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Brain-Computer Interfaces: Lab Experiments to Real-World Applications, Volume 228 [Kõva köide]

Volume editor (School of Computing and Intelligent Systems, Ulster University, Derry, UK)
  • Formaat: Hardback, 434 pages, kõrgus x laius: 235x191 mm, kaal: 760 g
  • Sari: Progress in Brain Research
  • Ilmumisaeg: 31-Aug-2016
  • Kirjastus: Academic Press Inc
  • ISBN-10: 0128042168
  • ISBN-13: 9780128042168
Teised raamatud teemal:
Brain-Computer Interfaces: Lab Experiments to Real-World Applications, Volume 228Suurem pilt
  • Formaat: Hardback, 434 pages, kõrgus x laius: 235x191 mm, kaal: 760 g
  • Sari: Progress in Brain Research
  • Ilmumisaeg: 31-Aug-2016
  • Kirjastus: Academic Press Inc
  • ISBN-10: 0128042168
  • ISBN-13: 9780128042168
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780128042168.html
Märksõnad:
 Brain-Computer Interfaces: Lab Experiments to Real-World Applications is the latest volume in the Progress in Brain Researchseries that 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
  • Enhances the literature of neuroscience by further expanding the established, ongoing international series
  • Examines major areas of basic and clinical research within the field

Muu info

This new volume in the Progress in Brain Research series provides a comprehensive review of the most recent progress in brain-computer interfaces
Contributors v
Preface xvii
PART I USER TRAINING
Chapter 1 Advances in User-Training for Mental-Imagery-Based BCI Control: Psychological and Cognitive Factors and their Neural Correlates
3(36)
C. Jeunet
B. N'Kaoua
F. Lotte
1 Introduction
4(2)
2 Psychological and Cognitive Factors Related to MI-BCI Performance
6(5)
2.1 Emotional and Cognitive States That Impact MI-BCI Performance
6(1)
2.2 Personality and Cognitive Traits That Influence MI-BCI Performance
7(1)
2.3 Other Factors Impacting MI-BCI Performance: Demographic Characteristics, Experience, and Environment
8(1)
2.4 To Summarize: MI-BCI Performance Is Affected by the Users' (1) Relationship with Technology, (2) Attention, and (3) Spatial Abilities
8(3)
3 The User-Technology Relationship: Introducing the Concepts of Computer Anxiety and Sense of Agency--Definition and Neural Correlates
11(4)
3.1 Apprehension of Technology: The Concept of CA---Definition
12(1)
3.2 "I did That!": The Concept of Sense of Agency---Definition
13(1)
3.3 "I did That!": The Concept of Sense of Agency---Neural Correlates
14(1)
4 Attention---Definition and Neural Correlates
15(3)
4.1 Attention---Definition
16(1)
4.2 Attention---Neural Correlates
17(1)
5 Spatial Abilities---Definition and Neural Correlates
18(4)
5.1 Spatial Abilities---Definition
19(1)
5.2 Spatial Abilities---Neural Correlates
20(2)
6 Perspectives: The User-Technology Relationship, Attention, and Spatial Abilities as Three Levers to Improve MI-BCI User-Training
22(5)
6.1 Demonstrating the Impact of the Protocol on CA and Sense of Agency
22(2)
6.2 Raising and Improving Attention
24(2)
6.3 Increasing Spatial Abilities
26(1)
7 Conclusion
27(12)
References
28(11)
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
39(32)
G.R. Muller-Putz
A. Schwarz
J. Pereira
P. Ofner
1 Overview
40(1)
2 Methods
40(24)
2.1 Classic Motor Imagination
40(9)
2.2 Decoding Motor Execution
