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

Description

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-blinkingDemonstrates how to use EEGs to develop more intuitive BCIs in real-time scenariosIncludes algorithms and scenarios that interface with MATLAB software for interactive use 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 Back Cover