Brain-computer interfaces (BCI) are devices which measure brain activity and translate it into messages or commands, thereby opening up many investigation and application possibilities. This book provides keys for understanding and designing these multi-disciplinary interfaces, which require many fields of expertise such as neuroscience, statistics, informatics and psychology.
This first volume, Methods and Perspectives, presents all the basic knowledge underlying the working principles of BCI. It opens with the anatomical and physiological organization of the brain, followed by the brain activity involved in BCI, and following with information extraction, which involves signal processing and machine learning methods. BCI usage is then described, from the angle of human learning and human-machine interfaces.
The basic notions developed in this reference book are intended to be accessible to all readers interested in BCI, whatever their background. More advanced material is also offered, for readers who want to expand their knowledge in disciplinary fields underlying BCI.
This first volume will be followed by a second volume, entitled Technology and Applications
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ISBN-13
978-1-119-14498-4 (9781119144984)
Schweitzer Klassifikation
1 - Cover [Seite 1]
2 - Title Page [Seite 5]
3 - Copyright [Seite 6]
4 - Contents [Seite 7]
5 - Foreword [Seite 15]
6 - Introduction [Seite 17]
6.1 - I.1. History [Seite 17]
6.2 - I.2. Introduction to BCIs [Seite 18]
6.2.1 - I.2.1. Classification of BCIs [Seite 20]
6.2.2 - I.2.2. BCI applications [Seite 21]
6.2.3 - I.2.3. Other BCI systems [Seite 21]
6.2.4 - I.2.4. Terminology [Seite 22]
6.3 - I.3. Book presentation [Seite 22]
6.3.1 - I.3.1. Foundations and methods [Seite 22]
6.3.2 - I.3.2. Reading guide [Seite 23]
6.4 - I.4. Acknowledgments [Seite 24]
6.5 - I.5. Bibliography [Seite 25]
7 - PART 1: Anatomy and Physiology [Seite 27]
7.1 - 1: Anatomy of the Nervous System [Seite 29]
7.1.1 - 1.1. General description of the nervous system [Seite 30]
7.1.2 - 1.2. The central nervous system [Seite 31]
7.1.2.1 - 1.2.1. The telencephalon [Seite 32]
7.1.2.2 - 1.2.2. The diencephalon [Seite 36]
7.1.2.3 - 1.2.3. The brain stem [Seite 38]
7.1.3 - 1.3. The cerebellum [Seite 40]
7.1.4 - 1.4. The spinal cord and its roots [Seite 41]
7.1.5 - 1.5. The peripheral nervous system [Seite 44]
7.1.5.1 - 1.5.1. Nerves [Seite 44]
7.1.5.2 - 1.5.2. General organization of the PNS [Seite 45]
7.1.5.3 - 1.5.3. The autonomic nervous system [Seite 46]
7.1.6 - 1.6. Some syndromes and pathologies targeted by Brain-Computer Interfaces [Seite 47]
7.1.6.1 - 1.6.1. Motor syndromes [Seite 47]
7.1.6.2 - 1.6.2. Some pathologies that may be treated with BCIs [Seite 48]
7.1.7 - 1.7. Conclusions [Seite 49]
7.1.8 - 1.8. Bibliography [Seite 50]
7.2 - 2: Functional Neuroimaging [Seite 51]
7.2.1 - 2.1. Functional MRI [Seite 52]
7.2.1.1 - 2.1.1. Basic principles of MRI [Seite 52]
7.2.1.2 - 2.1.2. Principles of fMRI [Seite 52]
7.2.1.3 - 2.1.3. Statistical data analysis: the linear model [Seite 53]
7.2.1.4 - 2.1.4. Independent component analysis [Seite 55]
7.2.1.5 - 2.1.5. Connectivity measures [Seite 56]
7.2.2 - 2.2. Electrophysiology: EEG and MEG [Seite 57]
7.2.2.1 - 2.2.1. Basic principles of signal generation [Seite 57]
7.2.2.2 - 2.2.2. Event-related potentials and fields [Seite 57]
