Brain-Computer Interfaces: Lab Experiments to Real-World Applications

 
 
Academic Press
  • 1. Auflage
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  • erschienen am 27. August 2016
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  • 434 Seiten
 
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978-0-12-809262-0 (ISBN)
 

Brain-Computer Interfaces: Lab Experiments to Real-World Applications, the latest volume in the Progress in Brain Research series, focuses on new trends and developments. This established international series examines major areas of basic and clinical research within the neurosciences, as well as popular and emerging subfields.


  • Explores new trends and developments in brain research
  • Enhances the literature of neuroscience by further expanding this established, ongoing international series
  • Examines major areas of basic and clinical research within the field
0079-6123
  • Englisch
  • San Diego
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  • Niederlande
Elsevier Science
  • 27,14 MB
978-0-12-809262-0 (9780128092620)
0128092629 (0128092629)
weitere Ausgaben werden ermittelt
  • Front Cover
  • Brain-Computer Interfaces: Lab Experiments to Real-World Applications
  • Copyright
  • Contributors
  • Contents
  • Preface
  • Part I: User Training
  • Chapter 1: Advances in user-training for mental-imagery-based BCI control: Psychological and cognitive factors and their ne ...
  • 1. Introduction
  • 2. Psychological and Cognitive Factors Related to MI-BCI Performance
  • 2.1. Emotional and Cognitive States That Impact MI-BCI Performance
  • 2.2. Personality and Cognitive Traits That Influence MI-BCI Performance
  • 2.3. Other Factors Impacting MI-BCI Performance: Demographic Characteristics, Experience, and Environment
  • 2.4. To Summarize: MI-BCI Performance Is Affected by the Users (1) Relationship with Technology, (2) Attention, and (3) S ...
  • 3. The User-Technology Relationship: Introducing the Concepts of Computer Anxiety and Sense of Agency-Definition and Neur ...
  • 3.1. Apprehension of Technology: The Concept of CA-Definition
  • 3.2. ``I did That!´´: The Concept of Sense of Agency-Definition
  • 3.3. ``I did That!´´: The Concept of Sense of Agency-Neural Correlates
  • 4. Attention-Definition and Neural Correlates
  • 4.1. Attention-Definition
  • 4.2. Attention-Neural Correlates
  • 5. Spatial Abilities-Definition and Neural Correlates
  • 5.1. Spatial Abilities-Definition
  • 5.2. Spatial Abilities-Neural Correlates
  • 6. Perspectives: The User-Technology Relationship, Attention, and Spatial Abilities as Three Levers to Improve MI-BCI Use ...
  • 6.1. Demonstrating the Impact of the Protocol on CA and Sense of Agency
  • 6.2. Raising and Improving Attention
  • 6.3. Increasing Spatial Abilities
  • 7. Conclusion
  • References
  • Part II: Non-Invasive Decoding of 3D Hand and Arm Movements
  • Chapter 2: From classic motor imagery to complex movement intention decoding: The noninvasive Graz-BCI approach
  • 1. Overview
  • 2. Methods
  • 2.1. Classic Motor Imagination
  • 2.1.1. SMR-based BCIs for control
  • 2.1.2. SMR-based BCIs for communication
  • 2.1.3. SMR-based BCI training (classic vs adaptive)
  • 2.2. Decoding Motor Execution
  • 2.3. Decoding Motor Imagination
  • 2.4. Decoding Movement Targets
  • 2.5. Decoding Movement Goals
  • 3. Conclusion
  • Acknowledgment
  • References
  • Chapter 3: 3D hand motion trajectory prediction from EEG mu and beta bandpower
  • 1. Introduction
  • 2. Methods
  • 2.1. Experimental Paradigm
  • 2.2. Data Acquisition
  • 2.3. Data Preprocessing
