 (2020), 27732780. https://doi.org/10.1109/TBME.2020.2971065 doi: 10.1109/TBME.2020.2971065
[24]	E. Eaton, M. DesJardins, Selective transfer between learning tasks using task-based boosting, in Proceedings of the 25th AAAI Conference on Artificial Intelligence, (2011), 337–342.
[25]	Y. Yao, G. Doretto, Boosting for transfer learning with multiple sources, in Proceedings of IEEE Computer Vision and Pattern Recognition, (2010), 1855–1862. https://doi.org/10.1109/CVPR.2010.5539857
[26]	B. Tan, E. Zhong, E. Xiang, Q. Yang, Multi-transfer: Transfer learning with multiple views and multiple sources, Stat. Anal. Data Min., 7 (2014), 282–293. https://doi.org/10.1002/sam.11226 doi: 10.1002/sam.11226
[27]	Y. Jiang, F. Chung, H. Ishibuchi, Z. Deng, S. Wang, Multitask TSK fuzzy system modeling by mining intertask common hidden structure, IEEE Trans. Cybernet., 45 (2015), 534–547. https://doi.org/10.1109/TCYB.2014.2330844 doi: 10.1109/TCYB.2014.2330844
[28]	Y. Du, Z. Tan, Q. Chen, Y. Zhang, C. Wang, Homogeneous online transfer learning with online distribution discrepancy minimization, in Proceedings of the 24th European Conference on Artificial Intelligence, (2020), 1–9. https://doi.org/10.48550/arXiv.1912.13226
[29]	Z. Li, Q. She, Y. Ma, J. Zhang, M. Sun, Online EEG classification method based on instance transfer, Chin. J. Sens. Actuators, 35 (2022), 1109–1116. https://doi.org/10.3969/j.issn.1004-1699.2022.08.015 doi: 10.3969/j.issn.1004-1699.2022.08.015
[30]	C. Brunner, R. Leeb, G. R. Müller-Putz, A. Schlögl, G. Pfurtscheller, BCI Competition 2008-Graz Data Set A, Institute for Knowledge Discovery (Laboratory of Brain-Computer Interfaces), Graz University of Technology, 16 (2008), 1–6.
[31]	BNCI Horizon 2020, Data sets-BNCI Horizon 2020, http://bnci-horizon-2020.eu/database/data-sets.
[32]	H. He, D. Wu, Transfer learning for brain-computer interfaces: A Euclidean space data alignment approach, IEEE Trans. Biomed. Eng., 67 (2020), 399–410. https://doi.org/10.1109/TBME.2019.2913914 doi: 10.1109/TBME.2019.2913914
[33]	W. Zhang, D. Wu, Manifold embedded knowledge transfer for brain-computer interfaces, IEEE Trans. Neural Syst. Rehabil. Eng., 28 (2020), 1117–1127. https://doi.org/10.1109/TNSRE.2020.2985996 doi: 10.1109/TNSRE.2020.2985996
[34]	P. Zanini, M. Congedo, C. Jutten, S. Said, Y. Berthoumieu, Transfer learning: A Riemannian geometry framework with applications to brain-computer interfaces, IEEE Trans. Biomed. Eng., 65 (2018), 1107–1116. https://doi.org/10.1109/TBME.2017.2742541 doi: 10.1109/TBME.2017.2742541
[35]	D. Wu, X. Jiang, R. Peng, Transfer learning for motor imagery based brain-computer interfaces: A tutorial, Neural Networks, 153 (2022), 235–253. https://doi.org/10.1016/j.neunet.2022.06.008 doi: 10.1016/j.neunet.2022.06.008
[36]	J. Wang, Y. Chen, S. Hao, W. Feng, Z. Shen, Balanced distribution adaptation for transfer learning, in Proceedings of IEEE International Conference on Data Mining (ICDM), (2017), 1129–1134. https://doi.org/10.1109/ICDM.2017.150
[37]	J. Wang, W. Feng, Y. Chen, H. Yu, M. Huang, P. S. Yu, Visual domain adaptation with manifold embedded distribution alignment, in Proceedings of the 26th ACM International Conference on Multimedia, (2018), 402–410. https://doi.org/10.1145/3240508.3240512

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