Abstract
Brain-computer interface (BCI) is a new communication and control technology established between human or animal brains and computer or other electronic equipment that does not rely on conventional brain information output pathways. The non-invasive BCI technology collects EEG signals from the cerebral cortex through signal acquisition equipment and processes them into signals recognized by the computer. The signals are preprocessed to extract signal features used for pattern recognition and finally are transformed into specific instructions for controlling external types of equipment. Therefore, the robustness of EEG signal representation is essential for intention recognition. Herein, we convert EEG signals into the image sequence and utilize the Local Relation Networks model to extract high-resolution feature information and demonstrate its advantages in the motor imagery (MI) classification task. The proposed method, MIIRvLR-Net, can effectively eliminate signal noise and improve the signal-to-noise ratio to reduce information loss. Extensive experiments using publicly available EEG datasets have proved that the proposed method achieved better performance than the state-of-the-art methods.
The second author has an equal first-author-level contribution to this work. This work has partially been supported by the Project of Philosophy and Social Sciences of Jilin Province under Project No. 2019C70 and the Thirteenth Five-Year Program for Social Science of Education Department of Jilin Province under Project No. JJKH20201187SK.
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References
Alfeld, P.: A trivariate clough-tocher scheme for tetrahedral data. Comput. Aided Geometric Des. 1(2), 169–181 (1984)
Alomari, M.H., AbuBaker, A., Turani, A., Baniyounes, A.M., Manasreh, A.: EEG mouse: a machine learning-based brain computer interface. Int. J. Adv. Comput. Sci. Appl. 5(4), 193–198 (2014)
Bashivan, P., Rish, I., Yeasin, M., Codella, N.: Learning representations from EEG with deep recurrent-convolutional neural networks. arXiv preprint arXiv:1511.06448 (2015)
Battaglia, P.W., et al.: Relational inductive biases, deep learning, and graph networks. arXiv preprint arXiv:1806.01261 (2018)
Chen, W., Wang, S., Zhang, X., Yao, L., Yue, L., Qian, B., Li, X.: EEG-based motion intention recognition via multi-task RNNs. In: Proceedings of the 2018 SIAM International Conference on Data Mining, pp. 279–287. SIAM (2018)
Chen, W., Yue, L., Li, B., Wang, C., Sheng, Q.Z.: DAMTRNN: a delta attention-based multi-task RNN for intention recognition. In: Li, J., Wang, S., Qin, S., Li, X., Wang, S. (eds.) ADMA 2019. LNCS (LNAI), vol. 11888, pp. 373–388. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-35231-8_27
Hu, H., Zhang, Z., Xie, Z., Lin, S.: Local relation networks for image recognition. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3464–3473 (2019)
Kim, Y., Ryu, J., Kim, K.K., Took, C.C., Mandic, D.P., Park, C.: Motor imagery classification using mu and beta rhythms of EEG with strong uncorrelating transform based complex common spatial patterns. Computational intelligence and neuroscience 2016 (2016)
Qiu, Y., Chen, W., Yue, L., Xu, M., Zhu, B.: Stct: spatial-temporal conv-transformer network for cardiac arrhythmias recognition. In: International Conference on Advanced Data Mining and Applications, pp. 86–100. Springer (2022)
or Rashid, M.M., Ahmad, M.: Classification of motor imagery hands movement using levenberg-marquardt algorithm based on statistical features of EEG signal. In: 2016 3rd International Conference on Electrical Engineering and Information Communication Technology (ICEEICT), pp. 1–6. IEEE (2016)
Sabour, S., Frosst, N., Hinton, G.E.: Dynamic routing between capsules. arXiv preprint arXiv:1710.09829 (2017)
Shenoy, H.V., Vinod, A.P., Guan, C.: Shrinkage estimator based regularization for EEG motor imagery classification. In: 2015 10th International Conference on Information, Communications and Signal Processing (ICICS), pp. 1–5. IEEE (2015)
Sita, J., Nair, G.: Feature extraction and classification of EEG signals for mapping motor area of the brain. In: 2013 International Conference on Control Communication and Computing (ICCC), pp. 463–468. IEEE (2013)
Yue, L., Shen, H., Wang, S., Boots, R., Long, G., Chen, W., Zhao, X.: Exploring BCI control in smart environments: intention recognition via EEG representation enhancement learning. ACM Trans. Knowl. Discovery Data (TKDD) 15(5), 1–20 (2021)
Yue, L., Shi, Z., Han, J., Wang, S., Chen, W., Zuo, W.: Multi-factors based sentence ordering for cross-document fusion from multimodal content. Neurocomputing 253, 6–14 (2017)
Yue, L., Tian, D., Chen, W., Han, X., Yin, M.: Deep learning for heterogeneous medical data analysis. World Wide Web 23(5), 2715–2737 (2020)
Yue, L., Tian, D., Jiang, J., Yao, L., Chen, W., Zhao, X.: Intention recognition from spatio-temporal representation of EEG signals. In: Qiao, M., Vossen, G., Wang, S., Li, L. (eds.) ADC 2021. LNCS, vol. 12610, pp. 1–12. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-69377-0_1
Zang, Y., Liu, Y., Chen, W., Li, B., Li, A., Yue, L., Ma, W.: Gisdcn: A graph-based interpolation sequential recommender with deformable convolutional network. In: International Conference on Database Systems for Advanced Applications, pp. 289–297. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-00126-0_21
Zhang, X., Yao, L., Huang, C., Sheng, Q.Z., Wang, X.: Intent recognition in smart living through deep recurrent neural networks. In: International Conference on Neural Information Processing, vol. 10635, pp. 748–758. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-70096-0_76
Zhang, X., Yao, L., Sheng, Q.Z., Kanhere, S.S., Gu, T., Zhang, D.: Converting your thoughts to texts: Enabling brain typing via deep feature learning of eeg signals. In: 2018 IEEE international conference on pervasive computing and communications (PerCom), pp. 1–10. IEEE (2018)
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Yue, L., Zhang, Y., Zhao, X., Zhang, Z., Chen, W. (2023). Improving Motor Imagery Intention Recognition via Local Relation Networks. In: Li, B., Yue, L., Tao, C., Han, X., Calvanese, D., Amagasa, T. (eds) Web and Big Data. APWeb-WAIM 2022. Lecture Notes in Computer Science, vol 13421. Springer, Cham. https://doi.org/10.1007/978-3-031-25158-0_26
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