  • 2.3.1. EEG data preprocessing
  • 2.3.1.1. Re-referencing and bandpass filtering
  • 2.3.1.2. Independent component analysis
  • 2.3.1.3. The potential time-series model
  • 2.3.1.4. The bandpower time-series model
  • 2.3.2. Kinematic data preprocessing
  • 2.3.3. Data synchronization, data validation, and task interval separation
  • 2.4. Kinematic Data Reconstruction
  • 2.4.1. Multiple linear regression
  • 2.5. Architecture Optimization, Training, Test, and Cross-Validation
  • 2.5.1. Data separation for inner-outer cross-validation
  • 2.5.2. Optimization 1: Time lag and embedding dimension
  • 2.5.3. Optimization 2: Channel selection and topological analysis
  • 2.5.4. Optimization 3: Reoptimization of time lag and embedding dimension
  • 2.5.5. Calculating the final results and cross-validation
  • 3. Results
  • 3.1. The Optimal Time Lag and Embedding Dimension
  • 3.2. The Optimal Channel Sets
  • 3.3. Accuracy of Trajectory Reconstruction
  • 4. Discussion
  • 4.1. Prominent Cortical Areas
  • 4.2. Prominent Bands/Results of the PTS and the BTS Model
  • 4.3. Inner-Outer (Nested) Cross-Validation for MTP BCIs
  • 4.4. Target Shuffling Test for Final Result Validation
  • 4.5. Limitations and Future Work
  • 5. Conclusion
  • References
  • Chapter 4: Multisession, noninvasive closed-loop neuroprosthetic control of grasping by upper limb amputees
  • 1. Introduction
  • 2. Materials and Methods
  • 2.1. Study Participants
  • 2.2. Data Acquisition and Instrumentation/Hardware
  • 2.3. Experiment Design
  • 2.4. Signal Processing
  • 2.4.1. Decoding
  • 3. Results
  • 3.1. Closed-Loop Grasping Performance Was Stable over Sessions
  • 3.2. Long-Term Stability of EEG Signal Features and Decoders
  • 4. Discussion
  • 4.1. Multisession, Closed-Loop BMI Performance
  • 4.2. Closed-Loop BMI and Multisession EEG Stability
  • 4.3. Implications for Noninvasive BMIs
  • Acknowledgments
  • References
  • Part III: Patients Studies and Clinical Applications
  • Chapter 5: Brain-computer interfaces in the completely locked-in state and chronic stroke
  • 1. Historical Perspective
  • 1.1. Initial Setback
  • 1.2. Early Successes
  • 2. Types of BCI
  • 3. BCI for Communication in Paralysis due to ALS
  • 3.1. Invasive BCI for Communication
  • 3.2. Noninvasive BCIs for Communication
  • 3.2.1. The SCP-BCI
  • 3.2.2. The SMR-BCI
  • 3.2.3. The P300-BCI
  • 3.3. Learning BCI Control in Paralysis
  • 3.3.1. The role of the basal ganglia in the learned acquisition of brain control
  • 3.4. Functional Near-Infrared Spectroscopy-Based BCI for Communication in CLIS
  • 4. BCIs for Chronic Stroke
  • 4.1. Stroke Rehabilitation Strategies
  • 4.2. Stroke BCIs Studies
  • 4.3. Taking Advantage of Brain Stimulation
  • 5. Future Perspective
  • Acknowledgments
  • References
  • Chapter 6: Brain-machine interfaces for rehabilitation of poststroke hemiplegia
  • 1. Introduction
  • 2. Signal Modality of BMI
  • 3. Identification of Biomarkers for BMI Motor Rehabilitation
  • 4. BMI Motor Rehabilitation and Its Outcome
  • 5. Possible Mechanisms Underlying BMI Motor Rehabilitation Training-Related Functional Recovery
  • 5.1. Use-Dependent Plasticity
  • 5.2. Hebbian (Timing Dependent) Plasticity
  • 5.3. Reward-Based Reinforcement Learning
  • 5.4. Error-Based Learning
  • 6. Clinical Positioning
  • 7. Future of BMI Motor Rehabilitation
  • 8. Unsolved Issues and Questions
  • 9. Concluding Remarks
  • Acknowledgments
  • References
  • Chapter 7: Neural and cortical analysis of swallowing and detection of motor imagery of swallow for dysphagia rehabilitati ...
