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Brain-computer interfaces. 1, Foundations and methods / edited by Maureen Clerc, Laurent Bougrain, Fabien Lotte. -- London : ISTE Ltd ; Hoboken, NJ : John Wiley & Sons, Inc, 2016. – (61.59 /B814 /v.1) |
Contents
Foreword
Introduction xv
Part 1.
Anatomy and Physiology 1
Chapter
1. Anatomy of the Nervous System 3
1.1.
General description of the nervous system
1.2. The
central nervous system
1.3. The
cerebellum
1.4. The
spinal cord and its roots
1.5. The
peripheral nervous system
1.6.
Some syndromes and pathologies targeted by Brain-Computer Interfaces
1.7.
Conclusions
1.8.
Bibliography
Chapter
2. Functional Neuroimaging 25
2.1.
Functional MRI 26
2.2.
Electrophysiology: EEG and MEG 31
2.3.
Simultaneous EEG-fMRI 37
2.4.
Discussion and outlook for the future 38
2.5.
Bibliography 40
Chapter
3. Cerebral Electrogenesis 45
3.1.
Electrical neuronal activity detected in EEG
3.2.
Dipolar and quadrupole fields
3.3. The
importance of geometry
3.4. The
influence of conductive media
3.5.
Conclusions
3.6.
Bibliography
Chapter
4. Physiological Markers for Controlling Active and Reactive BCIs
4.1.
Introduction
4.2.
Markers that enable active interface control
4.3.
Markers that make it possible to control reactive interfaces
4.4.
Conclusions
4.5.
Bibliography
Chapter
5. Neurophysiological Markers for Passive Brain-Computer Interfaces 85
5.1.
Passive BCI and mental states 85
5.2.
Cognitive load 87
5.3.
Mental fatigue and vigilance 89
5.4.
Attention
5.5.
Error detection
5.6.
Emotions
5.7.
Conclusions
5.8. Bibliography
Part 2.
Signal Processing and Machine Learning
101
Chapter
6. Electroencephalography Data Preprocessing
6.1.
Introduction
6.2.
Principles of EEG acquisition
6.3.
Temporal representation and segmentation . .
6.4.
Frequency representation
6.5. Time-frequency
representations
6.6.
Spatial representations 115
6.7.
Statistical representations 121
6.8.
Conclusions
6.9.
Bibliography
Chapter
7. EEG Feature Extraction 127
7.1.
Introduction 127
7.2.
Feature extraction 127
7.3.
Feature extraction for BCIs employing oscillatory activity . . . 130
7.4.
Feature extraction for the BCIs employing EPs
137
7.5.
Alternative methods and the Riemannian geometry approach 139
7.6.
Conclusions 141
7.7.
Bibliography 142
Chapter
8. Analysis of Extracellular Recordings
145
8.1.
Introduction
8.2. The
origin of the signal and its consequences
148
8.3.
Spike sorting: a chronological presentation
8.4.
Recommendations
8.5.
Bibliography
Chapter
9. Statistical Learning for BCIs 185
9.1.
Supervised statistical learning 185
9.2.
Specific training methods 192
9.3.
Performance metrics 194
9.4.
Validation and model selection 197
9.5.
Conclusions 202
9.6.
Bibliography 202
Part 3.
Human Learning and Human-Machine Interaction
207
Chapter
10. Adaptive Methods in Machine Learning
209
10.1.
The primary sources of variability
10.2.
Adaptation framework for BCIs
10.3.
Adaptive statistical decoding
10.4.
Generative model and adaptation
10.5.
Conclusions
10.6.
Bibliography
Chapter
11. Human Learning for Brain-Computer Interfaces 233
11.1.
Introduction
11.2.
Illustration: two historical BCI protocols
11.3.
Limitations of standard protocols used for BCIs
11.4.
State-of-the-art in BCI learning protocols
11.5.
Perspectives: toward user-adapted and user-adaptable learning protocols
11.6.
Conclusions
11.7.
Bibliography
Chapter
12. Brain-Computer Interfaces for Human-Computer Interaction 251
12.1. A
brief introduction to human-computer interaction 251
12.2.
Properties of BCIs from the perspective of HCI
255
12.3.
Which pattern for which task? 257
12.4.
Paradigms of interaction for BCIs 259
12.5.
Conclusions 265
12.6.
Bibliography 266
Chapter
13. Brain Training with Neurofeedback
271
13.1.
Introduction
13.2.
How does it work?
13.3.
Fifty years of history
13.4.
Where NF meets BCI
13.5.
Applications
13.6.
Conclusions
13.7.
Bibliography
List of
Authors 293
Index 295
Contents
of Volume 2 299
