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Table of contents
Chapter 1
Brain-computer Interfaces
1.1 BCI definition
1.2 BCI architecture
1.2.1 Signal acquisition
1.2.2 Signal processing
1.2.2.1 Feature extraction
1.2.2.2 Feature classification
1.2.3 System output
1.2.4 Feedback
1.3 BCI taxonomy
1.3.1 Dependent and independent BCIs
1.3.2 Exogenous and endogenous BCIs
1.3.3 Passive and active BCIs
1.3.4 Hybrid BCIs
1.3.5 BCI operating protocols
1.3.5.1 Synchronous protocols
1.3.5.2 Self-paced protocols
1.3.6 Continuous and discrete decoding
1.4 Brain Signals for BCI
1.4.1 Invasive recording techniques
1.4.1.1 Electrocorticography
1.4.1.2 Intracortical Recordings
1.4.2 Non-invasive recording techniques
1.4.2.1 Functional Magnetic Resonance Imaging (fMRI)
1.4.2.2 Functional Near Infrared
1.4.2.3 Magnetoencephalography (MEG)
1.4.2.4 Electroencephalography (EEG)
Chapter 2
EEG signals and motor imagery
2.1 EEG signals
2.1.1 EEG brain rhythms
2.1.2 International 10-20 system
2.2 Primary motor cortex
2.3 Sensorimotor rhythms
2.3.1 Event-related desynchronization
2.3.2 Event-related synchronization
2.3.3 Lateralization
2.3.4 Spatial mapping of ERD/ERS
2.4 Event-related potentials
2.5 Time course of ERD/ERS
2.6 Combined movements
2.6.1 4-class database
2.6.1.1 Paradigm and time scheme
2.6.2 ERD/ERS% analysis
2.6.3 Statistical analysis
Chapter 3
Robotic arm control
3.1 8-class database
3.1.1 Paradigm and time scheme
3.1.2 Oscillatory power analysis
3.1.3 Statistical analysis
Chapter 4
Signal Processing
4.1 Feature extraction
4.1.1 Covariance matrix
4.1.2 Normal distribution and eigenvalue decomposition
4.1.3 Common Spatial Patterns (CSP)
4.2 Analytical Common Spatial Patterns (ACSP)
4.3 Feature selection
4.3.1 Mutual information
4.4 Filter Bank Common Spatial Pattern (FBCSP)
4.4.1 Mutual Information-based Best Individual Feature (MIBIF) algorithm
4.5 Common Spatial Pattern by Joint Approximate Diagonalization (CSP by JAD)
4.5.1 Information theoretic feature extraction (ITFE)
4.6 Classification
4.6.1 Discriminant functions
4.6.1.1 Two classes
4.6.1.2 Multiple classes
4.6.1.3 Linear Discriminant Analysis (LDA)
4.6.1.4 Support Vector Machines (SVM)
4.6.2 Distance-based classification
4.6.2.1 Riemannian geometry
4.6.2.1.1 Classification in the Riemannian manifold
4.6.2.2 CSP and Riemannian geometry
Chapter 5
Multiclass and multilabel approaches
5.1 Multiclass approaches
5.1.1 One-versus-one approach
5.1.2 One-versus-all approach
5.1.3 Hierarchical approach
5.2 Multilabel approaches
5.2.1 One-step Multilabel (OsM) approach
5.2.2 Hierarchical Multilabel (HM) approach
5.2.3 One-step Hierarchichal Multilabel (OsHM) approach
Chapter 6
Experimental results
6.1 Experimental parameters
6.1.1 Classification algorithms
6.2 Cross-validation
6.2.1 Cross-validation on the 4-class database
6.2.2 Cross-validation on the 8-class database
6.3 Results: summary
Conclusions