  • 1. Background
  • 1.1. Introduction
  • 1.2. Swallowing Process and Assessment
  • 1.3. Objectives and Motivation
  • 2. Neural and Cortical Analysis of Swallowing
  • 2.1. Cortical Network of Swallowing
  • 2.2. Brain Activations of Swallowing and Tongue
  • 2.3. Cortical Activity of Swallowing for Patients
  • 3. Detection of MI-SW
  • 3.1. Overview of Detection of MI-SW
  • 3.2. Feature Extraction and Model Adaptation
  • 3.2.1. Feature extraction
  • 3.2.2. Model adaptation
  • 3.3. Neural Cortical Correlates of MI-SW with ME-SW
  • 3.4. Implications for Clinical Use
  • 4. Detection of MI-TM
  • 4.1. ICA and FBCSP-Based Detection
  • 4.2. Predictive-Spectral-Spatial Preprocessing for a Multiclass BCI, Prediction BCI, and Short-Time Fourier Transform-Bas ...
  • 5. Implications and Future Directions
  • 5.1. Future Directions for Neural Analysis of Swallowing
  • 5.2. Future Directions on the Rehabilitation of Stroke Dysphagia Patients
  • References
  • Chapter 8: A cognitive brain-computer interface for patients with amyotrophic lateral sclerosis
  • 1. Introduction
  • 1.1. Amyotrophic Lateral Sclerosis
  • 1.2. Brain-Computer Interfaces
  • 1.3. The Current Work
  • 2. Methods
  • 2.1. Experimental Paradigm
  • 2.2. Experimental Data
  • 2.3. EEG Analysis
  • 2.3.1. Attenuation of EMG Artifacts
  • 2.3.2. Preprocessing
  • 2.3.3. Pattern Classification
  • 2.3.4. Statistical Test on Decoding Accuracy
  • 2.3.5. Spatial Distribution
  • 2.3.6. Dynamics of Induced Bandpower Changes and Timecourse of Classification Accuracy
  • 3. Results
  • 4. Discussion
  • References
  • Chapter 9: Brain-computer interfaces for patients with disorders of consciousness
  • 1. The Disorders of Consciousness
  • 2. The Challenges of Communicating with a Damaged Brain
  • 3. BCIs for Patients with DoC
  • 3.1. Electroencephalography
  • 3.1.1. Sensorimotor rhythms
  • 3.1.1.1. Neural basis of SMRs
  • 3.1.1.2. SMR-based BCIs
  • 3.1.1.2.1. Alternative mental tasks
  • 3.1.1.2.2. Attempted movement
  • 3.1.1.2.3. Motor observation
  • 3.1.1.2.4. The importance of training and feedback
  • 3.1.1.2.5. The roles of motor capacity and attention in SMR-based BCI use
  • 3.1.1.3. The feasibility of SMR-based BCIs for long-term use
  • 3.1.2. P300 event-related potentials
  • 3.1.2.1. Neural basis of the P300
  • 3.1.2.2. Visual P300-based BCIs
  • 3.1.2.3. Auditory P300-based BCIs for patients with DoC
  • 3.1.2.3.1. Global-local predictive coding
  • 3.1.2.3.2. Subject's own name
  • 3.1.2.3.3. Auditory stream segregation
  • 3.1.2.3.4. Oddballs as words
  • 3.1.2.4. Tactile P300-based BCIs for patients with DoC
  • 3.1.2.5. P300-based BCI optimization and feasibility of long-term use
  • 3.1.3. Steady-state evoked potentials
  • 3.1.3.1. Visual SSEP-based BCIs
  • 3.1.3.2. Auditory SSEP-based BCIs
  • 3.1.3.3. Somatosensory SSEP-based BCIs
  • 3.1.4. Slow cortical potentials
  • 3.2. Single- or Multiunit Neuronal Activity
  • 3.3. BOLD Response
  • 4. Summary and Recommendations
  • References
  • Part IV: Non-Medical Applications
  • Chapter 10: A passive brain-computer interface application for the mental workload assessment on professional air traffic c ...
