Research on motor imagery recognition based on feature fusion and transfer adaptive boosting_Journal of Biomedical Engineering

Authors：

ZHANG Yuxin ^1,2,3 , ZHANG Chenrui ^1,2,3 , SUN Shihao ^2,3 ,  XU Guizhi ^1,2,3

1. Department of Biomedical Engineering, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, TianJin 300130, P. R. China;
2. State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, P. R. China;
3. Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin 300130, P. R. China;

Corresponding author：

XU Guizhi, Email: gzxu@hebut.edu.cn

Keywords：

Brain-computer interface; Transfer learning; Feature fusion; Transfer adaptive boosting; Motor imagery

DOI：

10.7507/1001-5515.202304067

Video：

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This paper proposes a motor imagery recognition algorithm based on feature fusion and transfer adaptive boosting (TrAdaboost) to address the issue of low accuracy in motor imagery (MI) recognition across subjects, thereby increasing the reliability of MI-based brain-computer interfaces (BCI) for cross-individual use. Using the autoregressive model, power spectral density and discrete wavelet transform, time-frequency domain features of MI can be obtained, while the filter bank common spatial pattern is used to extract spatial domain features, and multi-scale dispersion entropy is employed to extract nonlinear features. The IV-2a dataset from the 4^th International BCI Competition was used for the binary classification task, with the pattern recognition model constructed by combining the improved TrAdaboost integrated learning algorithm with support vector machine (SVM), k nearest neighbor (KNN), and mind evolutionary algorithm-based back propagation (MEA-BP) neural network. The results show that the SVM-based TrAdaboost integrated learning algorithm has the best performance when 30% of the target domain instance data is migrated, with an average classification accuracy of 86.17%, a Kappa value of 0.723 3, and an AUC value of 0.849 8. These results suggest that the algorithm can be used to recognize MI signals across individuals, providing a new way to improve the generalization capability of BCI recognition models.

Citation： ZHANG Yuxin, ZHANG Chenrui, SUN Shihao, XU Guizhi. Research on motor imagery recognition based on feature fusion and transfer adaptive boosting. Journal of Biomedical Engineering, 2025, 42(1): 9-16. doi: 10.7507/1001-5515.202304067 Copy

1.	Kim T, Kim S, Lee B. Effects of action observational training plus brain-computer interface‐based functional electrical stimulation on paretic arm motor recovery in patient with stroke: a randomized controlled trial. Occupational Therapy International, 2016, 23(1): 39-47.
2.	Ramadan R A, Vasilakos A V. Brain computer interface: control signals review. Neurocomputing, 2017, 223: 26-44.
3.	Lahane P, Jagtap J, Inamdar A, et al. A review of recent trends in EEG based brain-computer interface//2019 International Conference on Computational Intelligence in Data Science (ICCIDS), Chennai: IEEE, 2019: 1-6.
4.	Azami H, Kinney-Lang E, Ebied A, et al. Multiscale dispersion entropy for the regional analysis of resting-state magnetoencephalogram complexity in Alzheimer's disease//2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Jeju: IEEE, 2017: 3182-3185.
5.	Zhang W, Gao J, Liang Z, et al. Classification method of motor EEG signals based on EMD and refined composite multi-scale entropy//2021 International Conference on Electrical, Computer and Energy Technologies (ICECET), Cape Town: IEEE, 2021: 1-6.
6.	Yang L, Shi T, Lv J, et al. A multi-feature fusion decoding study for unilateral upper-limb fine motor imagery. Mathematical Biosciences and Engineering, 2023, 20(2): 2482-2500.
7.	Phadikar S, Sinha N, Ghosh R. Unsupervised feature extraction with autoencoders for EEG based multiclass motor imagery BCI. Expert Systems with Applications, 2023, 213: 118901.
8.	Wang J, Chen Y H, Yang J, et al. Intelligent classification technique of hand motor imagery using EEG beta rebound follow-up pattern. Biosensors, 2022, 12(6): 384.
9.	Xie Y, Oniga S. A review of processing methods and classification algorithm for EEG signal. Carpathian Journal of Electronic Computer Engineering, 2020, 13(1): 23-29.
10.	Aggarwal S, Chugh N. Review of machine learning techniques for EEG based brain computer interface. Archives of Computational Methods in Engineering, 2022, 29(5): 3001-3020.
11.	Zhuang F, Qi Z, Duan K, et al. A comprehensive survey on transfer learning. Proceedings of the IEEE, 2020, 109(1): 43-76.
12.	Zheng L, Liu G, Yan C, et al. Improved TrAdaBoost and its application to transaction fraud detection. IEEE Transactions on Computational Social Systems, 2020, 7(5): 1304-1316.
13.	Zhang X, Liang W, Ding T, et al. Individual similarity guided transfer modeling for EEG-based emotion recognition//2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), San Diego: IEEE, 2019: 1156-1161.
14.	Ang K K, Chin Z Y, Wang C, et al. Filter bank common spatial pattern algorithm on BCI competition IV datasets 2a and 2b. Frontiers in Neuroscience, 2012, 6: 39.
15.	Sury D A, Djamal E C. Brain-computer interface of motor imagery and emotion using multiple recurrent neural networks//2021 4th International Conference of Computer and Informatics Engineering (IC2IE), Depok: IEEE, 2021: 56-61.
16.	Dai W, Yang Q, Xue G R, et al. Boosting for transfer learning//Proceedings of the 24th International Conference on Machine Learning, Corvallis: ACM, 2007: 193-200.
17.	戴鸿辉. 基于迁移学习的少导联运动想象BCI系统研究. 南昌: 南昌大学, 2022.
18.	Xu Y, Hua J, Zhang H, et al. Improved transductive support vector machine for a small labelled set in motor imagery-based brain-computer interface. Computational Intelligence and Neuroscience, 2019, 2019: 2087132.
19.	Cai Y, She Q, Ji J, et al. Motor imagery EEG decoding using manifold embedded transfer learning. Journal of Neuroscience Methods, 2022, 370: 109489.

