進階搜尋


   電子論文尚未授權公開,紙本請查館藏目錄
(※如查詢不到或館藏狀況顯示「閉架不公開」,表示該本論文不在書庫,無法取用。)
系統識別號 U0026-1908202018213900
論文名稱(中文) 混合式腦機介面結合單軸機器人用於神經復健之應用
論文名稱(英文) Application of Hybrid Brain-Computer Interface Integrated with a Single-Axis Robot for Neurorehabilitation
校院名稱 成功大學
系所名稱(中) 生物醫學工程學系
系所名稱(英) Department of BioMedical Engineering
學年度 108
學期 2
出版年 109
研究生(中文) 吳峻寬
研究生(英文) Chun-Kuan Wu
學號 P86061037
學位類別 碩士
語文別 英文
論文頁數 82頁
口試委員 指導教授-楊岱樺
口試委員-朱銘祥
口試委員-林宙晴
口試委員-楊世宏
中文關鍵字 神經復健  穩態視覺誘發電位  動作相關去同步化電位  混合式腦機介面 
英文關鍵字 neurorehabilitation  steady-state visual evoked potential  event-related desynchronization  hybrid brain-computer-interface 
學科別分類
中文摘要 急性腦血管疾病是國人十大死因之一,其會造成偏癱、感覺失調或是表達障礙等。神經復健的原理為使患者受損腦區的鄰近區域活化並取代原受損部位,進而使患者恢復生活自理的機能。
本研究整合穩態視覺誘發電位(Steady-State Visual Evoked Potential, SSVEP)以及動作相關去同步化電位(Event-Related Desynchronization, ERD),開發一混合式腦機介面系統,並結合上肢單軸復健機器人進行復健模擬。本研究共招募十二位常人受試者,並以SSVEP選擇器做為控制變因分為控制組和實驗組進行系統測試與為期四週的復健模擬,最後以ERD判定成功率以及發生時間作為指標,比較混合特徵與單一特徵對於神經復健之效果。結果顯示,在ERD判定成功率方面,實驗組在第三週以及第四週之平均略高於控制組,而在ERD發生時間方面,實驗組可觀察到穩定進步的趨勢,其第四週平均較第一週縮短0.16秒,並且在第二至四週表現皆較控制組優異。此外亦發現實驗組受試者之SSVEP準確率與其ERD判定成功率成中度正相關(ρ=0.55)、與ERD發生時間呈中度負相關(ρ=-0.60)。
結論,本研究成功發展一套混合式腦機介面系統所控制的上肢復健機器人,並能對常人受試者進行復健模擬,並且證實混合特徵對於神經復健有更好的效果。未來應實際徵召中風病患對系統進行測試,以提升系統可信度與穩健性。
英文摘要 Acute cerebrovascular accident was one of the main cause of death in Taiwan, and it causes hemiplegia, loss of sensation and unclear speech, etc. The principle of neurorehabilitation is to activate the adjacent area of the damaged brain cortex of the patient to replace the lesion parts so that the patient can restore the function of daily living.
This research integrates the steady-state visual evoked potentials (SSVEP) and event-related desynchronization (ERD) to develop a hybrid brain-computer interface system for controlling the rehabilitation machine to perform rehabilitation simulation. Twelve normal subjects were recruited in this research. By the SSVEP selector as the control variable, subjects were divided into the control group and the experimental group, and perform the system testing and 4-week rehabilitation simulation. The success rate of ERD classification and the time to reach the maximum ERD were used as indicators to compare the effects of hybrid and single feature on neurorehabilitation. The results showed that in terms of the ERD success rate, the average of the experimental group in week 3 and week 4 was slightly higher than that of the control group. As for ERD happened time, a steadily improved trend could be seen in the experimental group. The average happened time at week 4 was about 0.16 seconds smaller than that of week 1. Besides, the experimental group was faster than the control group from week 2 to week 4. In addition, it was also found that SSVEP accuracy had a moderately positive (ρ=0.55) correlation with the success rate of ERD classification and had a moderately negative correlation (ρ=-0.60) with the ERD happened time.