49(5)
2.3 Decoding Motor Imagination
54(3)
2.4 Decoding Movement Targets
57(2)
2.5 Decoding Movement Goals
59(5)
3 Conclusion
64(7)
Acknowledgment
65(1)
References
65(6)
Chapter 3 3D Hand Motion Trajectory Prediction from EEG Mu and Beta Bandpower
71(36)
A. Korik
R. Sosnik
N. Siddique
D. Coyle
1 Introduction
72(3)
2 Methods
75(13)
2.1 Experimental Paradigm
75(3)
2.2 Data Acquisition
78(1)
2.3 Data Preprocessing
79(3)
2.4 Kinematic Data Reconstruction
82(2)
2.5 Architecture Optimization, Training, Test, and Cross-Validation
84(4)
3 Results
88(5)
3.1 The Optimal Time Lag and Embedding Dimension
88(1)
3.2 The Optimal Channel Sets
88(3)
3.3 Accuracy of Trajectory Reconstruction
91(2)
4 Discussion
93(8)
4.1 Prominent Cortical Areas
96(2)
4.2 Prominent Bands/Results of the PTS and the BTS Model
98(1)
4.3 Inner-Outer (Nested) Cross-Validation for MTP BCIs
98(1)
4.4 Target Shuffling Test for Final Result Validation
98(1)
4.5 Limitations and Future Work
99(2)
5 Conclusion
101(6)
References
101(6)
Chapter 4 Multisession, Noninvasive Closed-Loop Neuroprosthetic Control of Grasping by Upper Limb Amputees
107(24)
H.A. Agashe
A.Y. Paek
J.L. Contreras-Vidal
1 Introduction
108(2)
2 Materials and Methods
110(7)
2.1 Study Participants
110(1)
2.2 Data Acquisition and Instrumentation/Hardware
111(1)
2.3 Experiment Design
112(4)
2.4 Signal Processing
116(1)
3 Results
117(4)
3.1 Closed-Loop Grasping Performance Was Stable over Sessions
117(1)
3.2 Long-Term Stability of EEG Signal Features and Decoders
118(3)
4 Discussion
121(10)
4.1 Multisession, Closed-Loop BMI Performance
121(1)
4.2 Closed-Loop BMI and Multisession EEG Stability
122(1)
4.3 Implications for Noninvasive BMIs
123(1)
Acknowledgments
124(1)
References
124(7)
PART III PATIENTS STUDIES AND CLINICAL APPLICATIONS
Chapter 5 Brain--Computer Interfaces in the Completely Locked-in State and Chronic Stroke
131(32)
U. Chaudhary
N. Birbaumer
A. Ramos-Murguialday
1 Historical Perspective
132(4)
1.1 Initial Setback
133(1)
1.2 Early Successes
134(2)
2 Types of BCI
136(2)
3 BCI for Communication in Paralysis due to ALS
138(9)
3.1 Invasive BCI for Communication
139(1)
3.2 Noninvasive BCIs for Communication
140(3)
3.3 Learning BCI Control in Paralysis
143(3)
3.4 Functional Near-Infrared Spectroscopy-Based BCI for Communication in CLIS
146(1)
4 BCIs for Chronic Stroke
147(6)
4.1 Stroke Rehabilitation Strategies
148(1)
4.2 Stroke BCIs Studies
149(2)
4.3 Taking Advantage of Brain Stimulation
151(2)
5 Future Perspective
153(10)
Acknowledgments
153(1)
References
154(9)
Chapter 6 Brain-Machine Interfaces for Rehabilitation of Poststroke Hemiplegia
163(22)
J. Ushiba
S.R. Soekadar
1 Introduction
163(1)
2 Signal Modality of BMI
164(3)
3 Identification of Biomarkers for BMI Motor Rehabilitation
167(1)
4 BMI Motor Rehabilitation and Its Outcome
168(1)
5 Possible Mechanisms Underlying BMI Motor Rehabilitation Training-Related Functional Recovery
169(3)
5.1 Use-Dependent Plasticity
171(1)
5.2 Hebbian (Timing Dependent) Plasticity
171(1)
5.3 Reward-Based Reinforcement Learning
171(1)
5.4 Error-Based Learning
172(1)
6 Clinical Positioning
172(1)
7 Future of BMI Motor Rehabilitation
173(2)
8 Unsolved Issues and Questions
175(1)
9 Concluding Remarks
176(9)
Acknowledgments
177(1)
References
177(8)
