An exoskeleton upper limb rehabilitation robot based on electroencephalography
Abstract
Today, stroke patients have varying degrees of motor impairment after surgery. An Electroencephalography (EEG) signal is a potential change recorded on the scalp of a human or animal, which can be combined with a rehabilitation robot to help patients complete rehabilitation movements. In this paper, a new exoskeleton-type 6-DOF upper limb rehabilitation robot is designed based on EEG control. The wavelet denoising method based on Gaussian mixture model (GMM) is used for signal pre-processing. The wavelet packet decomposition method is used to extract feature vectors, and the feature performance index based on Mahalanobis distance and Babanobis distance is introduced to test the accuracy of Feature Performance Index (FPI) relativity. The random forest classifier was used to classify and recognize the EEG characteristics and obtain the motion intention of patients. The experimental research shows that the EEG signal processing method proposed in this paper has significant effect, and the upper limb rehabilitation robot based on EEG signal has feasibility. The whole system can significantly improve the patient’s rehabilitation enthusiasm.
References
1. Huang XL, Yan TB. Rehabilitation medicine, 5th ed. People’s Medical Publishing House; 2013. p. 151.
2. Twitchell TE. The Restoration Of Motor Function Following Hemiplegia In Man. Brain. 1951; 74(4): 443-480. doi: 10.1093/brain/74.4.443
3. Burgar CG, Lum PS, Shor PC, Machiel VanderLoos HF. Development of robots for rehabilitation therapy: the Palo Alto VA/Stanford experience. J. Rehabil. Res. Dev. 2000; 37(6): 663‐673.
4. Hesse S, Schulte‐Tigges G, Konrad M, et al. Robot‐assisted arm trainer for the passive and active practice of bilateral forearm and wrist movements in hemiparetic subjects. Arch. Phys. Med. Rehabil. 2003; 84(6): 915‐920. doi: 10.1016/S0003-9993(02)04954-7
5. Li Q. Research on upper limb rehabilitation system of exoskeleton robot based on sEMG signal. Harbin Institute of Technology; 2017
6. Tang Z. Research on the Control Method of an Upper-limb Rehabilitation Exoskeleton Based on Classification of Motor Imagery EEG. Journal of Mechanical Engineering. 2017; 53(10): 60. doi: 10.3901/jme.2017.10.060
7. Kwak NS, Müller KR, Lee SW. A lower limb exoskeleton control system based on steady state visual evoked potentials. Journal of Neural Engineering. 2015; 12(5): 056009. doi: 10.1088/1741-2560/12/5/056009
8. Kwak NS, Müller KR, Lee SW. A convolutional neural network for steady state visual evoked potential classification under ambulatory environment. PLOS ONE. 2017; 12(2): e0172578. doi: 10.1371/journal.pone.0172578
9. Chen Q, Peng H, Han X, et al. High-Speed Lifting Filtering in Brain-Computer Interface Signal Preprocessing. Biomedical Engineering Research. 2004; 23(3): 207-210.
10. Jiang N, Gizzi L, Mrachacz-Kersting N, et al. A brain–computer interface for single-trial detection of gait initiation from movement related cortical potentials. Clinical Neurophysiology. 2015; 126(1): 154-159. doi: 10.1016/j.clinph.2014.05.003
11. Ghassemzadeh N, Haghipour S. A review on EEG based brain computer interface systems feature extraction methods. International journal of Advanced Biological and Biomedical Research. 2016; 4(2): 117-123.
12. Webber WRS, Lesser RP, Richardson RT, et al. An approach to seizure detection using an artificial neural network (ANN). Electroencephalography and Clinical Neurophysiology. 1996; 98(4): 250-272. doi: 10.1016/0013-4694(95)00277-4
13. Vaid S, Singh P, Kaur C. EEG Signal Analysis for BCI Interface: A Review. In: 2015 Fifth International Conference on Advanced Computing & Communication Technologies; 2015. doi: 10.1109/acct.2015.72
14. Abootalebi V, Moradi MH, Khalilzadeh MA. A new approach for EEG feature extraction in P300-based lie detection. Computer Methods and Programs in Biomedicine. 2009; 94(1): 48-57. doi: 10.1016/j.cmpb.2008.10.001
15. Qiu Q. Characteristic Extraction and Pattern Classification of Surface EMG Signals [Master’s thesis]. Shanghai Jiao Tong University; 2009.
16. Zhang Y, Fang K. Introduction to Multivariate Statistical Analysis. Science Press; 1982.
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