In conclusion, this research has developed a single-axis rehabilitation robot for upper limbs controlled by a hybrid brain-computer interface system, which can perform rehabilitation simulations on normal subjects and proves that the hybrid feature system has a better effect on neurorehabilitation. In the future, more subjects especially stroke patients should be recruited to test the system to improve the reliability and robustness of the system.
論文目次 摘要 i
Abstract ii
誌謝 iii
List of content iv
List of figures vi
List of tables x
List of Symbols xi
CHAPTER 1 INTRODUCTION 1
1.1 Background and review 1
1.1.1 Stroke and rehabilitation 1
1.1.2 The development of rehabilitation robots 2
1.1.3 Structure of cerebral cortex and EEG 2
1.1.4 Brain-computer interface and EEG measurement 3
1.1.5 Brain-computer interface and stroke rehabilitation 5
1.1.6 The development of hybrid brain-computer interface 6
1.2 Motivations and objectives 7
CHAPTER 2 METHODS 10
2.1 Hybrid Brain-computer Interface Rehabilitation Robot System 10
2.1.1 A single-axis rehabilitation robot for upper extremities 10
2.1.2 The acquisition of EEG 19
2.2 System testing 19
2.2.1 EEG modulation experiment and feature identification 20
2.2.2 Movement modes parameters setting (ROM test) 25
2.3 Online rehabilitation simulation and experimental design 27
2.3.1 Architecture and procedures of rehabilitation simulation 28
2.3.2 The experimental design 30
2.3.3 Subjects 32
CHAPTER 3 RESULTS 33
3.1 The results of offline EEG modulation 33
3.2 The results of online rehabilitation simulation 49
3.2.1 The movement trajectory of the rehabilitation robot 50
3.2.2 Individual performance of the control group 51
3.2.3 individual performance of the experimental group 57
3.2.4 Comparison between groups 64
CHAPTER 4 DISCUSSION 66
4.1 Hybrid brain-computer interface 66
4.1.1 The relationship between SSVEP and ERD 66
4.1.2 Comparison with related studies 66
4.2 Adjustment of the algorithm on ERD classification 67
4.3 Improvement of the process, movements and mechanism 74
4.3.1 Mechanism reinforcement and motion modification 74
4.3.2 Adjustment of the task interface 75
CHAPTER 5 CONCLUSIONS AND SUGGESTIONS 77
5.1 Conclusions 77
5.2 Limitations and Suggestions 77
5.3 Future works 78
References 79
參考文獻 [1] J. R. Shiber, E. Fontane, and A. Adewale, "Stroke Registry: Hemorrhagic vs Ischemic Strokes," The American journal of Emergency Medicine, vol. 28, no. 3, pp. 331-333, 2010.
[2] S. M. Hatem et al., "Rehabilitation of motor function after stroke: a multiple systematic review focused on techniques to stimulate upper extremity recovery," Frontiers in Human Neuroscience, vol. 10, p. 442, 2016.
[3] S. W. O Driscoll and N. J. Giori, "Continuous passive motion (CPM): theory and principles of clinical application," Journal of Rehabilitation research and development, vol. 37, no. 2, pp. 179-188, 2000.
[4] E. L. Altschuler et al., "Rehabilitation of hemiparesis after stroke with a mirror," The Lancet, vol. 353, no. 9169, pp. 2035-2036, 1999.
[5] S. Hoermann et al., "Computerized Mirror Therapy with Augmented Reflection Technology for Early Stroke Rehabilitation: Clinical Feasibility and Integration as an Adjunct Therapy," Disability and Rehabilitation, Proceedings Paper vol. 39, no. 15, pp. 1503-1514, 2017.
