LEADER 06522nam 22008295 450 001 9910299669803321 005 20251113205400.0 010 $a9783319109787 010 $a3319109782 024 7 $a10.1007/978-3-319-10978-7 035 $a(CKB)3710000000271814 035 $a(EBL)1968482 035 $a(SSID)ssj0001386207 035 $a(PQKBManifestationID)11798986 035 $a(PQKBTitleCode)TC0001386207 035 $a(PQKBWorkID)11349526 035 $a(PQKB)11678159 035 $a(DE-He213)978-3-319-10978-7 035 $a(MiAaPQ)EBC1968482 035 $a(PPN)183097904 035 $a(EXLCZ)993710000000271814 100 $a20141101d2015 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aBrain-Computer Interfaces $eCurrent Trends and Applications /$fedited by Aboul Ella Hassanien, Ahmad Taher Azar 205 $a1st ed. 2015. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2015. 215 $a1 online resource (422 p.) 225 1 $aIntelligent Systems Reference Library,$x1868-4408 ;$v74 300 $aDescription based upon print version of record. 311 08$a9783319109770 311 08$a3319109774 320 $aIncludes bibliographical references at the end of each chapters. 327 $aForeword; Preface; Contents; Part I General Views on Brain-ComputerInterfacing; 1 Brain Computer Interface: A Review; Abstract; 1.1 Introduction; 1.2 Neuroimaging-Based Approaches in the BCI; 1.2.1 The Neuroimaging Modalities; 1.2.1.1 Electroencephalography; 1.2.1.2 Electrocorticography; 1.2.1.3 Magnetoencephalography; 1.2.1.4 Intracortical Neuron Recording; 1.2.1.5 Functional Magnetic Resonance Imaging; 1.2.1.6 Near Infrared Spectroscopy; 1.3 Control Signals in BCI Systems; 1.3.1 EEG Signal Processing for BCI; 1.3.1.1 Data Collection Through Electrodes 327 $a1.3.1.2 Pre-processing Methods in BCI Designs1.3.1.3 Sources of Noise in EEG Signal; 1.3.2 Preprocessing Techniques that Deal with EOG/EMG Artifacts; 1.3.3 Feature Extraction for BCI Designs; 1.3.3.1 EEG Features; 1.3.3.2 Feature Dimension Reduction Techniques; 1.3.4 Classification Methods and Post-processing; 1.3.4.1 Properties of Classifiers; 1.3.4.2 Brief Survey of Classifiers Used in BCI Research; 1.3.4.3 Linear Classifiers; 1.3.4.4 Neural Networks; 1.3.4.5 Nonlinear Bayesian Classifiers; 1.3.4.6 Nearest Neighbor Classifiers; 1.3.4.7 Combinations of Classifiers 327 $a1.3.5 Classification Performance Metrics1.4 Conclusion; References; 2 Basics of Brain Computer Interface; Abstract; 2.1 Introduction; 2.2 Brain Anatomy; 2.3 Brain Computer Interface Types; 2.3.1 Invasive BCI Acquisition Techniques; 2.3.2 Partially Invasive BCI Acquisition Techniques; 2.3.3 Non Invasive BCI Acquisition Techniques; 2.4 Types of BCI Signals; 2.5 Components of Interest; 2.5.1 Oscillatory EEG Activity; 2.5.2 Event-Related Potentials; 2.6 Monitoring Brain Activity Using EEG; 2.7 BCI System; 2.8 BCI Monitoring Hardware and Software; 2.9 Brain Computer Interface Applications 327 $a2.10 BCI Trends2.11 Conclusion; References; 3 Noninvasive Electromagnetic Methods for Brain Monitoring: A Technical Review; Abstract; 3.1 Introduction; 3.2 Human Brain Anatomy; 3.3 Brain Diseases; 3.4 Noninvasive Brain Monitoring; 3.4.1 Advantages of PET; 3.4.2 Disadvantages of PET; 3.5 Electromagnetic Brain Monitoring Methods; 3.5.1 Brain Metabolism and Brain Imaging; 3.5.2 Electroencephalography (EEG); 3.5.2.1 History; 3.5.2.2 EEG Potentials; 3.5.2.3 Source of Brain Potentials; 3.5.2.4 The EEG Interpretation; 3.5.2.5 Brain Waves and EEG Diagnosis; 3.5.2.6 Why EEG; 3.5.2.7 How It Works 327 $a3.5.2.8 EEG Instrumentation3.5.2.9 Preparation for an EEG Test; 3.5.2.10 Regular or Standard EEG; 3.5.2.11 Sleep-Deprived EEG; 3.5.2.12 Long Term EEG; 3.5.2.13 Ambulatory EEG; 3.5.2.14 Advantages of EEG; 3.5.2.15 Disadvantages; 3.5.2.16 Electrode Placement in EEG: 10&hx2013; 20 System (EEG); 3.5.3 Magnetoencephalography (MEG); 3.5.3.1 History; 3.5.3.2 Why Is an MEG Performed?; 3.5.3.3 How MEG Work; 3.5.3.4 Advantages of MEG; 3.5.3.5 Disadvantages of MEG; 3.5.4 Electrocorticography (ECoG); 3.5.4.1 Clinical Applications; 3.5.4.2 Advantages of ECoG; 3.5.4.3 Disadvantages of ECoG 327 $a3.5.5 Electroneurogram (ENG) 330 $aThe success of a BCI system depends as much on the system itself as on the user?s ability to produce distinctive EEG activity. BCI systems can be divided into two groups according to the placement of the electrodes used to detect and measure neurons firing in the brain. These groups are: invasive systems, electrodes are inserted directly into the cortex are used for single cell or multi unit recording, and electrocorticography (EcoG), electrodes are placed on the surface of the cortex (or dura); noninvasive systems, they are placed on the scalp and use electroencephalography (EEG) or magnetoencephalography (MEG) to detect neuron activity. The book is basically divided into three parts. The first part of the book covers the basic concepts and overviews of Brain Computer Interface. The second part describes new theoretical developments of BCI systems. The third part covers views on real applications of BCI systems. 410 0$aIntelligent Systems Reference Library,$x1868-4408 ;$v74 606 $aComputational intelligence 606 $aArtificial intelligence 606 $aUser interfaces (Computer systems) 606 $aHuman-computer interaction 606 $aNeurosciences 606 $aComputational Intelligence 606 $aArtificial Intelligence 606 $aUser Interfaces and Human Computer Interaction 606 $aNeuroscience 615 0$aComputational intelligence. 615 0$aArtificial intelligence. 615 0$aUser interfaces (Computer systems). 615 0$aHuman-computer interaction. 615 0$aNeurosciences. 615 14$aComputational Intelligence. 615 24$aArtificial Intelligence. 615 24$aUser Interfaces and Human Computer Interaction. 615 24$aNeuroscience. 676 $a573.860113 702 $aHassanien$b Aboul Ella$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aAzar$b Ahmad Taher$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910299669803321 996 $aBrain-computer interfaces$9260348 997 $aUNINA