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Brain-computer interface (BCI) technology provides a means of communication that allows individuals with severely impaired movement to communicate with assistive devices using the electroencephalogram (EEG) or other brain signals. The practicality of a BCI has been possible due to advances in multi-disciplinary areas of research related to cognitive neuroscience, brain-imaging techniques and human-computer interfaces. However, two major challenges remain in making BCI for assistive robotics practical for day-to-day use: the inherent lower bandwidth of BCI, and how to best handle the unknown embedded noise within the raw EEG.
Brain-Computer Interfacing for Assistive Robotics is a result of research focusing on these important aspects of BCI for real-time assistive robotic application. It details the fundamental issues related to non-stationary EEG signal processing (filtering) and the need of an alternative approach for the same. Additionally, the book also discusses techniques for overcoming lower bandwidth of BCIs by designing novel use-centric graphical user interfaces. A detailed investigation into both these approaches is discussed.
- An innovative reference on the brain-computer interface (BCI) and its utility in computational neuroscience and assistive robotics
- Written for mature and early stage researchers, postgraduate and doctoral students, and computational neuroscientists, this book is a novel guide to the fundamentals of quantum mechanics for BCI
- Full-colour text that focuses on brain-computer interfacing for real-time assistive robotic application and details the fundamental issues related with signal processing and the need for alternative approaches
- A detailed introduction as well as an in-depth analysis of challenges and issues in developing practical brain-computer interfaces.
Graduate students, post-doctoral fellows, neuroscientists, and computational neuroscientists, as well as researchers interested in robotics, biomedical signal processing, and the brain-computer interface
- List of Figures
- List of Tables
- List of Acronyms
- Chapter 1. Introduction
- 1.1 Introduction
- 1.2 Rationale
- 1.3 Objectives
- Chapter 2. Interfacing Brain and Machine
- 2.1 Introduction
- 2.2 The Brain and Electrode Placement
- 2.3 Operational Techniques in BCI
- 2.4 Data Acquisition
- 2.5 Preprocessing: A Signal Enhancement Requirement along with Noise Reduction
- 2.6 Feature Extraction
- 2.7 Classification
- 2.8 Post-processing
- 2.9 Validation and Optimization Techniques
- 2.10 Graphical User Interface [GUI]
- 2.11 Strategies in BCI Applications
- 2.12 Performance Measures of a BCI System
- 2.13 Conclusion
- Chapter 3. Fundamentals of Recurrent Quantum Neural Networks
- 3.1 Introduction
- 3.2 Postulates of Quantum Mechanics
- 3.3 Quantum Mechanics and the Schrodinger Wave Equation
- 3.4 Theoretical Concept of the RQNN Model
- 3.5 Traditional RQNN-Based Signal Enhancement
- 3.6 Revised RQNN-Based Signal Enhancement
- 3.7 Discussion
- 3.8 Conclusion
- Chapter 4. The Proposed Graphical User Interface (GUI)
- 4.1 Introduction
- 4.2 Overview of the Proposed GUI Within the BCI Framework
- 4.3 Interfacing MATLAB and Visual Basic
- 4.4 Conclusion
- Chapter 5. Recurrent Quantum Neural Network (RQNN)-Based EEG Enhancement
- 5.1 Introduction
- 5.2 Traditional RQNN Model for EEG Enhancement
- 5.3 Revised RQNN Model for EEG Signal Enhancement
- 5.4 Towards Subject-Specific RQNN Parameters
- 5.5 Discussion
- 5.6 Conclusion
- Chapter 6. Graphical User Interface (GUI) and Robot Operation
- 6.1 Introduction
- 6.2 The EEG Acquisition Process
- 6.3 RQNN-Based EEG Signal Enhancement
- 6.4 Autonomous and Supervised GUI Operation
- 6.5 Maneuvering the Simulated Mobile Robot Using Only MI EEG
- 6.6 Maneuvering the Physical Mobile Robot Using Only MI EEG
- 6.7 Conclusion
- Chapter 7. Conclusion
- 7.1 Contributions of the Book
- 7.2 Future Research Directions
- 7.3 Conclusion
- Appendix A. Understanding Evaluation Quantifiers for the Proposed Interface
- No. of pages:
- © Academic Press 2015
- 24th September 2014
- Academic Press
- Paperback ISBN:
- eBook ISBN:
Vaibhav Gandhi (author) received a First Class (Dist.) degree in Instrumentation & Control engineering in 2000, a First Class (Dist.) Masters degree in Electrical engineering in 2002 and a Ph.D. degree in Computing & Engineering in 2012. He was a recipient of the UK-India Education & Research Initiative (UKIERI) scholarship for his Ph.D. research in the area of Brain-Computer Interface for assistive robotics carried out at the Intelligent Systems Research Center, University of Ulster, UK and partly at IIT Kanpur, India. His Ph.D. focused on quantum mechanics motivated EEG signal processing, and an intelligent adaptive use-centric human-computer interface design for real-time control of a mobile robot for BCI users. His post-doctoral research involved work on shadow-hand multi-fingered mobile robot control using EMG/muscle signals, with contributions in the 3D printing aspects of a robotic hand.
He joined the department of Design Engineering & Mathematics, School of Science & Technology, Middlesex University London in 2013, where he is currently Lecturer in Robotics, Embedded Systems and Real-time Systems.
His research interests include brain-computer interfaces, biomedical signal processing, computational intelligence and neuroscience, use-centric graphical user interfaces, and assistive robotics.
Middlesex University, London, United Kingdom
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