[6] L. Oujamaa, I. Relave, J. Froger, D. Mottet, and J.-Y. Pelissier, "Rehabilitation of arm function after stroke. Literature review," Annals of physical and rehabilitation medicine, vol. 52, no. 3, pp. 269-293, 2009.
[7] A. Barzel et al., "Home-based Constraint-Induced Movement Therapy for Patients with Upper Limb Dysfunction after Stroke (HOMECIMT): a Cluster-Randomised, Controlled Trial," The Lancet Neurology, vol. 14, no. 9, pp. 893-902, 2015.
[8] B. R. Kim, M. H. Chun, L. S. Kim, and J. Y. Park, "Effect of Virtual Reality on Cognition in Stroke Patients," Annals of Rehabilitation Medicine, vol. 35, no. 4, p. 450, 2011.
[9] H. S. Lo and S. Q. Xie, "Exoskeleton Robots for Upper-Limb Rehabilitation: State of the Art and Future Prospects," Medical Engineering & Physics, vol. 34, no. 3, pp. 261-268, 2012.
[10] J. M. Veerbeek, A. C. Langbroek-Amersfoort, E. E. Van Wegen, C. G. Meskers, and G. Kwakkel, "Effects of Robot-Assisted Therapy for the Upper Limb after Stroke: a Systematic Review and Meta-analysis," Neurorehabilitation and Neural Repair, vol. 31, no. 2, pp. 107-121, 2017.
[11] H. I. Krebs, B. Volpe, and N. Hogan, "A Working Model of Stroke Recovery from Rehabilitation Robotics Practitioners," Journal of Neuroengineering and Rehabilitation, vol. 6, no. 1, p. 6, 2009.
[12] Ang, Kai & Guan, Cuntai & Phua, Kok Soon & Wang, Chuanchu & Zhou, Longjiang & Tang, Ka Yin & Joseph, Gopal & Kuah, Christopher & Chua, Karen. (2014). Brain-Computer Interface-based robotic end effector system for wrist and hand rehabilitation: results of a three-armed randomized controlled trial for chronic stroke. Frontiers in Neuroengineering. 7. 30. 10.3389/fneng.2014.00030.
[13] R. R. Seeley, T. D. Stephens, and P. Tate, Essentials of Anatomy and Physiology. McGraw-Hill, 2002.
[14] H. Asanuma and A. Keller, "Neurological basis of motor learning and memory," Concepts in Neurosciences, vol. 2, pp. 1-30, 01/01 1991.
[15] R. J. Nudo, "Mechanisms for Recovery of Motor Function Following Cortical damage," Current Opinion in Neurobiology, vol. 16, no. 6, pp. 638-644, 2006.
[16] J. R. Wolpaw, N. Birbaumer, D. J. McFarland, G. Pfurtscheller, and T. M. Vaughan, "Brain–computer interfaces for communication and control," Clinical neurophysiology, vol. 113, no. 6, pp. 767-791, 2002.
[17] M. Teplan, "Fundamentals of EEG measurement," Measurement science review, vol. 2, no. 2, pp. 1-11, 2002.
[18] D. Zhu, J. Bieger, G. Garcia Molina, and R. M. Aarts, "A survey of stimulation methods used in SSVEP-based BCIs," Computational intelligence and neuroscience, vol. 2010, 2010.
[19] G. Pfurtscheller, C. Neuper, D. Flotzinger, and M. Pregenzer, "EEG-based discrimination between imagination of right and left hand movement," Electroencephalography and clinical Neurophysiology, vol. 103, no. 6, pp. 642-651, 1997.
[20] W. Yi, S. Qiu, H. Qi, L. Zhang, B. Wan, and D. Ming, "EEG feature comparison and classification of simple and compound limb motor imagery," Journal of neuroengineering and rehabilitation, vol. 10, no. 1, p. 106, 2013.
[21] N.-S. Kwak, K.-R. Müller, and S.-W. Lee, "A convolutional neural network for steady state visual evoked potential classification under ambulatory environment," PloS one, vol. 12, no. 2, p. e0172578, 2017.
