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E-raamat: Smart Wheelchairs and Brain-computer Interfaces: Mobile Assistive Technologies

Edited by (Assistant Investigator, Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET), Division of Medical Technology, Department of Electronics and Automatics, School of Engineering - UNSJ, Argentina)
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  • Ilmumisaeg: 29-May-2018
  • Kirjastus: Academic Press Inc
  • Keel: eng
  • ISBN-13: 9780128128930
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Smart Wheelchairs and Brain-computer Interfaces: Mobile Assistive TechnologiesSuurem pilt
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 29-May-2018
  • Kirjastus: Academic Press Inc
  • Keel: eng
  • ISBN-13: 9780128128930
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Smart Wheelchairs and Brain-Computer Interfaces: Mobile Assistive Technologies combines the fields of neuroscience, rehabilitation and robotics via contributions from experts in their field to help readers develop new mobile assistive technologies. It provides information on robotics, control algorithm design for mobile robotics systems, ultrasonic and laser sensors for measurement and trajectory planning, and is ideal for researchers in BCI. A full view of this new field is presented, giving readers the current research in the field of smart wheelchairs, potential control mechanisms and human interfaces that covers mobility, particularly powered mobility, smart wheelchairs, particularly sensors, control mechanisms, and human interfaces.

  • Presents the first book that combines BCI and mobile robotics
  • Focuses on fundamentals and developments in assistive robotic devices which are commanded by alternative ways, such as the brain
  • Provides an overview of the technologies that are already available to support research and the development of new products
Contributors xv
Authors' Biographies xix
Chapter 1 Introduction
1(22)
Pablo Diez
1.1 Brain--Computer Interfaces
2(4)
1.1.1 The Brain
3(1)
1.1.2 Measuring Brain Activity
4(1)
1.1.3 Components of a BCI
5(1)
1.2 Pathologies
6(2)
1.2.1 Amyotrophic Lateral Sclerosis
7(1)
1.2.2 Stroke
7(1)
1.2.3 Locked-in Syndrome
7(1)
1.3 Types of BCI
8(4)
1.3.1 Self-Regulated Potentials
8(2)
1.3.2 Event-Related Potentials
10(1)
1.3.3 Invasive Approach: ECoG-Based BCI
11(1)
1.4 Measuring BCI Performance
12(4)
1.4.1 Confusion Matrix
13(1)
1.4.2 Accuracy and Error Rate
13(1)
1.4.3 Cohen's Kappa Coefficient
14(1)
1.4.4 Information Transfer Rate
15(1)
1.5 Other Aspects on BCI
16(2)
1.5.1 Synchronous or Asynchronous Approach
17(1)
1.5.2 BCI Adaptation or User Adaptation
17(1)
1.5.3 From Lab to the User's Home
17(1)
1.6 Outline of the Book
18(5)
References
18(5)
Chapter 2 The Motor System
23(10)
Fernando Chloca
2.1 An Introduction to the Motor System
23(1)
2.2 Disabilities
24(3)
2.2.1 Central Disabilities
24(2)
2.2.2 Peripheral Disabilities
26(1)
2.2.3 Rheumatological Disabilities
27(1)
2.3 Diseases
27(4)
2.3.1 Amyotrophic Lateral Sclerosis
27(1)
2.3.2 Spinal Muscular Atrophy
28(1)
2.3.3 Myopathies
29(1)
2.3.4 Multiple Sclerosis
29(1)
2.3.5 Spinal Cord Injuries
30(1)
2.3.6 Brainstem Injuries
30(1)
2.3.7 Stroke
30(1)
2.4 Further Remarks
31(2)
References
31(2)
Chapter 3 Using Noninvasive Methods to Drive Brain--Computer Interface (BCD: The Role of Electroencephalography and Functional Near-Infrared Spectroscopy in BCI
33(32)
Wei-Peng Teo
David White
Helen Macpherson
3.1 Introduction
34(1)
3.2 Functional Neuroanatomy: The Cerebral Cortex
34(5)
3.2.1 Introduction
34(1)
3.2.2 Anatomy of the Cerebral Cortex
35(1)
3.2.3 Brodmann Areas
36(1)
3.2.4 Motor and Sensory Areas
36(1)
3.2.5 Association Areas
37(1)
3.2.6 Integration Centers