7.2.2.3 - 2.2.3. Source localization [Seite 58]
7.2.2.4 - 2.2.4. Independent component analysis [Seite 60]
7.2.2.5 - 2.2.5. Time-frequency analysis [Seite 60]
7.2.2.6 - 2.2.6. Connectivity [Seite 61]
7.2.2.7 - 2.2.7. Statistical analysis [Seite 62]
7.2.3 - 2.3. Simultaneous EEG-fMRI [Seite 63]
7.2.3.1 - 2.3.1. Basic principles [Seite 63]
7.2.3.2 - 2.3.2. Applications and data analysis [Seite 63]
7.2.3.3 - 2.3.3. Connections between EEG and fMRI [Seite 64]
7.2.4 - 2.4. Discussion and outlook for the future [Seite 64]
7.2.5 - 2.5. Bibliography [Seite 66]
7.3 - 3: Cerebral Electrogenesis [Seite 71]
7.3.1 - 3.1. Electrical neuronal activity detected in EEG [Seite 71]
7.3.1.1 - 3.1.1. Action and postsynaptic potentials [Seite 72]
7.3.1.2 - 3.1.2. Resting potential, electrochemical gradient and PSPs [Seite 73]
7.3.1.3 - 3.1.3. From PSPs to EEG [Seite 74]
7.3.2 - 3.2. Dipolar and quadrupole fields [Seite 77]
7.3.2.1 - 3.2.1. Field created by an ion current due to the opening of ion channels [Seite 77]
7.3.2.1.1 - 3.2.1.1. Field created by an inflow of ions during a synapse (PSP) [Seite 78]
7.3.2.1.2 - 3.2.1.2. Field created by an ion inflow at the axon (AP) [Seite 80]
7.3.2.1.3 - 3.2.1.3. Field created by other neuronal activities [Seite 81]
7.3.2.2 - 3.2.2. Factors determining the value of the potential created by an ion current [Seite 82]
7.3.3 - 3.3. The importance of geometry [Seite 83]
7.3.3.1 - 3.3.1. Spatial summation, closed fields and open fields [Seite 83]
7.3.3.2 - 3.3.2. Effect of synapse position on the polarity of EEG [Seite 86]
7.3.3.3 - 3.3.3. Effect of active areas' position [Seite 87]
7.3.4 - 3.4. The influence of conductive media [Seite 88]
7.3.4.1 - 3.4.1. Influence of glial cells [Seite 88]
7.3.4.2 - 3.4.2. Influence of skull bones [Seite 89]
7.3.5 - 3.5. Conclusions [Seite 90]
7.3.6 - 3.6. Bibliography [Seite 90]
7.4 - 4: Physiological Markers for Controlling Active and Reactive BCIs [Seite 93]
7.4.1 - 4.1. Introduction [Seite 93]
7.4.2 - 4.2. Markers that enable active interface control [Seite 98]
7.4.2.1 - 4.2.1. Spatiotemporal variations in potential [Seite 98]
7.4.2.1.1 - 4.2.1.1. Slow variations of average cortical potential [Seite 98]
7.4.2.1.2 - 4.2.1.2. BP or readiness potential [Seite 99]
7.4.2.2 - 4.2.2. Spatiotemporal wave variations [Seite 100]
7.4.3 - 4.3. Markers that make it possible to control reactive interfaces [Seite 103]
7.4.3.1 - 4.3.1. Sensory evoked potentials [Seite 103]
7.4.3.1.1 - 4.3.1.1. Visual evoked potential [Seite 103]
7.4.3.1.2 - 4.3.1.2. Other steady-state potentials: steady-state auditory evoked potential, auditory steady-state response and steady-state somatosensory evoked potential [Seite 105]
7.4.3.2 - 4.3.2. Endogenous P300 potential [Seite 106]
7.4.4 - 4.4. Conclusions [Seite 107]
7.4.5 - 4.5. Bibliography [Seite 108]
7.5 - 5: Neurophysiological Markers for Passive Brain-Computer Interfaces [Seite 111]
7.5.1 - 5.1. Passive BCI and mental states [Seite 111]
7.5.1.1 - 5.1.1. Passive BCI: definition [Seite 111]
7.5.1.2 - 5.1.2. The notion of mental states [Seite 112]
7.5.1.3 - 5.1.3. General categories of neurophysiological markers [Seite 113]
7.5.2 - 5.2. Cognitive load [Seite 113]
7.5.2.1 - 5.2.1. Definition [Seite 113]
7.5.2.2 - 5.2.2. Behavioral markers [Seite 113]
7.5.2.3 - 5.2.3. EEG markers [Seite 113]
