Chapter
3 Recording and Stimulating the Brain
3.1 Recording Signals from the Brain
3.1.1 Invasive Techniques
Tetrodes and Multi-Unit Recording
Electrocorticography (ECoG)
Optical Recording: VoltageSensitive Dyes and Two-Photon Calcium Imaging
3.1.2 Noninvasive Techniques
Electroencephalography (EEG)
Magnetoencephalography (MEG)
Functional Magnetic Resonance Imaging (fMRI)
Functional Near Infrared (fNIR) Imaging
Positron Emission Tomography (PET)
3.2 Stimulating the Brain
3.2.1 Invasive Techniques
Direct Cortical Electrical Stimulation (DCES)
3.2.2 Noninvasive Techniques
Transcranial Magnetic Stimulation (TMS)
3.3 Simultaneous Recording and Stimulation
3.3.1 Multielectrode Arrays
3.5 Questions and Exercises
4.2 Frequency Domain Analysis
4.2.2 Discrete Fourier Transform (DFT)
4.2.3 Fast Fourier Transform (FFT)
4.4.3 Autoregressive (AR) Modeling
4.5.1 Bipolar, Laplacian, and Common Average Referencing
4.5.2 Principal Component Analysis (PCA)
4.5.3 Independent Component Analysis (ICA)
4.5.4 Common Spatial Patterns (CSP)
4.6 Artifact Reduction Techniques
4.6.2 Band-Stop and Notch Filtering
4.6.4 Principal Component Analysis (PCA)
4.6.5 Independent Component Analysis (ICA)
4.8 Questions and Exercises
5.1 Classification Techniques
5.1.1 Binary Classification
Linear Discriminant Analysis (LDA)
Regularized Linear Discriminant Analysis (RDA)
Quadratic Discriminant Analysis (QDA)
Neural Networks and Perceptrons
Support Vector Machine (SVM)
5.1.2 Ensemble Classification Techniques
5.1.3 MultiClass Classification
Combining Binary Classifiers
Nearest Neighbor and k-Nearest Neighbors
Learning Vector Quantization (LVQ) and DSLVQ
5.1.4 Evaluation of Classification Performance
Confusion Matrix and ROC Curve
Information Transfer Rate (ITR)
5.2.2 Neural Networks and Backpropagation
5.2.3 Radial Basis Function (RBF) Networks
5.4 Questions and Exercises
Part II Putting It All Together
6.2 Brain Responses Useful for Building BCIs
6.2.1 Conditioned Responses
6.2.2 Population Activity
6.2.3 Imagined Motor and Cognitive Activity
6.2.4 StimulusEvoked Activity
6.4 Questions and Exercises
Part III Major Types of BCIs
7.1 Two Major Paradigms in Invasive BrainComputer Interfacing
7.1.1 BCIs Based on Operant Conditioning
7.1.2 BCIs Based on Population Decoding
7.2 Invasive BCIs in Animals
7.2.1 BCIs for Prosthetic Arm and Hand Control
Estimating Kinetic Parameters from Neural Activity
Using Local Field Potentials (LFPs) Instead of Spikes
7.2.2 BCIs for Lower-Limb Control
7.2.3 BCIs for Cursor Control
Cursor Control Using a Linear Model
Cursor Control Using a Nonlinear Kalman Filter Model
Enhancing BCI Control by Combining Proprioceptive and Visual Feedback
Cognitive BCI for Reaching Movements
Enhancing the Performance of Cognitive BCIs
7.3 Invasive BCIs in Humans
7.3.1 Cursor and Robotic Control Using a Multielectrode Array Implant
7.3.2 Cognitive BCIs in Humans
7.4 Long-Term Use of Invasive BCIs
7.4.1 Long-Term BCI Use and Formation of a Stable Cortical Representation
7.4.2 Long-Term Use of a Human BCI Implant
7.6 Questions and Exercises
8.1 Electrocorticographic (ECoG) BCIs
8.1.1 ECoG BCIs in Animals
8.1.2 ECoG BCIs in Humans
