6 research outputs found
Improving BrainβMachine Interface Performance by Decoding Intended Future Movements
Objective. A brainβmachine interface (BMI) records neural signals in real time from a subject\u27s brain, interprets them as motor commands, and reroutes them to a device such as a robotic arm, so as to restore lost motor function. Our objective here is to improve BMI performance by minimizing the deleterious effects of delay in the BMI control loop. We mitigate the effects of delay by decoding the subject\u27s intended movements a short time lead in the future. Approach. We use the decoded, intended future movements of the subject as the control signal that drives the movement of our BMI. This should allow the user\u27s intended trajectory to be implemented more quickly by the BMI, reducing the amount of delay in the system. In our experiment, a monkey (Macaca mulatta) uses a future prediction BMI to control a simulated arm to hit targets on a screen. Main Results. Results from experiments with BMIs possessing different system delays (100, 200 and 300 ms) show that the monkey can make significantly straighter, faster and smoother movements when the decoder predicts the user\u27s future intent. We also characterize how BMI performance changes as a function of delay, and explore offline how the accuracy of future prediction decoders varies at different time leads. Significance. This study is the first to characterize the effects of control delays in a BMI and to show that decoding the user\u27s future intent can compensate for the negative effect of control delay on BMI performance
Classification of Movement Intention Using Independent Components of Premovement EEG
Many previous studies on brain-machine interfaces (BMIs) have focused on electroencephalography (EEG) signals elicited during motor-command execution to generate device commands. However, exploiting pre-execution brain activity related to movement intention could improve the practical applicability of BMIs. Therefore, in this study we investigated whether EEG signals occurring before movement execution could be used to classify movement intention. Six subjects performed reaching tasks that required them to move a cursor to one of four targets distributed horizontally and vertically from the center. Using independent components of EEG acquired during a premovement phase, two-class classifications were performed for left vs. right trials and top vs. bottom trials using a support vector machine. Instructions were presented visually (test) and aurally (condition). In the test condition, accuracy for a single window was about 75%, and it increased to 85% in classification using two windows. In the control condition, accuracy for a single window was about 73%, and it increased to 80% in classification using two windows. Classification results showed that a combination of two windows from different time intervals during the premovement phase improved classification performance in the both conditions compared to a single window classification. By categorizing the independent components according to spatial pattern, we found that information depending on the modality can improve classification performance. We confirmed that EEG signals occurring during movement preparation can be used to control a BMI
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Όλ¬Έ (λ°μ¬) -- μμΈλνκ΅ λνμ : μ¬λ²λν 체μ‘κ΅μ‘κ³Ό, 2021. 2. λ°μ¬λ².The continuously varied states of human body and surrounding environment require instantaneous motor adaptations and the understanding of motor goal to achieve desired actions. These sensory and cognitive processes have been investigated as elements in motor control during last five decades. Specially, the task dependency on sensory and cognitive processes suggest the effects of movement properties in terms of environment situation and motor goal. However, these effects were mostly empirically summarized with the measurements of either neural activity or simple motor accomplishment unilaterally. The current thesis addresses the quantification of sensory and cognitive processes based on simultaneous measurements of brain activity and synergic motor performance during multi-digit actions with different movement properties. Multi-digit action as a representation of synergic movements has developed into a widespread agency to quantify the efficacy of motor control, as the reason applied in this thesis.