7.5.2.4 - 5.2.4. Application example: air traffic control [Seite 114]
7.5.3 - 5.3. Mental fatigue and vigilance [Seite 115]
7.5.3.1 - 5.3.1. Definition [Seite 115]
7.5.3.2 - 5.3.2. Behavioral markers [Seite 115]
7.5.3.3 - 5.3.3. EEG markers [Seite 115]
7.5.3.4 - 5.3.4. Application example: driving [Seite 116]
7.5.4 - 5.4. Attention [Seite 116]
7.5.4.1 - 5.4.1. Definition [Seite 116]
7.5.4.2 - 5.4.2. Behavioral markers [Seite 117]
7.5.4.3 - 5.4.3. EEG markers [Seite 117]
7.5.4.4 - 5.4.4. Application example: teaching [Seite 118]
7.5.5 - 5.5. Error detection [Seite 118]
7.5.5.1 - 5.5.1. Definition [Seite 118]
7.5.5.2 - 5.5.2. Behavioral markers [Seite 118]
7.5.5.3 - 5.5.3. EEG markers [Seite 119]
7.5.5.4 - 5.5.4. Application example: tactile and robotic interfaces [Seite 119]
7.5.6 - 5.6. Emotions [Seite 120]
7.5.6.1 - 5.6.1. Definition [Seite 120]
7.5.6.2 - 5.6.2. Behavioral markers [Seite 120]
7.5.6.3 - 5.6.3. EEG markers [Seite 120]
7.5.6.4 - 5.6.4. Application example: communication and personal development [Seite 121]
7.5.7 - 5.7. Conclusions [Seite 122]
7.5.8 - 5.8. Bibliography [Seite 122]
8 - PART 2: Signal Processing and Machine Learning [Seite 127]
8.1 - 6: Electroencephalography Data Preprocessing [Seite 129]
8.1.1 - 6.1. Introduction [Seite 129]
8.1.2 - 6.2. Principles of EEG acquisition [Seite 130]
8.1.2.1 - 6.2.1. Montage [Seite 130]
8.1.2.2 - 6.2.2. Sampling and quantification [Seite 131]
8.1.3 - 6.3. Temporal representation and segmentation [Seite 131]
8.1.3.1 - 6.3.1. Segmentation [Seite 132]
8.1.3.2 - 6.3.2. Time domain preprocessing [Seite 132]
8.1.4 - 6.4. Frequency representation [Seite 133]
8.1.4.1 - 6.4.1. Fourier transform [Seite 133]
8.1.4.2 - 6.4.2. Frequency filtering [Seite 134]
8.1.5 - 6.5. Time-frequency representations [Seite 135]
8.1.5.1 - 6.5.1. Time-frequency atom [Seite 135]
8.1.5.2 - 6.5.2. Short-time Fourier transform [Seite 137]
8.1.5.3 - 6.5.3. Wavelet transform [Seite 138]
8.1.5.4 - 6.5.4. Time-frequency transforms of discrete signals [Seite 140]
8.1.5.5 - 6.5.5. Toward other redundant representations [Seite 140]
8.1.6 - 6.6. Spatial representations [Seite 141]
8.1.6.1 - 6.6.1. Topographic representations [Seite 141]
8.1.6.2 - 6.6.2. Spatial filtering [Seite 142]
8.1.6.2.1 - 6.6.2.1. Surface Laplacian [Seite 143]
8.1.6.2.2 - 6.6.2.2. Cortical current density [Seite 143]
8.1.6.3 - 6.6.3. Source reconstruction [Seite 144]
8.1.6.4 - 6.6.4. Using spatial representations in BCI [Seite 146]
8.1.7 - 6.7. Statistical representations [Seite 147]
8.1.7.1 - 6.7.1. Principal component analysis [Seite 147]
8.1.7.2 - 6.7.2. Independent component analysis [Seite 148]
8.1.7.3 - 6.7.3. Using statistical representations in BCI [Seite 148]
8.1.8 - 6.8. Conclusions [Seite 149]
8.1.9 - 6.9. Bibliography [Seite 150]
8.2 - 7: EEG Feature Extraction [Seite 153]
8.2.1 - 7.1. Introduction [Seite 153]
8.2.2 - 7.2. Feature extraction [Seite 153]
8.2.3 - 7.3. Feature extraction for BCIs employing oscillatory activity [Seite 156]
8.2.3.1 - 7.3.1. Basic design for BCI using oscillatory activity [Seite 156]
8.2.3.2 - 7.3.2. Toward more advanced, multiple electrode BCIs [Seite 157]
8.2.3.2.1 - 7.3.2.1. Spatial filtering [Seite 158]
8.2.3.3 - 7.3.3. The CSP algorithm [Seite 159]
8.2.3.4 - 7.3.4. Illustration on real data [Seite 161]
8.2.4 - 7.4. Feature extraction for the BCIs employing EPs [Seite 163]