Chapter 7 Neural and Cortical Analysis of Swallowing and Detection of Motor Imagery of Swallow for Dysphagia Rehabilitation---A Review
185(36)
H. Yang
K.K. Ang
C. Wang
K.S. Phua
C. Guan
1 Background
186(1)
1.1 Introduction
186(2)
1.2 Swallowing Process and Assessment
186(1)
1.3 Objectives and Motivation
187(1)
2 Neural and Cortical Analysis of Swallowing
188(10)
2.1 Cortical Network of Swallowing
188(4)
2.2 Brain Activations of Swallowing and Tongue
192(5)
2.3 Cortical Activity of Swallowing for Patients
197(1)
3 Detection of MI-SW
198(11)
3.1 Overview of Detection of MI-SW
198(4)
3.2 Feature Extraction and Model Adaptation
202(2)
3.3 Neural Cortical Correlates of MI-SW with ME-SW
204(3)
3.4 Implications for Clinical Use
207(2)
4 Detection of MI-TM
209(5)
4.1 ICA and FBCSP-Based Detection
209(3)
4.2 Predictive-Spectral-Spatial Preprocessing for a Multiclass BCI, Prediction BCI, and Short-Time Fourier Transform-Based Detection
212(2)
5 Implications and Future Directions
214(7)
5.1 Future Directions for Neural Analysis of Swallowing
214(1)
5.2 Future Directions on the Rehabilitation of Stroke Dysphagia Patients
215(1)
References
216(5)
Chapter 8 A Cognitive Brain-Computer Interface for Patients with Amyotrophic Lateral Sclerosis
221(20)
M.R. Hohmann
T. Fomina
V. Jayaram
N. Widmann
C. Forster
J. Just
M. Synofzik
B. Scholkopf
L. Schols
M. Grosse-Wentrup
1 Introduction
222(2)
1.1 Amyotrophic Lateral Sclerosis
222(1)
1.2 Brain-Computer Interfaces
222(1)
1.3 The Current Work
223(1)
2 Methods
224(5)
2.1 Experimental Paradigm
224(1)
2.2 Experimental Data
225(1)
2.3 EEG Analysis
226(3)
3 Results
229(4)
4 Discussion
233(8)
References
236(5)
Chapter 9 Brain-Computer Interfaces for Patients with Disorders of Consciousness
241(54)
R.M. Gibson
A.M. Owen
D. Cruse
1 The Disorders of Consciousness
241(2)
2 The Challenges of Communicating with a Damaged Brain
243(2)
3 BCIs for Patients with DoC
245(27)
3.1 Electroencephalography
245(26)
3.2 Single- or Multiunit Neuronal Activity
271(1)
3.3 BOLD Response
271(1)
4 Summary and Recommendations
272(23)
References
274(21)
PART IV NON-MEDICAL APPLICATIONS
Chapter 10 A Passive Brain---Computer Interface Application for the Mental Workload Assessment on Professional Air Traffic Controllers During Realistic Air Traffic Control Tasks
295(34)
P. Arico
G. Borghini
G. Di Flumeri
A. Colosimo
S. Pozzi
F. Babiloni
1 Introduction
296(7)
1.1 Passive Brain--Computer Interface
297(1)
1.2 Mental Workload: The Mean and Its Neurophysiological Measurements
298(2)
1.3 An Example of Mental Workload Measure in Realistic Settings: The Air Traffic Management Case
300(3)
1.4 Present Study
303(1)
2 Materials and Methods
303(10)
2.1 Experimental Protocol
303(4)
2.2 Neurophysiological Data Analysis
307(3)
2.3 Performed Data Analyses
310(3)
3 Results
313(4)
3.1 Overtime Stability of the EEG-Based Workload Measure
313(4)
4 Discussion
317(3)
5 Conclusion
320(9)
Acknowledgments
321(1)
References
321(8)
Chapter 11 3D Graphics, Virtual Reality, and Motion-Onset Visual Evoked Potentials in Neurogaming
329(28)
R. Beveridge
S. Wilson
D. Coyle
1 Introduction
330(4)
2 Methodology
334(8)
2.1 Data Acquisition
334(2)
2.2 Study 1---Graphical Complexity (Basic Games)
336(2)
2.3 Study 2---Graphical Complexity (Commercial Games)
338(3)
2.4 Study 3---OCR vs LCD Screen
341(1)
3 Data Analysis
