||Application of Hybrid Brain-Computer Interface Integrated with a Single-Axis Robot for Neurorehabilitation
||Department of BioMedical Engineering
steady-state visual evoked potential
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)。
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.
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
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