8.2.4.1 - 7.4.1. Spatial filtering for BCIs employing EPs [Seite 164]
8.2.5 - 7.5. Alternative methods and the Riemannian geometry approach [Seite 165]
8.2.6 - 7.6. Conclusions [Seite 167]
8.2.7 - 7.7. Bibliography [Seite 168]
8.3 - 8: Analysis of Extracellular Recordings [Seite 171]
8.3.1 - 8.1. Introduction [Seite 171]
8.3.1.1 - 8.1.1. Why is recording neuronal populations desirable? [Seite 172]
8.3.1.2 - 8.1.2. How can neuronal populations be recorded? [Seite 172]
8.3.1.3 - 8.1.3. The properties of extracellular data and the necessity of spike sorting [Seite 173]
8.3.2 - 8.2. The origin of the signal and its consequences [Seite 174]
8.3.2.1 - 8.2.1. Relationship between current and potential in a homogeneous medium [Seite 174]
8.3.2.2 - 8.2.2. Relationship between the derivatives of the membrane potential and the transmembrane current [Seite 176]
8.3.2.3 - 8.2.3. "From electrodes to tetrodes" [Seite 180]
8.3.3 - 8.3. Spike sorting: a chronological presentation [Seite 181]
8.3.3.1 - 8.3.1. Naked eye sorting [Seite 181]
8.3.3.2 - 8.3.2. Window discriminator (1963) [Seite 181]
8.3.3.3 - 8.3.3. Template matching (1964) [Seite 182]
8.3.3.4 - 8.3.4. Dimension reduction and clustering (1965) [Seite 183]
8.3.3.5 - 8.3.5. Principal component analysis (1968) [Seite 184]
8.3.3.6 - 8.3.6. Resolving superposition (1972) [Seite 186]
8.3.3.7 - 8.3.7. Dynamic amplitude profiles of action potentials (1973) [Seite 187]
8.3.3.8 - 8.3.8. Optimal filters (1975) [Seite 188]
8.3.3.9 - 8.3.9. Stereotrodes and amplitude ratios (1983) [Seite 191]
8.3.3.10 - 8.3.10. Sampling jitter (1984) [Seite 194]
8.3.3.11 - 8.3.11. Graphical tools [Seite 196]
8.3.3.12 - 8.3.12. Automatic clustering [Seite 197]
8.3.4 - 8.4. Recommendations [Seite 205]
8.3.5 - 8.5. Bibliography [Seite 207]
8.4 - 9: Statistical Learning for BCIs [Seite 211]
8.4.1 - 9.1. Supervised statistical learning [Seite 211]
8.4.1.1 - 9.1.1. Training data and the predictor function [Seite 212]
8.4.1.2 - 9.1.2. Empirical risk and regularization [Seite 213]
8.4.1.3 - 9.1.3. Classical methods of classification [Seite 216]
8.4.1.3.1 - 9.1.3.1. Linear discriminant analysis [Seite 216]
8.4.1.3.2 - 9.1.3.2. Support Vector Machines (SVM) [Seite 217]
8.4.2 - 9.2. Specific training methods [Seite 218]
8.4.2.1 - 9.2.1. Selection of variables and sensors [Seite 218]
8.4.2.2 - 9.2.2. Multisubject learning, information transfer [Seite 220]
8.4.3 - 9.3. Performance metrics [Seite 220]
8.4.3.1 - 9.3.1. Classification performance metrics [Seite 221]
8.4.3.2 - 9.3.2. Regression performance metrics [Seite 222]
8.4.4 - 9.4. Validation and model selection [Seite 223]
8.4.4.1 - 9.4.1. Estimation of the performance metric [Seite 223]
8.4.4.1.1 - 9.4.1.1. Random sampling [Seite 224]
8.4.4.1.2 - 9.4.1.2. K-cross-validation and leave-one-out [Seite 225]
8.4.4.1.3 - 9.4.1.3. Bootstrapping [Seite 225]
8.4.4.2 - 9.4.2. Optimization of hyperparameters [Seite 226]
8.4.5 - 9.5. Conclusions [Seite 228]
8.4.6 - 9.6. Bibliography [Seite 228]
9 - PART 3: Human Learning and Human-Machine Interaction [Seite 233]
9.1 - 10: Adaptive Methods in Machine Learning [Seite 235]
9.1.1 - 10.1. The primary sources of variability [Seite 235]
9.1.1.1 - 10.1.1. Intrasubject variability [Seite 236]
9.1.1.2 - 10.1.2. Intersubject variability [Seite 237]
9.1.2 - 10.2. Adaptation framework for BCIs [Seite 239]
