Brain-computer interfaces. Foundations and methods / 1, (2016)

• Record Type: Book
• Other Titles: Foundations and methods
• Further Information: Note: Edited by Maureen Clerc, Laurent Bougrain, Fabien Lotte.
• Editors: Clerc, Maureen; Bougrain, Laurent; Lotte, Fabien
• Contents: Foreword xiii; José DEL R. MILLAN Introduction xv; Maureen CLERC, Laurent BOUGRAIN and Fabien LOTTE Part 1. Anatomy and Physiology 1 Chapter 1. Anatomy of the Nervous System 3 ; Matthieu KANDEL and Maude TOLLET 1.1. General description of the nervous system 4 1.2. The central nervous system 5 1.2.1. The telencephalon 6 1.2.2. The diencephalon 10 1.2.3. The brain stem 12 1.3. The cerebellum 14 1.4. The spinal cord and its roots 15 1.5. The peripheral nervous system 18 1.5.1. Nerves 18 1.5.2. General organization of the PNS 19 1.5.3. The autonomic nervous system 20 1.6. Some syndromes and pathologies targeted by Brain–Computer Interfaces 21 1.6.1. Motor syndromes 21 1.6.2. Some pathologies that may be treated with BCIs 22 1.7. Conclusions 23 1.8. Bibliography 24 Chapter 2. Functional Neuroimaging 25 ; Christian BÉNAR 2.1. Functional MRI 26 2.1.1. Basic principles of MRI 26 2.1.2. Principles of fMRI 26 2.1.3. Statistical data analysis: the linear model 27 2.1.4. Independent component analysis 29 2.1.5. Connectivity measures 30 2.2. Electrophysiology: EEG and MEG 31 2.2.1. Basic principles of signal generation 31 2.2.2. Event-related potentials and fields 31 2.2.3. Source localization 32 2.2.4. Independent component analysis 34 2.2.5. Time–frequency analysis 34 2.2.6. Connectivity 35 2.2.7. Statistical analysis 36 2.3. Simultaneous EEG-fMRI 37 2.3.1. Basic principles 37 2.3.2. Applications and data analysis 37 2.3.3. Connections between EEG and fMRI 38 2.4. Discussion and outlook for the future 38 2.5. Bibliography 40 Chapter 3. Cerebral Electrogenesis 45 ; Franck VIDAL 3.1. Electrical neuronal activity detected in EEG 45 3.1.1. Action and postsynaptic potentials 46 3.1.2. Resting potential, electrochemical gradient and PSPs 47 3.1.3. From PSPs to EEG 48 3.2. Dipolar and quadrupole fields 51 3.2.1. Field created by an ion current due to the opening of ion channels 51 3.2.2. Factors determining the value of the potential created by an ion current 56 3.3. The importance of geometry 57 3.3.1. Spatial summation, closed fields and open fields 57 3.3.2. Effect of synapse position on the polarity of EEG 60 3.3.3. Effect of active areas’ position 61 3.4. The influence of conductive media 62 3.4.1. Influence of glial cells 62 3.4.2. Influence of skull bones 63 3.5. Conclusions 64 3.6. Bibliography 64 Chapter 4. Physiological Markers for Controlling Active and Reactive BCIs 67 ; François CABESTAING and Philippe DERAMBURE 4.1. Introduction 67 4.2. Markers that enable active interface control 72 4.2.1. Spatiotemporal variations in potential 72 4.2.2. Spatiotemporal wave variations 74 4.3. Markers that make it possible to control reactive interfaces 77 4.3.1. Sensory evoked potentials 77 4.3.2. Endogenous P300 potential 80 4.4. Conclusions 81 4.5. Bibliography 82 Chapter 5. Neurophysiological Markers for Passive Brain–Computer Interfaces 85 ; Raphaëlle N. ROY and Jérémy FREY 5.1. Passive BCI and mental states 85 5.1.1. Passive BCI: definition 85 5.1.2. The notion of mental states 86 5.1.3. General categories of neurophysiological markers 87 5.2. Cognitive load 87 5.2.1. Definition 87 5.2.2. Behavioral markers 87 