37(1)
3.2.7 Functions of the PFC
38(1)
3.2.8 Cerebral Lateralization
38(1)
3.3 Noninvasive Neuroimaging Techniques
39(12)
3.3.1 Electroencephalography
39(7)
3.3.2 Functional Near-Infrared Spectroscopy
46(5)
3.4 Application of EEG and fNIRS in BCI Research
51(14)
3.4.1 Healthy and Clinical Populations
51(1)
3.4.2 Limitations and Future Direction of EEG and fNIRS in BCI Research
52(2)
References
54(11)
Chapter 4 Biopotential Acquisition for Brain-Wheelchair Interfaces
65(30)
Federico N. Guerrero
Enrique M. Spinelli
4.1 Introduction
66(1)
4.2 Biopotential Instrumentation Basics
67(8)
4.2.1 Basic Principles
67(2)
4.2.2 Analog Conditioning
69(2)
4.2.3 Dynamic Range and Measurement Paradigm
71(1)
4.2.4 Measurement Topologies
72(1)
4.2.5 Electronic Instrumentation Practical Parameters
73(2)
4.3 Signal Quality, Interference, and Artifacts
75(5)
4.3.1 Magnetically Coupled EMI
76(1)
4.3.2 Capacitively Coupled EMI
76(3)
4.3.3 Artifacts
79(1)
4.4 BWI Instrumentation
80(2)
4.4.1 Summary of Signals Used for BWIs
80(1)
4.4.2 EEG Electrodes and Electrode Support
81(1)
4.4.3 Synchronous Acquisition
81(1)
4.5 Advanced Instrumentation
82(3)
4.5.1 Active Electrodes
82(1)
4.5.2 Body Potential Driving
82(3)
4.6 Acquisition Systems for BWI Application
85(5)
4.6.1 Specifications and Commercial Equipment
85(1)
4.6.2 Electrical Safety
86(1)
4.6.3 Practical Design: Medium Complexity Multichannel System Based on ADS 1299
86(4)
4.7 Conclusions
90(5)
References
91(4)
Chapter 5 EEG Signal Processing in Brain--Computer Interface
95(16)
M. Agustina Garces
Lorena L. Orosco
5.1 Preprocessing
97(1)
5.2 Feature Extraction
98(3)
5.2.1 Time-Domain Analysis
98(1)
5.2.2 Spectral Analysis
98(1)
5.2.3 Time-Frequency Analysis: Wavelet Transform
99(1)
5.2.4 Chaos and Dynamic Analysis: Entropy
100(1)
5.2.5 Principal Components Analysis and Independent Components Analysis
101(1)
5.3 Feature Selection: Dimensionality Reduction
101(2)
5.3.1 Genetic Algorithm
102(1)
5.3.2 Distinctive Sensitive Learning Vector Quantization
103(1)
5.4 Classifiers
103(2)
5.4.1 Support Vector Machines
104(1)
5.4.2 Linear Discriminant Analysis
104(1)
5.4.3 Neural Networks
105(1)
5.5 Performance Evaluation of the Signal Processing Stage
105(1)
5.6 Summary and Conclusions
106(5)
References
107(4)
Chapter 6 High-Speed Steady-State Visual Evoked Potential-Based Brain--Computer Interfaces
111
Xiaogang Chen
Yijun Wang
Xiaorong Gao
6.1 Introduction
112(4)
6.1.1 Steady-State Visual Evoked Potential-Based Brain--Computer Interface
112(1)
6.1.2 Steady-State Visual Evoked Potential Brain--Computer Interface With High Information Transfer Rates
113(1)
6.1.3 Challenges Confronting Practical System Design
114(2)
6.2 Coding and Decoding Methods of Steady-State Visual Evoked Potentials
116(6)
6.2.1 Multiple Target Coding in Steady-State Visual Evoked Potential-Based Brain--Computer Interfaces
116(3)
6.2.2 Target Identification Methods of Steady-State Visual Evoked Potentials
119(3)
6.3 Design of High-Speed Steady-State Visual Evoked Potential-Based Brain--Computer Interfaces
122(4)
6.3.1 A General System Framework
122(1)
6.3.2 Offline System Design by Simulation With a Benchmark Dataset
123(1)
6.3.3 Online Implementation
124(1)
6.3.4 Example of a High-Speed Brain--Computer Interface Speller
125(1)
6.4 Future Directions of Steady-State Visual Evoked Potential-Based Brain--Computer Interfaces
126
References
127(3)
Chapter 7 P300-Based Brain--Computer Interfaces
130(1)
Omar Pina-Ramirez
Raquel Valdes-Cristerna
Veronica Medina-Banuelos
Oscar Yanez-Suarez
7.1 Introduction
131(1)
7.2 P300 Event-Related Potential
132(8)
7.2.1 P300 Elicitation Paradigm
133(2)
7.2.2 P300 Waveform
135(2)
7.2.3 P300 ERP Complex (N1, N2, P2, P3, N4 Component Waves)
137(2)
