Brain computer interface nursing bed control system based on deep learning and dual visual feedback_Journal of Biomedical Engineering

Authors：

 WANG Pai ¹ , JI Xingxing ¹ , WANG Jiali ¹ , YU Xiaojun ²

1. School of Electrical and Control Engineering, Xi 'an University of Science and Technology, Xi 'an 710054, P. R. China;
2. School of Automation, Northwestern Polytechnical University, Xi 'an 710072, P. R. China;

Corresponding author：

WANG Pai, Email: wangpai2013@xust.edu.cn

Keywords：

Motor imagery-brain computer interface; Visual feedback; Graph convolutional network; Multi-scale convolution; Nursing bed system

DOI：

10.7507/1001-5515.202504047

Video：

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Abstract Full text Figures/Tables Video References Cited by

In order to meet the need of autonomous control of patients with severe limb disorders, this paper designs a nursing bed control system based on motor imagery-brain computer interface (MI-BCI). In view of the low decoding performance of cross-subjects and the dynamic fluctuation of cognitive state in the existing MI-BCI technology, the neural network structure optimization and user interaction feedback enhancement are improved. Firstly, the optimized dual-branch graph convolution multi-scale neural network integrates dynamic graph convolution and multi-scale convolution. The average classification accuracy is higher than that of multi-scale attention temporal convolution network, Gram angle field combined with convolution long short term memory hybrid network, Transformer-based graph convolution network and other existing methods. Secondly, a dual visual feedback mechanism is constructed, in which electroencephalogram (EEG) topographic map feedback can improve the discrimination of spatial patterns, and attention state feedback can enhance the temporal stability of signals. Compared with the single EEG topographic map feedback and non-feedback system, the average classification accuracy of the proposed method is also greatly improved. Finally, in the four classification control task of nursing bed, the average control accuracy of the system is 90.84%, and the information transmission rate is 84.78 bits/min. In summary, this paper provides a reliable technical solution for improving the autonomous interaction ability of patients with severe limb disorders, which has important theoretical significance and application value.

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1. McFarland D J, Wolpaw J R. Brain computer interfaces for communication and control. Communications of the ACM, 2011, 54(5): 60-66.
2. Cervera M A, Soekadar S R, Ushiba J, et al. Brain computer interfaces for post-stroke motor rehabilitation: a meta-analysis. Annals of Clinical and Translational Neurology, 2018, 5(5): 651-663.
3. Ghosh R. A survey of brain computer interface using non-invasive methods. arXiv preprint, 2023, arXiv: 2309.13151.
4. Schirrmeister R T, Springenberg J T, Fiederer L D J, et al. Deep learning with convolutional neural networks for EEG decoding and visualization. Human Brain Mapping, 2017, 38(11): 5391-5420.
5. 刘拓, 叶阳阳, 王坤, 等. 运动想象脑电信号分类算法的研究进展. 生物医学工程学杂志, 2021, 38(5): 995-1002.
6. Nazre R, Budke R, Oak O, et al. A temporal convolutional network-based approach for network intrusion detection. arXiv preprint, 2024, arXiv: 2412.17452.
7. Bhandari H C, Pandeya Y R, Jha K, et al. Exploring non-Euclidean approaches: a comprehensive survey on graph-based techniques for EEG signal Analysis. Journal of Advances in Information Technology, 2024, 15(10): 1089-1105.
8. Sun B, Zhang H, Wu Z, et al. Adaptive spatiotemporal graph convolutional networks for motor imagery classification. IEEE Signal Processing Letters, 2021, 28(1): 219-223.
9. Gao H, Wang X, Chen Z, et al. Graph convolutional network with connectivity uncertainty for EEG-based emotion recognition. IEEE Journal of Biomedical and Health Informatics, 2024, 28(10): 5917-5928.
10. Wang A, Tian X, Jiang D, et al. Rehabilitation with brain computer interface and upper limb motor function in ischemic stroke: a randomized controlled trial. Med, 2024, 5(6): 559-569.
11. Birbaumer N, Weber C, Neuper C, et al. Physiological regulation of thinking: brain computer interface (BCI) research, Prog Brain Res, 2006, 159: 369-391.
12. Butet S, Fleury M, Duché Q, et al. EEG-fMRI neurofeedback versus motor imagery after stroke, a randomized controlled trial. Journal of NeuroEngineering and Rehabilitation, 2025, 22: 67.
13. Kim M S, Park H, Kwon I, et al. Efficacy of brain computer interface training with motor imagery contingent feedback in improving upper limb function and neuroplasticity among persons with chronic stroke: a double blinded, parallel group, randomized controlled trial. Journal of NeuroEngineering and Rehabilitation, 2025, 22: 1.
14. Ang K K, Chin Z Y, Zhang H H, et al. Filter bank common spatial pattern (FBCSP) in brain computer interface//2008 IEEE International Joint Conference on Neural Networks (IJCNN), New York: IEEE, 2008: 2390-2397.
15. Lawhern V J, Solon A J, Waytowich N R, et al. EEGNet: a compact convolutional neural network for EEG-based brain-computer interfaces. Journal of Neural Engineering, 2018, 15(5): 056013.
16. 李卫校. 基于深度学习的运动想象脑电信号识别方法. 淮南: 安徽理工大学, 2024.
17. Bhatt M W, Sharma S. Multi-scale self-attention approach for analysing motor imagery signals in brain-computer interfaces. Journal of Neuroscience Methods, 2024, 408: 110182.
18. 吕仁杰, 常文文, 严光辉, 等. 基于GAF与混合模型的运动想象分类研究. 电子科技大学学报, 2024, 53(6): 952-960.
19. Hamidi A, Kiani K. Motor imagery EEG signals classification using a Transformer-GCN approach. Applied Soft Computing, 2024, 170: 112686.
20. Brunner C, Leeb R, Müller-Putz G, et al. BCI Competition 2008-Graz data set A. (2008-07-03)[2008-12-12]. DOI: 10.21227/katb-zv89.
21. Wang Y. Research on event related desynchronization of motor imagery and movement based on localized EEG cortical sources. arXiv preprint, 2025, arXiv: 2502.19869.
22. 肖健, 党选举. 多域特征融合的脑电信号肢体运动特征提取与动作识别. 电子测量技术, 2024, 47(18): 23-30.
23. 刘童瑶. 基于机器学习的复杂信号调制识别技术研究. 成都: 电子科技大学, 2025.
24. 高明星, 岳晶晶, 李鹏飞, 等. 基于ERP的草原公路自动驾驶接管认知特性研究. 内蒙古农业大学学报(自然科学版), 2024, 45(6): 69-75.
25. Li G, Huang S, Xu W, et al. The impact of mental fatigue on brain activity: a comparative study both in resting state and task state using EEG. BMC Neuroscience, 2020, 21: 20.
26. Wang J, Xu Y, Tian J, et al. Driving fatigue detection with three non-hair-bearing EEG channels and modified transformer model. Entropy, 2022, 24(12): 1715.

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