5.2.3. EEG markers 87 5.2.4. Application example: air traffic control 88 5.3. Mental fatigue and vigilance 89 5.3.1. Definition 89 5.3.2. Behavioral markers 89 5.3.3. EEG markers 89 5.3.4. Application example: driving 90 5.4. Attention 90 5.4.1. Definition 90 5.4.2. Behavioral markers 91 5.4.3. EEG markers 91 5.4.4. Application example: teaching 92 5.5. Error detection 92 5.5.1. Definition 92 5.5.2. Behavioral markers 92 5.5.3. EEG markers 93 5.5.4. Application example: tactile and robotic interfaces 93 5.6. Emotions 94 5.6.1. Definition 94 5.6.2. Behavioral markers 94 5.6.3. EEG markers 94 5.6.4. Application example: communication and personal development 95 5.7. Conclusions 96 5.8. Bibliography 96 Part 2. Signal Processing and Machine Learning 101 Chapter 6. Electroencephalography Data Preprocessing 103 ; Maureen CLERC 6.1. Introduction 103 6.2. Principles of EEG acquisition 104 6.2.1. Montage 104 6.2.2. Sampling and quantification 105 6.3. Temporal representation and segmentation 105 6.3.1. Segmentation 106 6.3.2. Time domain preprocessing 106 6.4. Frequency representation 107 6.4.1. Fourier transform 107 6.4.2. Frequency filtering 108 6.5. Time–frequency representations 109 6.5.1. Time–frequency atom 109 6.5.2. Short-time Fourier transform 111 6.5.3. Wavelet transform 112 6.5.4. Time–frequency transforms of discrete signals 114 6.5.5. Toward other redundant representations 114 6.6. Spatial representations 115 6.6.1. Topographic representations 115 6.6.2. Spatial filtering 116 6.6.3. Source reconstruction 118 6.6.4. Using spatial representations in BCI 120 6.7. Statistical representations 121 6.7.1. Principal component analysis 121 6.7.2. Independent component analysis 122 6.7.3. Using statistical representations in BCI 122 6.8. Conclusions 123 6.9. Bibliography 124 Chapter 7. EEG Feature Extraction 127 ; Fabien LOTTE and Marco CONGEDO 7.1. Introduction 127 7.2. Feature extraction 127 7.3. Feature extraction for BCIs employing oscillatory activity 130 7.3.1. Basic design for BCI using oscillatory activity 130 7.3.2. Toward more advanced, multiple electrode BCIs 131 7.3.3. The CSP algorithm 133 7.3.4. Illustration on real data 135 7.4. Feature extraction for the BCIs employing EPs 137 7.4.1. Spatial filtering for BCIs employing EPs 138 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 ; Christophe POUZAT 8.1. Introduction 145 8.1.1. Why is recording neuronal populations desirable? 146 8.1.2. How can neuronal populations be recorded? 146 8.1.3. The properties of extracellular data and the necessity of spike sorting 147 8.2. The origin of the signal and its consequences 148 8.2.1. Relationship between current and potential in a homogeneous medium 148 8.2.2. Relationship between the derivatives of the membrane potential and the transmembrane current 150 8.2.3. “From electrodes to tetrodes” 154 8.3. Spike sorting: a chronological presentation 155 8.3.1. Naked eye sorting 155 8.3.2. Window discriminator (1963) 155 8.3.3. Template matching (1964) 156 8.3.4. Dimension reduction and clustering (1965) 157 8.3.5. Principal
• Edition: 1st
• Publisher Details: London : Wiley-ISTE
• Publication Date: 2016
• Extent: 1 online resource
• Subjects: 612.82; Brain-computer interfaces
• Languages: English
• ISBNs: 9781119144991; 9781119144984
• Related ISBNs: 9781848218260
• Notes: Note: Description based on CIP data; item not viewed.
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.