EEG-Based Brain-Computer Interfaces: Cognitive Analysis and Control Applications

✍ Scribed by Dipali Bansal, Rashima Mahajan

• Publisher: Academic Press
• Year: 2019
• Tongue: English
• Leaves: 212
• Category: Library

✦ Synopsis

EEG-Based Brain-Computer Interface: Cognitive Analysis and Control Applications provides a technical approach to using brain signals for control applications, along with the EEG-related advances in BCI. The research and techniques in this book discuss time and frequency domain analysis on deliberate eye-blinking data as the basis for EEG-triggering control applications. In addition, the book provides experimental scenarios and features algorithms for acquiring real-time EEG signals using commercially available units that interface with MATLAB software for acquisition and control.

• Details techniques for multiple types of analysis (including ERP, scalp map, sub-band power and independent component) to acquire data from deliberate eye-blinking
• Demonstrates how to use EEGs to develop more intuitive BCIs in real-time scenarios
• Includes algorithms and scenarios that interface with MATLAB software for interactive use

✦ Table of Contents

Cover
EEG-BASED
BRAIN-COMPUTER
INTERFACES:
COGNITIVE ANALYSIS
AND CONTROL
APPLICATIONS
Copyright
Preface
Acknowledgments
1
Introduction
Rationale
BCI Success Stories
BCI Market Analysis
Technical Overview
Brain Anatomy
From Brain to Computer
Previous Work Related to Voluntary Eyeblink-Based BCI and Control
Objectives
References
2
EEG-Based Brain-Computer Interfacing (BCI)
Introduction
EEG-Based BCI Architecture
Signal Acquisition
Preprocessing
Feature Extraction
Classification
Translation Into Operative Control Signals
Techniques in BCI
Invasive and Partially-Invasive BCI Techniques
Electrocorticography (ECoG)
Intracortical Neuron Recording
Noninvasive BCI Techniques
Magnetoencephalography (MEG)
Functional Magnetic Resonance Imaging
Functional Near-Infrared Spectroscopy (fNIRS)
Electroencephalography (EEG)
Data Acquisition
Brain Electric Potential
EEG Electrode Positioning
EEG Electrodes
EEG Signals and Rhythms
Preamplification, Filtering and Analog-to-Digital Conversion
Preprocessing
EEG Artifacts
Physiological Artifacts
Nonphysiological Artifacts
EEG Artifact Rejection
Artifact Rejection Using Temporal Filtering
Artifact Rejection Using Spatial Filtering
Feature Extraction
EEG Signal Representation in Time Domain
Event-Related Synchronization/Desynchronization (ERS/ERD)
Evoked Potentials
Slow Cortical Potentials
EEG Signal Representation in Frequency Domain
Band Power Features
PSD Features
EEG Signal Representation in Time-Frequency Domain
Short-Time Fourier Transform
Wavelet Transform
EEG Signal Representation in Spatial Domain
Classification
Linear Classifiers
Nonlinear Classifiers
BCI Performance
BCI Applications
BCI: Clinical Applications
BCI-Based Assistive Devices for Communication
BCI-Based Assistive Devices for Locomotion and Movement
BCI for Neurorehabilitation
BCI for Cognitive State Analysis
BCI for Medical Diagnostics
BCI: Nonclinical Applications
BCI in Neuroergonomics
BCI for Smart Home
BCI in Neuromarketing and Advertising
BCI for Games and Entertainment
BCI for Security and Validation
Conclusion
References
Further Reading
3
Real-Time EEG Acquisition
Introduction
Overview of Acquisition Units
Selection Criteria in Terms of Specifications
EEG Devices
Emotive Epoc/Epoc+ Headset
Features
Emotiv EPOC+
Emotiv Insight
Features
Features
Muse
Features
OpenBCI
Features
Neurosky Mindwave
Features of TGAT1/TGAM1
Features of TGAT2
Wearable Sensing
Features
Ant Neuro (eegomylab)
Neuroelectrics (Enobio 32)
Features
Brain Products: LiveAmp (32 channels)
Brain Products: ActiCHamp
Features
Development of EEG-Based BCI for Eyeblink Acquisition
Selection of EEG Acquisition Unit
EMOTIV Test Bench
Understanding European Data Format (.edf)
Experiment Design for Eyeblink Acquisition
Acquisition of EEG Signals Using EMOTIV Test Bench
Acquisition of Online EEG Signals Directly in MATLAB
Import of EEG Data Into MATLAB
Selection of EEG Signal Analysis Toolbox
Import of EEG Data Into EEGLAB Toolbox
Import of EEG Data Into MATLAB Workspace
Import of EEG Data Into Simulink
Conclusion
References
Further Reading
4
Cognitive Analysis: Time Domain
Introduction
Preprocessing
Prefiltering
ICA of Filtered EEG Data
Channel ERP Analysis
ERP Scalp Map Analysis at Different Latencies
Result and Analysis
Conclusion
References
5
Cognitive Analysis: Frequency Domain
Introduction
Channel Spectral Analysis
Subband Power Analysis
EEG Coherence Analysis
Result and Analysis
Conclusion
References
Further Reading
6
EEG Based BCI-Control Applications
Introduction
In-House Development of Eyeblink-Based BCI for Control
Control Triggers Using MATLAB Software
Arduino Uno Hardware Interfacing for Control Applications
Possible Other Control Applications Using EEG-Based BCI
National Instruments (NI) LabVIEW-Enabled Control Using BCI
EEG-Based Prosthetic Hand Control Designed Using LabVIEW
EEG-Based Eyeblink Controlled Robot Developed in LabVIEW
EEG-Based Intelligent Stress Buster Developed in LabVIEW
Read the Smile State and Cognitive Actions Using LabVIEW
Case Studies Related to BCI Developed Using LabVIEW
A Neuropsychology Pilot Study to Examine Mental Fatigue
A Therapeutic Game for the Elderly With Notifications for Caregivers
A Real-Time System to Identify Timely Symptoms of Driver Fatigue to Prevent Accidents
Assessment of Motor Cognitive Skills in School Children
Mathworks MATLAB/Simulink-Enabled Control Using BCI
EEG-Based BCI Developed in MATLAB for Cognitive Biometrics
EEG-Based Cursor Movement Control Developed in MATLAB/Simulink
Musical Brain Cap Developed in MATLAB/Simulink
MATLAB/Simulink-Based Control of Mini Drone Using BCI
MATLAB-Based Robotic Claw Control Using BCI
Conclusion
References
Further Reading
7
Conclusion
Major Contributions
Time-Domain Analysis
Frequency-Domain Analysis
In-House Development of Eyeblink-Based BCI for Control
Future Directions and Conclusion
Index
A
B
C
D
E
F
G
H
I
K
L
M
N
O
P
R
S
T
V
W
Z
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