[22] T. Yu, Y. Li, J. Long, and Z. Gu, "Surfing the internet with a BCI mouse," Journal of Neural Engineering, vol. 9, no. 3, p. 036012, 2012/05/25 2012.
[23] C. Pandarinath et al., "High performance communication by people with paralysis using an intracortical brain-computer interface," Elife, vol. 6, p. e18554, 2017.
[24] Y. Li, J. Pan, F. Wang, and Z. Yu, "A hybrid BCI system combining P300 and SSVEP and its application to wheelchair control," IEEE Transactions on Biomedical Engineering, vol. 60, no. 11, pp. 3156-3166, 2013.
[25] G. R. Muller-Putz and G. Pfurtscheller, "Control of an electrical prosthesis with an SSVEP-based BCI," IEEE Transactions on Biomedical Engineering, vol. 55, no. 1, pp. 361-364, 2007.
[26] J. J. Daly and J. R. Wolpaw, "Brain–computer interfaces in neurological rehabilitation," The Lancet Neurology, vol. 7, no. 11, pp. 1032-1043, 2008.
[27] S. R. Soekadar, N. Birbaumer, and L. G. Cohen, "Brain–computer interfaces in the rehabilitation of stroke and neurotrauma," in Systems neuroscience and rehabilitation: Springer, 2011, pp. 3-18.
[28] M. Velliste, S. Perel, M. C. Spalding, A. S. Whitford, and A. B. Schwartz, "Cortical control of a prosthetic arm for self-feeding," Nature, vol. 453, no. 7198, pp. 1098-1101, 2008.
[29] L. R. Hochberg et al., "Reach and grasp by people with tetraplegia using a neurally controlled robotic arm," Nature, vol. 485, no. 7398, pp. 372-375, 2012.
[30] A. B. Ajiboye et al., "Restoration of reaching and grasping movements through brain-controlled muscle stimulation in a person with tetraplegia: a proof-of-concept demonstration," The Lancet, vol. 389, no. 10081, pp. 1821-1830, 2017.
[31] J. J. Daly, R. Cheng, J. Rogers, K. Litinas, K. Hrovat, and M. Dohring, "Feasibility of a new application of noninvasive brain computer interface (BCI): a case study of training for recovery of volitional motor control after stroke," Journal of neurologic physical therapy, vol. 33, no. 4, pp. 203-211, 2009.
[32] P.-C. Kung, C.-C. K. Lin, M.-S. Ju, and S.-M. Chen, "Reducing abnormal synergies of forearm, elbow, and shoulder joints in stroke patients with neuro-rehabilitation robot treatment and assessment," Journal of Medical and Biological Engineering, vol. 32, no. 2, pp. 139-146, 2009.
[33] N. Mrachacz-Kersting et al., "Efficient neuroplasticity induction in chronic stroke patients by an associative brain-computer interface," Journal of neurophysiology, vol. 115, no. 3, pp. 1410-1421, 2016.
[34] A. Biasiucci et al., "Brain-actuated functional electrical stimulation elicits lasting arm motor recovery after stroke," Nature communications, vol. 9, no. 1, pp. 1-13, 2018.
[35] G. Pfurtscheller et al., "The hybrid BCI," Frontiers in neuroscience, vol. 4, p. 3, 2010.
[36] I. Choi, I. Rhiu, Y. Lee, M. H. Yun, and C. S. Nam, "A systematic review of hybrid brain-computer interfaces: Taxonomy and usability perspectives," PloS one, vol. 12, no. 4, p. e0176674, 2017.
[37] B. Choi and S. Jo, "A low-cost EEG system-based hybrid brain-computer interface for humanoid robot navigation and recognition," PloS one, vol. 8, no. 9, 2013.
[38] B. Guragain, A. Haider, and R. Fazel-Rezai, "Hybrid Brain-Computer Interface Systems: Approaches, Features, and Trends," Evolving BCI Therapy: Engaging Brain State Dynamics, p. 113, 2018.