In this thesis, multi-digit rotation and pressing tasks were performed with different movement directions, frequencies, feedback modalities, or task complexities. (Chapter 3) The changes of movement direction induced a decrease in motor synergy but regardless of which direction. (Chapter 4 and 5) Increased frequency of rhythmic movement reduced synergic motor performance associate with decreased sensory process and less efficient cognitive process. (Chapter 6) More comprehensive feedback modality improved synergic performance with increased sensory process. (Chapter 7) Increased movement complexity had a consistent but stronger effect as increased frequency on synergic performance and efficiency of cognitive process. These observations suggest that several movement properties affect the contributions of sensory and cognitive processes to motor control which can be quantified through either neural activity or synergic motor performance. Accordingly, those movement properties may be applied in the rehabilitation of motor dysfunction by developing new training programs or assistant devices. Additionally, it may be possible to develop a simplified while efficient method to estimate the contribution of sensory or cognitive process to motor control.μμκ°κ°μΌλ‘ λ³ννλ μ 체 μνμ μ£Όλ³ νκ²½μ μνΈμμ© μμμ μλ§μ μμ§μμ μννκΈ° μν΄μλ κ·Έμ λ°λ₯Έ μ¦κ°μ μΈ μ΄λ μ μ(motor adaptation) κ³Όμ μ κ³Όμ λͺ©νμ λν μ΄ν΄κ° νμνλ€. μ΄λ₯Ό μν΄ μΈκ°μ κ°κ° λ° μΈμ§ μ²λ¦¬κ³Όμ μ μ΄λ μ μ΄ λΆμΌμ μ€μν μμλ‘ μ¬κ²¨μ‘λ€. μ νμ°κ΅¬μ λ°λ₯΄λ©΄, μ΄λ κ³Όμ μ λ°λΌ λ³ννλ κ°κ° λ° μΈμ§ μ²λ¦¬κ³Όμ μ μ£Όλ³ νκ²½κ³Ό κ³Όμ μ λͺ©νμ λ°λΌ μμ§μμ νΉμ±μ μν₯μ λ―ΈμΉλ€κ³ λ³΄κ³ λμ΄μλ€. κ·Έλ¬λ μ΄λ¬ν μν₯μ λλΆλΆ λ¨μν μ΄λκ³Όμ μν κ²°κ³Ό λλ μΈ‘μ λ μ κ²½ νλμ μν΄ κ²½νμ μΌλ‘ μμ½λ κ²°κ³Όμ κ΅νλμ΄ μλ€. λ°λΌμ λ³Έ λ
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1.1 Problem statement 1
1.2 Study objective 2
1.3 Organization of dissertation 3
Chapter 2. Background 6
2.1 Motor system 6
2.1.1 Ascending pathway 6
2.1.2 Descending pathway 8
2.1.3 Brain networks 9
2.2 Motor synergy 11
2.2.1 Synergy in performance 12
2.2.2 Synergy in muscles 13
2.2.3 Synergy in neurons 14
2.3 Motor control 15
2.1.1 Sensory process 16
2.1.2 Cognitive process 19
Chapter 3. Effect of movement direction: Multi-Finger Interaction and Synergies in Finger Flexion and Extension Force Production 23
3.1 Abstract 23
3.2 Introduction 24
3.3 Method 28
3.4 Results 35
3.4.1 Maximal voluntary contraction (MVC) force and finger independency 36
3.4.2 Timing indices 37
3.4.3 Multi-finger synergy indices in mode space 39
3.4.4 Multi-finger synergy indices in force space 43
3.5 Discussion 44
3.5.1 Finger independency during finger flexion and extension 44
3.5.2 Multi-finger synergies in force and mode spaces 46
3.5.3 Anticipatory synergy adjustment 48
Chapter 4. Effect of Frequency: Brain Oxygenation Magnitude and Mechanical Outcomes during Multi-Digit Rhythmic Rotation Task 51
4.1 Abstract 51
4.2 Introduction 51
4.3 Methods 55
4.4 Results 61
4.4.1 PET imaging 61
4.4.2 Finger forces 62
4.4.3 UCM analysis 64
4.4.4 Correlation between neural activation and mechanics 65
4.5 Discussion 66
4.5.1 Regions involved in feedback 67
4.5.2 Regions involved in feedforward 69
4.5.3 Corporation of feedforward and feedback 71
4.6 Conclusions 72
Chapter 5. Effect of frequency: Prefrontal Cortex Oxygenation during Multi-Digit Rhythmic Pressing Actions using fNIRS 74
5.1 Abstract 74
5.2 Introduction 74
5.3 Method 77
5.4 Results 84
5.4.1 Performance 84
5.4.2 Multi-digit coordination indices 84
5.4.3 Functional connectivity (FC) 87
5.5 Discussion 88
5.6 Conclusion 91
Chapter 6. Effect of Sensory Modality: Multi-Sensory Integration during Multi-Digit Rotation Task with Different Frequency 92
6.1 Abstract 92
6.2 Introduction 92
6.3 Method 94
6.4 Results 100
6.4.1 Performance 100
6.4.2 Multi-digit coordination indices 101
6.5 Discussion 101
6.6 Conclusion 103
Chapter 7. Effect of Task Complexity: Prefrontal Cortex Oxygenation during Multi-Digit Pressing Actions with Different Frequency Components 104
7.1 Abstract 104
7.2 Introduction 104
7.3 Method 106
7.4 Results 112
7.4.1 Performance 112
7.4.2 Multi-finger coordination indices 113
7.4.3 Functional connectivity (FC) 114
7.5 Discussion 115
7.5.1 Relation between Frequency and task complexity 115
7.5.2 Cognitive process in motor control 116
7.5.3 Relation between motor coordination and cognitive process 118
7.6 Conclusion 119
Chapter 8. Conclusions and Future Work 120
8.1 Summary of conclusions 120
8.2 Future work 121
Bibliography 122
Abstract in Korean 160Docto