1. Kim T, Kim S, Lee B. Effects of action observational training plus brain-computer interface‐based functional electrical stimulation on paretic arm motor recovery in patient with stroke: a randomized controlled trial. Occupational Therapy International, 2016, 23(1): 39-47.
2. Ramadan R A, Vasilakos A V. Brain computer interface: control signals review. Neurocomputing, 2017, 223: 26-44.
3. Lahane P, Jagtap J, Inamdar A, et al. A review of recent trends in EEG based brain-computer interface//2019 International Conference on Computational Intelligence in Data Science (ICCIDS), Chennai: IEEE, 2019: 1-6.
4. Azami H, Kinney-Lang E, Ebied A, et al. Multiscale dispersion entropy for the regional analysis of resting-state magnetoencephalogram complexity in Alzheimer's disease//2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Jeju: IEEE, 2017: 3182-3185.
5. Zhang W, Gao J, Liang Z, et al. Classification method of motor EEG signals based on EMD and refined composite multi-scale entropy//2021 International Conference on Electrical, Computer and Energy Technologies (ICECET), Cape Town: IEEE, 2021: 1-6.
6. Yang L, Shi T, Lv J, et al. A multi-feature fusion decoding study for unilateral upper-limb fine motor imagery. Mathematical Biosciences and Engineering, 2023, 20(2): 2482-2500.
7. Phadikar S, Sinha N, Ghosh R. Unsupervised feature extraction with autoencoders for EEG based multiclass motor imagery BCI. Expert Systems with Applications, 2023, 213: 118901.
8. Wang J, Chen Y H, Yang J, et al. Intelligent classification technique of hand motor imagery using EEG beta rebound follow-up pattern. Biosensors, 2022, 12(6): 384.
9. Xie Y, Oniga S. A review of processing methods and classification algorithm for EEG signal. Carpathian Journal of Electronic Computer Engineering, 2020, 13(1): 23-29.
10. Aggarwal S, Chugh N. Review of machine learning techniques for EEG based brain computer interface. Archives of Computational Methods in Engineering, 2022, 29(5): 3001-3020.
11. Zhuang F, Qi Z, Duan K, et al. A comprehensive survey on transfer learning. Proceedings of the IEEE, 2020, 109(1): 43-76.
12. Zheng L, Liu G, Yan C, et al. Improved TrAdaBoost and its application to transaction fraud detection. IEEE Transactions on Computational Social Systems, 2020, 7(5): 1304-1316.
13. Zhang X, Liang W, Ding T, et al. Individual similarity guided transfer modeling for EEG-based emotion recognition//2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), San Diego: IEEE, 2019: 1156-1161.
14. Ang K K, Chin Z Y, Wang C, et al. Filter bank common spatial pattern algorithm on BCI competition IV datasets 2a and 2b. Frontiers in Neuroscience, 2012, 6: 39.
15. Sury D A, Djamal E C. Brain-computer interface of motor imagery and emotion using multiple recurrent neural networks//2021 4th International Conference of Computer and Informatics Engineering (IC2IE), Depok: IEEE, 2021: 56-61.
16. Dai W, Yang Q, Xue G R, et al. Boosting for transfer learning//Proceedings of the 24th International Conference on Machine Learning, Corvallis: ACM, 2007: 193-200.
17. 戴鸿辉. 基于迁移学习的少导联运动想象BCI系统研究. 南昌: 南昌大学, 2022.
18. Xu Y, Hua J, Zhang H, et al. Improved transductive support vector machine for a small labelled set in motor imagery-based brain-computer interface. Computational Intelligence and Neuroscience, 2019, 2019: 2087132.
19. Cai Y, She Q, Ji J, et al. Motor imagery EEG decoding using manifold embedded transfer learning. Journal of Neuroscience Methods, 2022, 370: 109489.

Journal of Biomedical Engineering

Research on motor imagery recognition based on feature fusion and transfer adaptive boosting

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