Motor imagery guidance based rehabilitation system for stroke patients using body ownership illusion

다국어 초록 (Multilingual Abstract)

Motor imagery (MI) has emerged as a promising approach in neurorehabilitation, particularly for stroke patients who require non-physical engagement of motor networks. However, MI training remains challenging due to its inherent ambiguity and variability—especially in individuals with cognitive or motor impairments.
This dissertation presents a novel two-part framework for enhancing MI training, combining immersive guidance with real-time neurofeedback. The first component is a motor imagery guidance system that leverages a virtual hand illusion to induce a sense of body ownership and enhance imagery vividness. The second is a neurofeedback system that provides interpretable, continuous feedback based on each individual’s neural activation patterns.
The integrated framework was evaluated through a combination of offline and online experiments, targeting both healthy individuals and stroke patients. The MI guidance system was assessed in both populations and found to effectively elicit vivid and consistent imagery, particularly in stroke patients with limited motor function. The neurofeedback system was then tested in healthy participants under real-time conditions, demonstrating reliable improvements in imagery performance. These were reflected in enhanced feedback scores and stronger modulation of motor-related neural activity, confirmed through both sensor-level and source-level analyses. Collectively, these results highlight the framework’s ability to support MI training across different user groups, with each system component contributing distinct but complementary benefits.
Together, these findings support a closed-loop MI training framework that links input enhancement with adaptive feedback. The system offers a practical, explainable alternative to conventional BCI paradigms and establishes a foundation for future clinical applications in stroke rehabilitation. While exploratory in scope, this work establishes a foundational framework for physiologically grounded MI training systems and informs future clinical translation.

국문 초록 (Abstract)

뇌졸중 환자의 효과적인 재활을 위해, 실제 움직임 없이 뇌의 운동 네트워크를 활성화할 수 있는 운동 상상이 널리 활용되고 있다. 그러나 운동 상상의 본질적인 모호성과 뇌졸중 환자의 운동 및 인지 기능 저하로 인해, 그 효과를 안정적으로 끌어내는 데에는 여전히 어려움이 존재한다.
본 연구는 이러한 문제를 해결하기 위해, 몰입형 안내와 실시간 뇌 상태 기반의 정량적 피드백으로 구성된 새로운 운동 상상 기반 재활 프레임워크를 제안한다. 먼저, 가상손 착각을 활용해 신체 소유감을 유도하여, 상상 수행을 보다 생생하고 일관되게 만들어주는 운동 상상 안내 시스템을 개발하였다. 이 시스템은 뇌졸중 환자를 포함한 대상자에게 적용되어, 기존 운동상상 수행의 한계를 보완할 수 있음을 확인하였다. 또한, 개별 사용자의 뇌 활성 패턴을 기준으로 상상 상태를 평가하고 실시간 피드백을 제공하는 뉴로피드백 시스템을 구성하고, 건강한 참가자를 대상으로 한 기초 실험을 통해 운동 상상 수행의 점진적 향상과 뇌 반응의 변화를 확인하였다.
두 시스템은 폐루프 구조로 통합되어, 향상된 훈련 신호와 정량적 피드백을 통한 실시간 학습을 연결하는 새로운 재활 훈련 체계를 구성한다. 이는 기존 운동 상상 기반 재활 시스템의 한계를 극복할 수 있는 실용적이고 임상 적용 가능한 대안으로 기대된다.

목차 (Table of Contents)

• Chapter 1. Background 1
• 1.1 Stroke 1
• 1.2 Neuroplasticity 2
• 1.3 Motor Imagery 4
• 1.4 Motor Imagery-based Brain-Computer Interface (MI-BCI) 7
• Chapter 2. Introduction 11
• 2.1 MI-BCI training in stroke rehabilitation 11
• 2.2 Previous attempts to guide MI 12
• 2.2.1 MI guidance system 13
• 2.2.2 Neurofeedback system 15
• 2.3 Objective of the thesis 18
• Chapter 3. Proposed MI guidance system 19
• 3.1 VHI-based MI guidance system 19
• 3.1.1 Development 19
• 3.2 Validation in healthy people 22
• 3.2.1 Participants and experimental setup 22
• 3.2.2 Data Acquisition and Analysis 24
• 3.2.3 Results 26
• 3.2.4 Discussion 29
• 3.2.5 Conclusions 34
• 3.3 Validation in stroke patients 34
• 3.3.1 Participants and experimental setup 35
• 3.3.2 Data Acquisition and Analysis 37
• 3.3.3 Results 41
• 3.4 Conclusion 52
• Chapter 4. Neurofeedback system 53
• 4.1 Proposed Neurofeedback system 53
• 4.2 Validation of the Proposed System 65
• 4.2.1 Implementation Details 65
• 4.2.2 Offline Validation of the Scoring Algorithm 67
• 4.2.3 Online Neurofeedback Application 79
• Chapter 5. General Discussion 90
• 5.1 Revisiting the Research Hypothesis 90
• 5.2 Interpretation of Findings 90
• 5.3 Limitations and Considerations 92
• 5.4 Implications for BCI and Rehabilitation Fields 94
• Chapter 6. Conclusion 97
• 6.1 Summary of Contributions 97
• 6.2 Contributions 97
• 6.3 Closing Remarks 98
• References 99