Brain-Computer Interfaces 1

Methods and Perspectives

Inbunden, Engelska, 2016

AvMaureen Clerc,Laurent Bougrain,Fabien Lotte

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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.

Produktinformation

Utgivningsdatum2016-07-15
Mått163 x 241 x 25 mm
Vikt631 g
FormatInbunden
SpråkEngelska
Antal sidor330
FörlagISTE Ltd and John Wiley & Sons Inc
ISBN9781848218260

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Foreword xiiiJosé DEL R. MILLANIntroduction xvMaureen CLERC, Laurent BOUGRAIN and Fabien LOTTEPart 1. Anatomy and Physiology 1Chapter 1. Anatomy of the Nervous System 3Matthieu KANDEL and Maude TOLLET1.1. General description of the nervous system 41.2. The central nervous system 51.2.1. The telencephalon 61.2.2. The diencephalon 101.2.3. The brain stem 121.3. The cerebellum 141.4. The spinal cord and its roots 151.5. The peripheral nervous system 181.5.1. Nerves 181.5.2. General organization of the PNS 191.5.3. The autonomic nervous system 201.6. Some syndromes and pathologies targeted by Brain–Computer Interfaces 211.6.1. Motor syndromes 211.6.2. Some pathologies that may be treated with BCIs 221.7. Conclusions 231.8. Bibliography 24Chapter 2. Functional Neuroimaging 25Christian BÉNAR2.1. Functional MRI 262.1.1. Basic principles of MRI 262.1.2. Principles of fMRI 262.1.3. Statistical data analysis: the linear model 272.1.4. Independent component analysis 292.1.5. Connectivity measures 302.2. Electrophysiology: EEG and MEG 312.2.1. Basic principles of signal generation 312.2.2. Event-related potentials and fields 312.2.3. Source localization 322.2.4. Independent component analysis 342.2.5. Time–frequency analysis 342.2.6. Connectivity 352.2.7. Statistical analysis 362.3. Simultaneous EEG-fMRI 372.3.1. Basic principles 372.3.2. Applications and data analysis 372.3.3. Connections between EEG and fMRI 382.4. Discussion and outlook for the future 382.5. Bibliography 40Chapter 3. Cerebral Electrogenesis 45Franck VIDAL3.1. Electrical neuronal activity detected in EEG 453.1.1. Action and postsynaptic potentials 463.1.2. Resting potential, electrochemical gradient and PSPs 473.1.3. From PSPs to EEG 483.2. Dipolar and quadrupole fields 513.2.1. Field created by an ion current due to the opening of ion channels 513.2.2. Factors determining the value of the potential created by an ion current 563.3. The importance of geometry 573.3.1. Spatial summation, closed fields and open fields 573.3.2. Effect of synapse position on the polarity of EEG 603.3.3. Effect of active areas’ position 613.4. The influence of conductive media 623.4.1. Influence of glial cells 623.4.2. Influence of skull bones 633.5. Conclusions 643.6. Bibliography 64Chapter 4. Physiological Markers for Controlling Active and Reactive BCIs 67François CABESTAING and Philippe DERAMBURE4.1. Introduction 674.2. Markers that enable active interface control 724.2.1. Spatiotemporal variations in potential 724.2.2. Spatiotemporal wave variations 744.3. Markers that make it possible to control reactive interfaces 774.3.1. Sensory evoked potentials 774.3.2. Endogenous P300 potential 804.4. Conclusions 814.5. Bibliography 82Chapter 5. Neurophysiological Markers for Passive Brain–Computer Interfaces 85Raphaëlle N. ROY and Jérémy FREY5.1. Passive BCI and mental states 855.1.1. Passive BCI: definition 855.1.2. The notion of mental states 865.1.3. General categories of neurophysiological markers 875.2. Cognitive load 875.2.1. Definition 875.2.2. Behavioral markers 875.2.3. EEG markers 875.2.4. Application example: air traffic control 885.3. Mental fatigue and vigilance 895.3.1. Definition 895.3.2. Behavioral markers 895.3.3. EEG markers 895.3.4. Application example: driving 905.4. Attention 905.4.1. Definition 905.4.2. Behavioral markers 915.4.3. EEG markers 915.4.4. Application example: teaching 925.5. Error detection 925.5.1. Definition 925.5.2. Behavioral markers 925.5.3. EEG markers 935.5.4. Application example: tactile and robotic interfaces 935.6. Emotions 945.6.1. Definition 945.6.2. Behavioral markers 945.6.3. EEG markers 945.6.4. Application example: communication and personal development 955.7. Conclusions 965.8. Bibliography 96Part 2. Signal Processing and Machine Learning 101Chapter 6. Electroencephalography Data Preprocessing 103Maureen CLERC6.1. Introduction 1036.2. Principles of EEG acquisition 1046.2.1. Montage 1046.2.2. Sampling and quantification 1056.3. Temporal representation and segmentation 1056.3.1. Segmentation 1066.3.2. Time domain preprocessing 1066.4. Frequency representation 1076.4.1. Fourier transform 1076.4.2. Frequency filtering 1086.5. Time–frequency representations 1096.5.1. Time–frequency atom 1096.5.2. Short-time Fourier transform 1116.5.3. Wavelet transform 1126.5.4. Time–frequency transforms of discrete signals 1146.5.5. Toward other redundant representations 1146.6. Spatial representations 1156.6.1. Topographic representations 1156.6.2. Spatial filtering 1166.6.3. Source reconstruction 1186.6.4. Using spatial representations in BCI 1206.7. Statistical representations 1216.7.1. Principal component