Brain-Computer Interfaces 1

Maureen Clerc is Senior Researcher at Inria Sophia Antipolis, France. Laurent Bougrain is Assistant Professor at the University of Lorraine, France. Fabien Lotte is Junior Researcher at Inria Bordeaux, France.

• 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