7.2.4 Why Is P300 Used in BCIs?
139(1)
7.3 P300 for Spelling Task
140(13)
7.3.1 Classical P300 Spelling Task
140(4)
7.3.2 P300 Spelling Screen Variants
144(9)
7.4 P300 for Nonspelling Tasks
153(11)
7.4.1 Computer Interaction
154(3)
7.4.2 Behavior Interaction
157(2)
7.4.3 P300-Commanded Wheelchairs
159(5)
7.4.4 Hybrid Paradigms
164(1)
7.5 Conclusion
164(7)
References
165(6)
Chapter 8 Motor Imagery Based Brain--Computer Interfaces
171(26)
Reinhold Scherer
Carmen Vidaurre
8.1 Introduction
172(1)
8.2 Motor Imagery as Intellectual Process to Encode Messages
172(3)
8.2.1 Neurophysiological Phenomena of Motor Imagery
172(1)
8.2.2 How to Perform Motor Imagery? User Education Aspects
173(2)
8.2.3 Things to Take Into Consideration
175(1)
8.3 Signal Conditioning and Processing (Closed-Loop Brain--Computer Interface Control)
175(6)
8.3.1 Preprocessing
175(1)
8.3.2 Feature Extraction
176(1)
8.3.3 Feature Translation by Classification or Regression
177(4)
8.4 Application Programs
181(1)
8.5 How to Gain Brain--Computer Interface Control? (Brain--Computer Co-Adaptation)
182(1)
8.5.1 How Good Does Motor Imagery-Brain--Computer Interface Control Work?
183(1)
8.6 Open Issues and Current Motor Imagery-Based Brain--Computer Interface Research
183(6)
8.6.1 System Robustness and Online Co-Adaptation (Machine Learning)
184(1)
8.6.2 Adaptation of Experimental Training Paradigms (Human Factors)
185(1)
8.6.3 Improve Interpretability of Electroencephalogram Oscillations (Basic Neuroscience)
186(3)
8.6.4 Intellectual Processes for Encoding Messages
189(1)
8.7 Conclusion
189(8)
References
190(7)
Chapter 9 Electrocorticogram Based Brain--Computer Interfaces
197(32)
Christoph Guger
Christoph Kapeller
Hiroshi Ogawa
Robert Pruckl
Johannes Grunwald
Kyousuke Kamada
9.1 Introduction
197(3)
9.2 High-Gamma Mapping
200(11)
9.2.1 Identification of the Motor Cortex
206(2)
9.2.2 Localization of the Fusiform Face Area
208(2)
9.2.3 Functional Mapping With Corticocortical Evoked Potentials
210(1)
9.3 Real-Time Brain--Computer Interfaces Control
211(9)
9.3.1 Teleoperation of a Humanoid Robot Through a Motor Imagery Task
211(3)
9.3.2 Toward Real-Time Hand Prosthesis Control
214(2)
9.3.3 P300
216(2)
9.3.4 Code-Based or c-VEP for Brain--Computer Interface
218(1)
9.3.5 Real-Time Decoding of Recognized Faces
219(1)
9.4 Discussion
220(9)
References
221(8)
Chapter 10 Hybrid Brain--Computer Interfaces for Wheelchair Control: A Review of Existing Solutions, Their Advantages and Open Challenges
229(28)
Lucas R. Trambaiolli
Tiago H. Falk
10.1 Concepts of Hybrid Brain--Computer Interfaces
230(6)
10.2 Applied Hybrid BCIs
236(5)