analysis 1216.7.2. Independent component analysis 1226.7.3. Using statistical representations in BCI 1226.8. Conclusions 1236.9. Bibliography 124Chapter 7. EEG Feature Extraction 127Fabien LOTTE and Marco CONGEDO7.1. Introduction 1277.2. Feature extraction 1277.3. Feature extraction for BCIs employing oscillatory activity 1307.3.1. Basic design for BCI using oscillatory activity 1307.3.2. Toward more advanced, multiple electrode BCIs 1317.3.3. The CSP algorithm 1337.3.4. Illustration on real data 1357.4. Feature extraction for the BCIs employing EPs 1377.4.1. Spatial filtering for BCIs employing EPs 1387.5. Alternative methods and the Riemannian geometry approach 1397.6. Conclusions 1417.7. Bibliography 142Chapter 8. Analysis of Extracellular Recordings 145Christophe POUZAT8.1. Introduction 1458.1.1. Why is recording neuronal populations desirable? 1468.1.2. How can neuronal populations be recorded? 1468.1.3. The properties of extracellular data and the necessity of spike sorting 1478.2. The origin of the signal and its consequences 1488.2.1. Relationship between current and potential in a homogeneous medium 1488.2.2. Relationship between the derivatives of the membrane potential and the transmembrane current 1508.2.3. “From electrodes to tetrodes” 1548.3. Spike sorting: a chronological presentation 1558.3.1. Naked eye sorting 1558.3.2. Window discriminator (1963) 1558.3.3. Template matching (1964) 1568.3.4. Dimension reduction and clustering (1965) 1578.3.5. Principal component analysis (1968) 1588.3.6. Resolving superposition (1972) 1608.3.7. Dynamic amplitude profiles of action potentials (1973) 1618.3.8. Optimal filters (1975) 1628.3.9. Stereotrodes and amplitude ratios (1983) 1658.3.10. Sampling jitter (1984) 1688.3.11. Graphical tools 1708.3.12. Automatic clustering 1718.4. Recommendations 1798.5. Bibliography 181Chapter 9. Statistical Learning for BCIs 185Rémi FLAMARY, Alain RAKOTOMAMONJY and Michèle SEBAG9.1. Supervised statistical learning 1859.1.1. Training data and the predictor function 1869.1.2. Empirical risk and regularization 1879.1.3. Classical methods of classification 1909.2. Specific training methods 1929.2.1. Selection of variables and sensors 1929.2.2. Multisubject learning, information transfer 1949.3. Performance metrics 1949.3.1. Classification performance metrics 1959.3.2. Regression performance metrics 1969.4. Validation and model selection 1979.4.1. Estimation of the performance metric 1979.4.2. Optimization of hyperparameters 2009.5. Conclusions 2029.6. Bibliography 202Part 3. Human Learning and Human–Machine Interaction 207Chapter 10. Adaptive Methods in Machine Learning 209Maureen CLERC, Emmanuel DAUCÉ and Jérémie MATTOUT10.1. The primary sources of variability 20910.1.1. Intrasubject variability 21010.1.2. Intersubject variability 21110.2. Adaptation framework for BCIs 21310.3. Adaptive statistical decoding 21410.3.1. Covariate shift 21410.3.2. Classifier adaptation 21610.3.3. Subject-adapted calibration 21810.3.4. Optimal tasks 21910.3.5. Correspondence between task and command 22110.4. Generative model and adaptation 22110.4.1. Bayesian approach 22110.4.2. Sequential decision 22410.4.3. Online optimization of stimulations 22610.5. Conclusions 22910.6. Bibliography 229Chapter 11. Human Learning for Brain–Computer Interfaces 233Camille JEUNET, Fabien LOTTE and Bernard N’KAOUA11.1. Introduction 23311.2. Illustration: two historical BCI protocols 23511.3. Limitations of standard protocols used for BCIs 23711.4. State-of-the-art in BCI learning protocols 23811.4.1. Instructions 23811.4.2. Training tasks 23911.4.3. Feedback 23911.4.4. Learning environment 24211.4.5. In summary: guidelines for designing more effective training protocols 24311.5. Perspectives: toward user-adapted and user-adaptable learning protocols 24411.6. Conclusions 24711.7. Bibliography 247Chapter 12. Brain–Computer Interfaces for Human–Computer Interaction 251Andéol EVAIN, Nicolas ROUSSEL, Géry CASIEZ, Fernando ARGELAGUET-SANZ and Anatole LÉCUYER12.1. A brief introduction to human–computer interaction 25112.1.1. Interactive systems, interface and interaction 25212.1.2. Elementary tasks and interaction techniques 25212.1.3. Theory of action feedback 25312.1.4. Usability 25412.2. Properties of BCIs from the perspective of HCI 25512.3. Which pattern for which task? 25712.4. Paradigms of interaction for BCIs 25912.4.1. BCI interaction loop 25912.4.2. Main paradigms of interaction for BCIs 26012.5. Conclusions 26512.6. Bibliography 266Chapter 13. Brain Training with Neurofeedback 271Lorraine PERRONNET, Anatole LÉCUYER, Fabien LOTTE, Maureen CLERC and Christian BARILLOT13.1. Introduction 27113.2. How does it work? 27413.2.1. Design of an NF training program 27413.2.2. Course of an NF session: where the eyes “look” at the brain 27513.2.3. A learning procedure that we still do not fully understand 27613.3. Fifty years of history 27813.3.1. A premature infatuation 27813.3.2. Diversification of approaches 27913.4. Where NF meets BCI 28113.5. Applications 28313.6. Conclusions 28713.7. Bibliography 288List of Authors 293Index 295Contents of Volume 2 299