9.1.3 - 10.3. Adaptive statistical decoding [Seite 240]
9.1.3.1 - 10.3.1. Covariate shift [Seite 240]
9.1.3.2 - 10.3.2. Classifier adaptation [Seite 242]
9.1.3.2.1 - 10.3.2.1. Sliding window retraining [Seite 242]
9.1.3.2.2 - 10.3.2.2. Gradient descent [Seite 242]
9.1.3.3 - 10.3.3. Subject-adapted calibration [Seite 244]
9.1.3.3.1 - 10.3.3.1. Reinforcement learning [Seite 244]
9.1.3.4 - 10.3.4. Optimal tasks [Seite 245]
9.1.3.5 - 10.3.5. Correspondence between task and command [Seite 247]
9.1.4 - 10.4. Generative model and adaptation [Seite 247]
9.1.4.1 - 10.4.1. Bayesian approach [Seite 247]
9.1.4.2 - 10.4.2. Sequential decision [Seite 250]
9.1.4.3 - 10.4.3. Online optimization of stimulations [Seite 252]
9.1.4.3.1 - 10.4.3.1. Adaptive experiments in cognitive neuroscience [Seite 253]
9.1.5 - 10.5. Conclusions [Seite 255]
9.1.6 - 10.6. Bibliography [Seite 255]
9.2 - 11: Human Learning for Brain-Computer Interfaces [Seite 259]
9.2.1 - 11.1. Introduction [Seite 259]
9.2.2 - 11.2. Illustration: two historical BCI protocols [Seite 261]
9.2.3 - 11.3. Limitations of standard protocols used for BCIs [Seite 263]
9.2.4 - 11.4. State-of-the-art in BCI learning protocols [Seite 264]
9.2.4.1 - 11.4.1. Instructions [Seite 264]
9.2.4.2 - 11.4.2. Training tasks [Seite 265]
9.2.4.3 - 11.4.3. Feedback [Seite 265]
9.2.4.4 - 11.4.4. Learning environment [Seite 268]
9.2.4.5 - 11.4.5. In summary: guidelines for designing more effective training protocols [Seite 269]
9.2.5 - 11.5. Perspectives: toward user-adapted and user-adaptable learning protocols [Seite 270]
9.2.6 - 11.6. Conclusions [Seite 273]
9.2.7 - 11.7. Bibliography [Seite 273]
9.3 - 12: Brain-Computer Interfaces for Human-Computer Interaction [Seite 277]
9.3.1 - 12.1. A brief introduction to human-computer interaction [Seite 277]
9.3.1.1 - 12.1.1. Interactive systems, interface and interaction [Seite 278]
9.3.1.2 - 12.1.2. Elementary tasks and interaction techniques [Seite 278]
9.3.1.3 - 12.1.3. Theory of action feedback [Seite 279]
9.3.1.4 - 12.1.4. Usability [Seite 280]
9.3.2 - 12.2. Properties of BCIs from the perspective of HCI [Seite 281]
9.3.3 - 12.3. Which pattern for which task? [Seite 283]
9.3.4 - 12.4. Paradigms of interaction for BCIs [Seite 285]
9.3.4.1 - 12.4.1. BCI interaction loop [Seite 285]
9.3.4.2 - 12.4.2. Main paradigms of interaction for BCIs [Seite 286]
9.3.5 - 12.5. Conclusions [Seite 291]
9.3.6 - 12.6. Bibliography [Seite 292]
9.4 - 13: Brain Training with Neurofeedback [Seite 297]
9.4.1 - 13.1. Introduction [Seite 297]
9.4.2 - 13.2. How does it work? [Seite 300]
9.4.2.1 - 13.2.1. Design of an NF training program [Seite 300]
9.4.2.2 - 13.2.2. Course of an NF session: where the eyes "look" at the brain [Seite 301]
9.4.2.3 - 13.2.3. A learning procedure that we still do not fully understand [Seite 302]
9.4.3 - 13.3. Fifty years of history [Seite 304]
9.4.3.1 - 13.3.1. A premature infatuation [Seite 304]
9.4.3.2 - 13.3.2. Diversification of approaches [Seite 305]
9.4.4 - 13.4. Where NF meets BCI [Seite 307]
9.4.5 - 13.5. Applications [Seite 309]
9.4.6 - 13.6. Conclusions [Seite 313]
9.4.7 - 13.7. Bibliography [Seite 314]
10 - List of Authors [Seite 319]
11 - Index [Seite 321]
12 - Contents of Volume 2 [Seite 325]
13 - Other titles from ISTE in Cognitive Science and Knowledge Management [Seite 329]
14 - EULA [Seite 332]