10.3 Is More Always the Merrier?
241(4)
10.4 Existing and Emerging Technologies
245(2)
10.5 Final Considerations
247(10)
References
248(9)
Chapter 11 Wheelchairs: History, Characteristics, and Technical Specifications
257(34)
Silvia E. Rodrigo
Carina V. Herrera
11.1 A Brief History of the Wheelchair
257(2)
11.2 Technological Characteristics of Wheelchairs
259(11)
11.2.1 Supporting Structure
260(5)
11.2.2 Manual Propelling Structure
265(2)
11.2.3 Powered Propelling Structure
267(3)
11.3 Issues Related to the Appropriate Selection of a Wheelchair
270(9)
11.3.1 Posture in the Wheelchair
271(4)
11.3.2 Adaptation to the User
275(4)
11.4 Wheelchair Normative
279(12)
11.4.1 Vocabulary
280(2)
11.4.2 Specifications Related to Dimensions, Mass, and Maneuvering Space of Wheelchairs
282(2)
11.4.3 Safety Specifications
284(2)
11.4.4 Performance Specifications
286(3)
References
289(2)
Chapter 12 Smart-Wheelchairs
291(32)
Christian Mandel
Tim Laue
Serge Autexier
12.1 Introduction
291(2)
12.2 Fields of Technological Development
293(16)
12.2.1 Indoor and Outdoor Localization Techniques
293(3)
12.2.2 (Semi-)Autonomous Navigation
296(3)
12.2.3 Driving Assistance Systems
299(4)
12.2.4 User Interfaces for Special Needs
303(6)
12.3 Smart Support: Compensation, Rehabilitation, Assessment, and Training
309(5)
12.3.1 Application Areas
310(4)
12.4 Future Perspectives
314(9)
12.4.1 Improved Wheelchair Designs
314(1)
12.4.2 Progression of Sensorial Equipment
315(1)
12.4.3 New Algorithms for State Estimation, Navigation, and Control
315(1)
12.4.4 New Application Areas
316(1)
References
317(6)
Chapter 13 Brain--Computer Interfaces for Controlling Wheelchairs
323(22)
Alvaro Fernandez-Rodriguez
Francisco Velasco-Alvarez
Ricardo Ron-Angevin
13.1 Introduction
323(3)
13.1.1 Relevant Factors to be Considered
325(1)
13.2 Signal
326(3)
13.2.1 ERD/ERS
326(1)
13.2.2 P300
327(1)
13.2.3 SSVEP
328(1)
13.2.4 Hybrid-Mental
328(1)
13.2.5 Muscle-Assisted
329(1)
13.3 Feature Extraction and Classification Methods
329(1)
13.4 Navigation
330(2)
13.5 User's Task and Interface
332(5)
13.5.1 ERD/ERS
332(1)
13.5.2 P300
333(1)
13.5.3 SSVEP
334(1)
13.5.4 Hybrid-Mental
335(1)
13.5.5 Muscle-Assisted
335(2)
13.6 Evaluation
337(1)
13.7 Conclusions
338(7)
References
340(5)
Chapter 14 Control Strategies of a Brain-Controlled Wheelchair Using Two Mental Tasks
345(24)
Francisco Velasco-Alvarez
Alvaro Fernandez-Rodriguez
Antonio Diaz-Estrella
Maria J. Blanca-Mena
Ricardo Ron-Angevin
14.1 Introduction
346(3)
14.1.1 Electroencephalographic Signals and Choice of an Endogenous System Based on Sensorimotor Rhythm
346(1)
14.1.2 Influence of the Number of Mental Tasks on Performance
347(1)
14.1.3 Application of Virtual Reality in Training
347(2)
14.2 University of Malaga-Brain--Computer Interface Proposal
349(5)
14.2.1 Brain--Computer Interface Training Based on the Graz Paradigm
349(1)
14.2.2 Signal Acquisition and Processing
350(1)
14.2.3 Mapping Two Mental Tasks into More Commands
351(2)
14.2.4 Comparison With Similar Paradigms
353(1)
14.3 Real Brain-Controlled Wheelchair
354(1)
14.3.1 Prototype Implementation
354(1)
14.4 Paradigm Variations
354(10)
14.4.1 Testing the Control Paradigm in Different Environments
356(1)
14.4.2 Alternative Ways of Controlling the Brain-Controlled Wheelchair
356(1)
14.4.3 Navigation Strategies
357(7)
14.5 Conclusions
364(5)
References
366(3)
Chapter 15 Towards a System to Command a Robotic Wheelchair Based on Independent SSVEP-BCI
369(12)
Teodiano Bastos-Filho
Alan Floriano
Eduardo Couto
Richard J. M. Godinez-Tello
15.1 Introduction
369(2)
15.2 Materials and Methods
371(4)
15.2.1 Subjects
371(1)
15.2.2 EEG Acquisition
371(1)
15.2.3 Stimulus Design
372(1)
15.2.4 Protocol
372(1)
15.2.5 EEG Signal Processing
373(2)
15.3 Results and Discussions
375(2)
15.4 Conclusions and Future Work
