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Brain-computer interfacing for assis...

Gandhi, Vaibhav,

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Brain-computer interfacing for assistive robotics : = electroencephalograms, recurrent quantum neural networks, and user-centric graphical interfaces /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Brain-computer interfacing for assistive robotics :/ Vaibhav Gandhi.
其他題名:	electroencephalograms, recurrent quantum neural networks, and user-centric graphical interfaces /
作者:	Gandhi, Vaibhav,
面頁冊數:	1 online resource (259 pages) :illustrations (some color), tables
內容註:	1. Introduction -- 1.1. Introduction -- 1.2. Rationale -- 1.3. Objectives -- 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 -- 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.
內容註:	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 -- 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 -- 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 -- 7. Conclusion -- 7.1. Contributions of the Book -- 7.2. Future Research Directions -- 7.3. Conclusion.
標題:	Brain-computer interfaces. -
電子資源:	https://www.sciencedirect.com/science/book/9780128015438
ISBN:	9780128015872

Brain-computer interfacing for assistive robotics : = electroencephalograms, recurrent quantum neural networks, and user-centric graphical interfaces /
Gandhi, Vaibhav,

Brain-computer interfacing for assistive robotics :electroencephalograms, recurrent quantum neural networks, and user-centric graphical interfaces /Vaibhav Gandhi. - 1 online resource (259 pages) :illustrations (some color), tables

Includes bibliographical references and index.

1. Introduction -- 1.1. Introduction -- 1.2. Rationale -- 1.3. Objectives -- 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 -- 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.

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 e.

ISBN: 9780128015872Subjects--Topical Terms:

908299
Brain-computer interfaces.
Index Terms--Genre/Form:

542853
Electronic books.

LC Class. No.: QP360.7 / .G363 2015eb

Dewey Class. No.: 003.5

National Library of Medicine Call No.: QT 36.2

Brain-computer interfacing for assistive robotics : = electroencephalograms, recurrent quantum neural networks, and user-centric graphical interfaces /
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505 0 $a 1. Introduction -- 1.1. Introduction -- 1.2. Rationale -- 1.3. Objectives -- 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 -- 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.
505 8 $a 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 -- 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 -- 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 -- 7. Conclusion -- 7.1. Contributions of the Book -- 7.2. Future Research Directions -- 7.3. Conclusion.
520 $a 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 e.
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