ECoG Cursor Control Based on Motor Imagery
One-Dimensional Cursor Control
TwoDimensional Cursor Control
Amplification of ECoG Activity through BCI Use
Using Classifiers to Decode ECoG Signals
ECoG BCI for Arm Movement Control
ECoG BCIs for Prosthetic Hand Control
Long-Term Stability of ECoG BCIs
8.2 BCIs Based on Peripheral Nerve Signals
8.2.2 Targeted Muscle Reinnervation (TMR)
8.4 Questions and Exercises
9.1 Electroencephalographic (EEG) BCIs
9.1.1 Oscillatory Potentials and ERD
9.1.2 Slow Cortical Potentials
9.1.3 Movement-Related Potentials
9.1.4 Stimulus-Evoked Potentials
Steady State Visually Evoked Potential (SSVEP)
Auditory Evoked Potentials
9.1.5 BCIs Based on Cognitive Tasks
9.1.6 Error Potentials in BCIs
9.2 Other Noninvasive BCIs: fMRI, MEG, and fNIR
9.2.1 Functional Magnetic Resonance Imaging-Based BCIs
9.2.2 Magnetoencephalography-Based BCIs
9.2.3 Functional Near Infrared and Optical BCIs
9.4 Questions and Exercises
10.1.1 Restoring Hearing: Cochlear Implants
10.1.2 Restoring Sight: Cortical and Retinal Implants
10.2.1 Deep Brain Stimulation (DBS)
10.3 Sensory Augmentation
10.5 Questions and Exercises
11 Bidirectional and Recurrent BCIs
11.1 Cursor Control with Direct Cortical Instruction via Stimulation
11.2 Active Tactile Exploration Using a BCI and Somatosensory Stimulation
11.3 Bidirectional BCI Control of a Mini-Robot
11.4 Cortical Control of Muscles via Functional Electrical Stimulation
11.5 Establishing New Connections between Brain Regions
11.7 Questions and Exercises
Part IV Applications and Ethics
12.1 Medical Applications
12.1.1 Sensory Restoration
12.1.3 Cognitive Restoration
12.1.5 Restoring Communication with Menus, Cursors, and Spellers
12.1.6 Brain-Controlled Wheelchairs
12.2 Nonmedical Applications
12.2.1 Web Browsing and Navigating Virtual Worlds
12.2.3 High Throughput Image Search
12.2.4 Lie Detection and Applications in Law
12.2.5 Monitoring Alertness
12.2.6 Estimating Cognitive Load
12.2.7 Education and Learning
12.2.8 Security, Identification, and Authentication
12.2.9 Physical Amplification with Exoskeletons
12.2.10 Mnemonic and Cognitive Amplification
12.2.11 Applications in Space
12.2.12 Gaming and Entertainment
12.2.13 Brain-Controlled Art
12.4 Questions and Exercises
13 Ethics of Brain-Computer Interfacing
13.1 Medical, Health, and Safety Issues
13.1.1 Balancing Risks versus Benefits
13.2 Abuse of BCI Technology
13.3 BCI Security and Privacy
13.5 Moral and Social Justice Issues
13.7 Questions and Exercises
Appendix Mathematical Background
A.1 Basic Mathematical Notation and Units of Measurement
A.2 Vectors, Matrices, and Linear Algebra
A.2.3 Eigenvectors and Eigenvalues
A.2.4 Lines, Planes, and Hyperplanes
A.3.1 Random Variables and Axioms of Probability
A.3.2 Joint and Conditional Probability
A.3.3 Mean, Variance, and Covariance
A.3.4 Probability Density Function
A.3.5 Uniform Distribution
A.3.6 Bernoulli Distribution
A.3.7 Binomial Distribution
A.3.8 Poisson Distribution
A.3.9 Gaussian Distribution
A.3.10 Multivariate Gaussian Distribution
Brain-Computer Interfacing
：An Introduction
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