377(4)
References
378(3)
Chapter 16 EOG-Based Wheelchair Control
381(24)
Rafael Barea Navarro
Luciano Boquete Vazquez
Elena Lopez Guillen
16.1 Electrooculography
381(1)
16.2 EOG Signal Acquisition System
382(1)
16.3 Eye Model Based on EOG
383(3)
16.4 EOG-based Smart-Wheelchairs Review
386(5)
16.4.1 Wheelesley Project
387(1)
16.4.2 SIAMO Project
388(1)
16.4.3 Intelligent RoboChair Project
388(1)
16.4.4 Doshisha Wheelchair
389(1)
16.4.5 EOG Chair Project
389(2)
16.4.6 Other EOG-Based Wheelchairs
391(1)
16.5 Wheelchair Guidance Strategies Using EOG
391(8)
16.5.1 Direct Access
392(1)
16.5.2 Scanning Access
393(1)
16.5.3 Guidance by Eye Commands
394(5)
16.6 Conclusions
399(6)
References
401(4)
Chapter 17 Voice-Directed Autonomous Navigation of a Smart-Wheelchair
405(20)
Ling Chen
Sen Wang
Huosheng Hu
Dongbing Gu
Ian Dukes
17.1 Introduction
406(1)
17.2 Related Research
406(3)
17.2.1 Joystick-Based Navigation
407(1)
17.2.2 Touch Screen- Or Touchpad-Based Navigation
407(1)
17.2.3 Voice-Based Navigation
407(1)
17.2.4 Vision-Based Navigation
407(1)
17.2.5 Accelerometer-Based Navigation
408(1)
17.2.6 Noninvasive Brain--Computer Interface-Based Navigation
408(1)
17.2.7 Shared Control-Based Navigation
409(1)
17.3 Hardware Configuration
409(3)
17.3.1 Off-the-Shelf Wheelchair
409(1)
17.3.2 Sensors Involved
409(3)
17.3.3 Embedded Computer
412(1)
17.3.4 Summary
412(1)
17.4 Software Deployment
412(6)
17.4.1 Key Algorithms Involved
412(3)
17.4.2 Other Miscellaneous Software Modules
415(2)
17.4.3 Summary
417(1)
17.5 Experiments
418(4)
17.5.1 Experimental Setup
418(1)
17.5.2 Results and Analysis
418(4)
17.6 Conclusion
422(3)
References
422(3)
Chapter 18 Brain--Computer Interfaces for Neurorehabilitation: Enhancing Functional Electrical Stimulation
425(28)
Cesar Marquez-Chin
Isabel Bolivar-Telleria
Milos R. Popovic
18.1 Introduction
426(2)
18.1.1 Stroke
426(1)
18.1.2 Spinal Cord Injury
426(1)
18.1.3 Rehabilitation of Voluntary Function After Stroke and Spinal Cord Injury
427(1)
18.2 Functional Electrical Stimulation as a Therapeutic Intervention
428(8)
18.2.1 Functional Electrical Stimulation Therapy for Stroke Rehabilitation
428(2)
18.2.2 Functional Electrical Stimulation Therapy for Spinal Cord Injury Rehabilitation
430(2)
18.2.3 The Authors' Contributions to Functional Electrical Stimulation Therapy
432(2)
18.2.4 Mechanisms of Recovery of Voluntary Function Using Functional Electrical Stimulation Therapy
434(1)
18.2.5 The Challenge for Functional Electrical Stimulation Therapy in Neurorehabilitation
434(2)
18.3 Brain--Computer Interfaces
436(2)
18.4 Brain--Computer Interfaces for Rehabilitation
438(6)
18.4.1 The Authors' Contributions to the Use of Brain--Computer Interfaces + Functional Electrical Stimulation Therapy for Neurorehabilitaiton
439(5)
18.5 Final Remarks
444(9)
References
445(8)
Index 453
In 2007, Dr. Diez began his Doctorate in the Institute of Automatics (INAUT) at the National University of San Juan (UNSJ) from Argentina. In 2012, he obtained a Postdoctoral Fellowship on the Division of Medical Technology (GATEME), at UNSJ from Argentina. The National Council for Scientific Research and Technologies (CONICET) from Argentina funded the research work of Dr. Diez from 2007. Currently, he is Investigator of CONICET.

Dr. Diez has published his research work in many journals and conferences and he has won different awards, among them, The Sarmiento Prize” for the best doctoral thesis; an award from the Secretary of Sciences, Technologies and Innovation of San Juan State Government. He is a reviewer for funding programs, a member of scientific committees and is a reviewer for conferences and journals. He currently works in the development of assistive technologies for people with disabilities.
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