[39] A. M. Savić, N. M. Malešević, and M. B. Popović, "Feasibility of a hybrid brain-computer interface for advanced functional electrical therapy," The Scientific World Journal, vol. 2014, 2014.
[40] A. M. Savić and M. B. Popović, "Brain Computer Interface Prototypes for Upper Limb Rehabilitation: a review of principles and experimental results," in 2015 23rd Telecommunications Forum Telfor (TELFOR), 2015, pp. 452-459: IEEE.
[41] R. Leeb, H. Sagha, R. Chavarriaga, and J. D. Millan, "A hybrid brain-computer interface based on the fusion of electroencephalographic and electromyographic activities," (in English), Journal of Neural Engineering, Article; Proceedings Paper vol. 8, no. 2, p. 5, Apr 2011, Art. no. 025011.
[42] J. Y. Long, Y. Q. Li, H. T. Wang, T. Y. Yu, J. H. Pan, and F. Li, "A Hybrid Brain Computer Interface to Control the Direction and Speed of a Simulated or Real Wheelchair," (in English), Ieee Transactions on Neural Systems and Rehabilitation Engineering, Article vol. 20, no. 5, pp. 720-729, Sep 2012.
[43] L. Cao, J. Li, H. F. Ji, and C. J. Jiang, "A Hybrid Brain Computer Interface System Based on the Neurophysiological Protocol and Brain-actuated Switch for Wheelchair Control," Journal of Neuroscience Methods, vol. 229, pp. 33-43, 2014.
[44] J. X. Ma, Y. Zhang, A. Cichocki, and F. Matsuno, "A Novel EOG/EEG Hybrid Human-Machine Interface Adopting Eye Movements and ERPs: Application to Robot Control," Ieee Transactions on Biomedical Engineering, Article vol. 62, no. 3, pp. 876-889, Mar 2015.
[45] F. Grimm, A. Walter, M. Spuler, G. Naros, W. Rosenstiel, and A. Gharabaghi, "Hybrid Neuroprosthesis for the Upper Limb: Combining Brain-Controlled Neuromuscular Stimulation with a Multi-Joint Arm Exoskeleton," (in English), Frontiers in Neuroscience, vol. 10, p. 11, Aug 2016, Art. no. 367.
[46] 黃國維, "利用視覺引導及混合特徵發展控制復健矯形手即時回饋之腦機介面," 碩士, 機械工程學系, 國立成功大學, 台南市, 2017.
[47] 龔品誠, "神經復健機器人於中風病患肩肘與前臂關節不正常協同動作之評估及治療,"國立成功大學, 機械工程學系博士論文, 台南市, 2011.
[48] C.-W. Chen, C.-C. K. Lin, and M.-S. Ju, "Hand Orthosis Controlled Using Brain-Computer Interface," Journal of Medical and Biological Engineering, vol. 29, no. 5, pp. 234-241, 2009.
[49] G. Pfurtscheller and F. L. Da Silva, "Event-Related EEG/MEG Synchronization and Desynchronization: Basic Principles," Clinical neurophysiology, vol. 110, no. 11, pp. 1842-1857, 1999.
[50] 黃繪禎, "EEG腦機介面控制肩肘機器人於中風病患復健研究," 碩士, 機械工程學系碩博士班, 國立成功大學, 台南市, 2012.
[51] 宋信毅, "EEG控制肩肘復健機器人對中風病患復健與功能性磁振造影評估," 碩士, 機械工程學系碩博士班, 國立成功大學, 台南市, 2013.
論文全文使用權限
  • 同意授權校內瀏覽/列印電子全文服務,於2025-08-01起公開。
  • 同意授權校外瀏覽/列印電子全文服務,於2025-08-01起公開。


  • 如您有疑問,請聯絡圖書館
    聯絡電話:(06)2757575#65773
    聯絡E-mail:etds@email.ncku.edu.tw