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Table of Contents
Preface; Acknowledgements; Contents; Nomenclature; 1 Introduction; 1.1 Medical Background and Requirements; 1.2 BCI Systems; 1.3 EMG-Based Neuromuscular Interface; 1.4 Human-Robot Interaction Control; 1.5 Summary; References; 2 State of the Art; 2.1 EEG-Based BCI and Its Challenges; 2.1.1 Steady State Visual Evoked Potentials; 2.1.2 EEG Signal Processing: Improving the SNR; 2.1.3 EEG Signal Processing: Signal Translation and Classification; 2.1.4 Current Limitations; 2.2 EMG and the Neuromuscular Interface; 2.2.1 Applications of sEMG; 2.2.2 sEMG-Based Neuromuscular Interface.
2.2.3 Current Challenges2.3 Neuromusculoskeletal Models for Gait Rehabilitation; 2.3.1 Musculoskeletal Model; 2.3.2 EMG-Driven Models; 2.4 Discussion; 2.5 Summary; References; 3 Signal Processing Methods for SSVEP-Based BCIs; 3.1 Introduction; 3.2 Adjacent Narrow Band Filter (ANBF) Algorithm; 3.2.1 Artefact Reduction; 3.2.2 Frequency Recognition Strategy; 3.3 Methods and Materials; 3.3.1 Experimental Protocol; 3.3.2 EEG Recording and Evaluation; 3.4 Results; 3.5 Discussion; 3.6 Summary; References; 4 SSVEP-Based BCI for Lower Limb Rehabilitation; 4.1 Introduction; 4.2 Methods and Materials.
4.2.1 Subjects and Visual Stimulator4.2.2 SSVEP Signal Processing; 4.2.3 Robotic Exoskeleton Device; 4.2.4 Experimental Protocols; 4.2.5 Control Algorithm; 4.3 Results; 4.4 Discussion; 4.5 Summary; References; 5 A Hybrid BCI for Gaming; 5.1 Introduction; 5.2 BCI Setup; 5.2.1 Signal Recording and Processing; 5.2.2 Super Street Fighter Video Game; 5.3 Experimental Method and Results; 5.3.1 Experimental Protocol; 5.3.2 Results; 5.4 Discussion; 5.5 Summary; References; 6 EMG-Driven Physiological Model for Upper Limb; 6.1 Neuromusculoskeletal Model; 6.1.1 Musculoskeletal Geometry Model.
6.1.2 Musculotendon Model6.1.3 Kinematic Model; 6.2 Model Sensitivity Analysis; 6.2.1 Model Parameters; 6.2.2 Sensitivity Analysis; 6.2.3 Results and Discussion; 6.3 Elbow Physiological Model Validation; 6.3.1 Experimental Setup; 6.3.2 Model Validation; 6.4 Summary; References; 7 Exoskeleton Control Based on Neural Interface; 7.1 Exoskeleton Development; 7.2 Exoskeleton Control; 7.2.1 Control System Design; 7.2.2 Control of the Elbow Joint; 7.3 Human-Robot Interface; 7.3.1 Interface Design and Parameter Tuning; 7.3.2 Graphical User Interface; 7.4 Summary; References.
8 Muscle Force Estimation Model for Gait Rehabilitation8.1 Patient-Specific Muscle Force Estimation; 8.1.1 Patient-Specific Musculoskeletal Model; 8.1.2 Inverse Dynamic Modelling; 8.1.3 Static Optimisation; 8.2 PMFE Evaluation and Results; 8.2.1 PMFE Evaluation; 8.2.2 Simulation Results; 8.2.3 Discussion; 8.3 Human-Inspired Robotic Exoskeleton; 8.4 Biological Command Based Controller; 8.4.1 Dynamic Modelling; 8.4.2 Patient-Specific Muscle Force Estimation; 8.4.3 PMFE Based Feedforward Controller; 8.5 PSBc Evaluation and Results; 8.5.1 Computer Simulation and Results.
2.2.3 Current Challenges2.3 Neuromusculoskeletal Models for Gait Rehabilitation; 2.3.1 Musculoskeletal Model; 2.3.2 EMG-Driven Models; 2.4 Discussion; 2.5 Summary; References; 3 Signal Processing Methods for SSVEP-Based BCIs; 3.1 Introduction; 3.2 Adjacent Narrow Band Filter (ANBF) Algorithm; 3.2.1 Artefact Reduction; 3.2.2 Frequency Recognition Strategy; 3.3 Methods and Materials; 3.3.1 Experimental Protocol; 3.3.2 EEG Recording and Evaluation; 3.4 Results; 3.5 Discussion; 3.6 Summary; References; 4 SSVEP-Based BCI for Lower Limb Rehabilitation; 4.1 Introduction; 4.2 Methods and Materials.
4.2.1 Subjects and Visual Stimulator4.2.2 SSVEP Signal Processing; 4.2.3 Robotic Exoskeleton Device; 4.2.4 Experimental Protocols; 4.2.5 Control Algorithm; 4.3 Results; 4.4 Discussion; 4.5 Summary; References; 5 A Hybrid BCI for Gaming; 5.1 Introduction; 5.2 BCI Setup; 5.2.1 Signal Recording and Processing; 5.2.2 Super Street Fighter Video Game; 5.3 Experimental Method and Results; 5.3.1 Experimental Protocol; 5.3.2 Results; 5.4 Discussion; 5.5 Summary; References; 6 EMG-Driven Physiological Model for Upper Limb; 6.1 Neuromusculoskeletal Model; 6.1.1 Musculoskeletal Geometry Model.
6.1.2 Musculotendon Model6.1.3 Kinematic Model; 6.2 Model Sensitivity Analysis; 6.2.1 Model Parameters; 6.2.2 Sensitivity Analysis; 6.2.3 Results and Discussion; 6.3 Elbow Physiological Model Validation; 6.3.1 Experimental Setup; 6.3.2 Model Validation; 6.4 Summary; References; 7 Exoskeleton Control Based on Neural Interface; 7.1 Exoskeleton Development; 7.2 Exoskeleton Control; 7.2.1 Control System Design; 7.2.2 Control of the Elbow Joint; 7.3 Human-Robot Interface; 7.3.1 Interface Design and Parameter Tuning; 7.3.2 Graphical User Interface; 7.4 Summary; References.
8 Muscle Force Estimation Model for Gait Rehabilitation8.1 Patient-Specific Muscle Force Estimation; 8.1.1 Patient-Specific Musculoskeletal Model; 8.1.2 Inverse Dynamic Modelling; 8.1.3 Static Optimisation; 8.2 PMFE Evaluation and Results; 8.2.1 PMFE Evaluation; 8.2.2 Simulation Results; 8.2.3 Discussion; 8.3 Human-Inspired Robotic Exoskeleton; 8.4 Biological Command Based Controller; 8.4.1 Dynamic Modelling; 8.4.2 Patient-Specific Muscle Force Estimation; 8.4.3 PMFE Based Feedforward Controller; 8.5 PSBc Evaluation and Results; 8.5.1 Computer Simulation and Results.