342(2)
3.1 Data Preprocessing
342(1)
3.2 mVEP Classification---Training Data
343(1)
3.3 mVEP Classification---Testing Data
344(1)
4 Results
344(4)
4.1 Study 1---Comparing Graphical Complexity (Basic Games)
344(1)
4.2 Study 2---Comparing Graphical Complexity (Commercial Games)
345(1)
4.3 Study 3---OCR vs LCD Screen
346(1)
4.4 Studies 1-3---Best Channels
347(1)
5 Discussion
348(2)
6 Conclusion
350(7)
References
350(7)
PART V BCI IN PRACTICE AND USABILITY CONSIDERATIONS
Chapter 12 Interfacing Brain with Computer to Improve Communication and Rehabilitation After Brain Damage
357(32)
A. Riccio
F. Pichiorri
F. Schettini
J. Toppi
M. Risetti
R. Formisano
M. Molinari
L. Astolfi
F. Cincotti
D. Mattia
1 Introduction
358(1)
2 Multidisciplinary Approach to BCI Design
359(2)
2.1 BCI Users in Clinical Contexts
359(1)
2.2 User Needs and Usability Evaluation
360(1)
3 Replacing Communication and Control
361(7)
3.1 BCIs for Communication in End-Users
363(5)
4 Improving Motor and Cognitive Function
368(6)
4.1 Motor Rehabilitation
369(2)
4.2 Cognitive Rehabilitation
371(1)
4.3 Harnessing Brain Reorganization via BCI
372(2)
5 Conclusion and Future Perspectives
374(15)
Acknowledgments
375(1)
References
375(14)
Chapter 13 BCI in Practice
389(16)
D.J. McFarland
T.M. Vaughan
1 Overview of Common BCI Systems
390(3)
2 Some Issues in Applications for End Users
393(3)
3 Studies with End Users
396(9)
Acknowledgments
398(1)
References
398(7)
Index 405(8)
Other volumes in PROGRESS IN BRAIN RESEARCH 413
Damien Coyle is a Professor of Neurotechnology at the School of Computing and Intelligent Systems, Ulster University. He is developing neurotechnology to enable movement-free communication for the physically impaired. These developments are underpinned by research in computational intelligence, bio-signal processing, computational neuroscience, neuroimaging and brain-computer interface (BCI) applications.Damien is a Fellow of the UK Higher Education Academy, and teaches at both undergraduate and postgraduate level in computer science and engineering and supervises a number of PhD students and researchers. Damien studied at Ulster University, graduating as a Bachelor of Engineering in Electronics and Computing with first class honours in 2002 and with a PhD in intelligent signal processing for BCI applications in 2006. He joined the School as Lecturer (2006-2012), becoming Senior Lecturer (2012-2013), Reader (2013-2014) and Professor (2015). Previously an Ulster University Distinguished Research Fellow, Damien has won international research awards including the 2008 IEEE Computational Intelligence Society (CIS) Outstanding Doctoral Dissertation Award and the 2011 International Neural Network Society (INNS) Young Investigator of the Year Award. His research was shortlisted for the 2015 Annual International BCI Research Award. He was a Royal Academy of Engineering/The Leverhulme Trust Senior Research Fellow in 2013 and is currently a Royal Academy of Engineering Enterprise Fellow 2016. Damien is a founding member of the International BCI Society, a Senior member of the IEEE and chairs the IEEE Computational Intelligence Society (CIS) (UKRI chapter) which was awarded best and outstanding chapter awards from the IEEE region 8 and the IEEE CIS in 2012 and 2013, respectively.