  • 1. Introduction
  • 1.1. Passive Brain-Computer Interface
  • 1.2. Mental Workload: The Mean and Its Neurophysiological Measurements
  • 1.3. An Example of Mental Workload Measure in Realistic Settings: The Air Traffic Management Case
  • 1.4. Present Study
  • 2. Materials and Methods
  • 2.1. Experimental Protocol
  • 2.1.1. Subjects
  • 2.1.2. Experimental task
  • 2.1.3. Collected data related to the mental workload of ATCOs
  • 2.1.3.1. Neurophysiological data
  • 2.1.3.2. Subjective workload assessment (self-assessment)
  • 2.1.3.3. Subjective Workload Assessment (SME Assessment)
  • 2.1.4. People involved and study organization
  • 2.2. Neurophysiological Data Analysis
  • 2.2.1. EEG signal processing
  • 2.2.2. EEG-based mental workload index
  • 2.3. Performed Data Analyses
  • 2.3.1. Reliability over time of the neurophysiological workload measure
  • 2.3.1.1. Subjective workload assessment
  • 2.3.1.2. EEG-based workload assessment
  • 2.3.1.2.1. EEG features selection analysis
  • 2.3.2. Comparison between neurophysiological and subjective workload evaluation
  • 2.3.2.1. Self-workload assessment
  • 2.3.2.2. EEG-based workload assessment
  • 2.3.2.3. Accuracy of neurophysiological measurement in comparison with standard workload assessment
  • 3. Results
  • 3.1. Overtime Stability of the EEG-Based Workload Measure
  • 3.1.1. Self-workload assessment
  • 3.1.2. EEG-based workload assessment
  • 3.1.2.1. EEG features selection analysis
  • 3.1.3. Comparison between EEG-based and subjective workload assessment
  • 3.1.3.1. Subjective assessment
  • 3.1.3.2. EEG-based workload assessment
  • 3.1.3.3. Accuracy of neurophysiological measurement in comparison with standard workload assessment
  • 4. Discussion
  • 5. Conclusion
  • Acknowledgments
  • References
  • Chapter 11: 3D graphics, virtual reality, and motion-onset visual evoked potentials in neurogaming
  • 1. Introduction
  • 2. Methodology
  • 2.1. Data Acquisition
  • 2.2. Study 1-Graphical Complexity (Basic Games)
  • 2.3. Study 2-Graphical Complexity (Commercial Games)
  • 2.4. Study 3-OCR vs LCD Screen
  • 3. Data Analysis
  • 3.1. Data Preprocessing
  • 3.2. mVEP Classification-Training Data
  • 3.3. mVEP Classification-Testing Data
  • 4. Results
  • 4.1. Study 1-Comparing Graphical Complexity (Basic Games)
  • 4.2. Study 2-Comparing Graphical Complexity (Commercial Games)
  • 4.3. Study 3-OCR vs LCD Screen
  • 4.4. Studies 1-3-Best Channels
  • 5. Discussion
  • 6. Conclusion
  • References
  • Part V: BCI in Practice and Usability Considerations
  • Chapter 12: Interfacing brain with computer to improve communication and rehabilitation after brain damage
  • 1. Introduction
  • 2. Multidisciplinary Approach to BCI Design
  • 2.1. BCI Users in Clinical Contexts
  • 2.2. User Needs and Usability Evaluation
  • 2.2.1. Communication and control applications
  • 2.2.2. Rehabilitation applications
  • 3. Replacing Communication and Control
  • 3.1. BCIs for Communication in End-Users
  • 4. Improving Motor and Cognitive Function
  • 4.1. Motor Rehabilitation
  • 4.2. Cognitive Rehabilitation
  • 4.3. Harnessing Brain Reorganization via BCI
  • 5. Conclusion and Future Perspectives
  • Acknowledgments
  • References
  • Chapter 13: BCI in practice
  • 1. Overview of Common BCI Systems
  • 2. Some Issues in Applications for End Users
  • 3. Studies with End Users
  • Acknowledgments
  • References
  • Index
  • Other volumes in Progress in Brain Research
  • Back Cover

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