Neurotechnology: Methods, advances and applications (Healthcare Technologies)

✍ Scribed by Victor Hugo C. de Albuquerque (editor), Alkinoos Athanasiou (editor), Sidarta Ribeiro (editor)

• Publisher: Institution of Engineering and Technology
• Year: 2020
• Tongue: English
• Leaves: 310
• Category: Library

✦ Synopsis

This book focuses on recent advances and future trends in the methods and applications of technologies that are used in neuroscience for the evaluation, diagnosis and treatment of neurological diseases and conditions or for the improvement of quality of life. The editors have assembled contributions from a range of international experts, to bring together key topics in neurotechnology, neuroengineering, and neurorehabilitation. The book explores biomedical signal processing, neuroimaging acquisition and analysis, computational intelligence, virtual and augmented reality, biometrics, machine learning and neurorobotics, human machine interaction, mobile apps and discusses ways in which these neural technologies can be used as diagnostic tools, research methods, treatment modalities, as well as in devices and apps in everyday life.

This cross-disciplinary topic is of particular interest to researchers and professionals with a background in neuroscience-related disciplines and neurotechnology, but also touches on a wide range of other fields including biomedical engineering, AI, medicine, healthcare, security and industry, among others.

✦ Table of Contents

Cover
Contents
About the editors
Foreword
Acknowledgements
1 A brief introduction to neurotechnology: old challenges and new battlegrounds
2 Current trends of biomedical signal processing in neuroscience
2.1 Introduction
2.2 Main sections
2.2.1 EEG pre-processing and feature extraction
2.2.2 Inverse problem solution
2.2.3 Principles of FC and NBS analysis
2.2.4 Graph theory analysis of functional brain networks
2.2.5 Biomedical signal-processing application on sleep analysis through PSG data
2.2.6 Biomedical signal-processing application on psychiatric EEG data
2.3 Open frontiers and conclusions
References
3 Neuroimage acquisition and analysis
3.1 Introduction
3.2 Neuroimaging modalities
3.2.1 Magnetic resonance imaging
3.2.2 Functional MRI
3.2.3 Diffusion MRI
3.2.4 Positron emission tomography
3.2.5 EEG and MEG
3.3 Image registration
3.3.1 Cost functions for registration
3.3.2 Linear registration
3.3.3 Nonlinear registration
3.3.4 Standard spaces and templates
3.4 Image segmentation
3.4.1 Deformable models
3.4.2 Markov random fields
3.4.3 Convolutional neural networks
3.5 Statistical testing
3.5.1 Statistical parametric maps and familywise errors
3.5.2 Voxel-based morphometry
3.5.3 Modeling task-based fMRI
3.5.4 Modeling resting-state fMRI
3.5.5 Modeling dMRI
3.6 Predictive modeling
3.6.1 Supervised classification and regression
3.6.2 Features for predictive modeling
3.6.3 Hyperparameter tuning and evaluation
3.7 Outlook
References
4 Virtual and augmented reality in neuroscience
4.1 Introduction
4.2 BCI trends
4.3 Neurorehabilitation and neurotherapy
4.4 Operative virtual guidance and neurosurgical education
4.5 Virtual reality, the virtual laboratory and the case for neuroanatomy
4.6 Event related potentials (ERPs) from virtual stimuli
4.7 Toward an integrated sensor immersion ecosystem
4.8 Conclusions
List of abbreviations
References
5 EEG-based biometric systems
5.1 Survey scope
5.2 Contributions
5.3 EEG-based person authentication and identification systems
5.3.1 Artificial neural networks, convolutional neural networks and extensions
5.3.2 Cross correlation
5.3.3 L1/L2 and cosine distance
5.3.4 Random forest
5.3.5 SVM, support vector data description, and extensions
5.3.6 Bayes classifier
5.3.7 k-Nearest neighbors
5.3.8 Gaussian mixture model
5.3.9 Linear/quadratic classifiers
5.3.10 Classifiers not defined
5.3.11 Final considerations
5.4 Paradigms for signals acquisition
5.4.1 REO and REC
5.4.2 ERP
5.4.2.1 VEP
5.4.2.2 P300
5.4.2.3 N400
5.4.3 RSVP
5.4.4 Motor movement/motor imagery
5.4.5 Steady-state evoked potentials (SSEVP)
5.5 Datasets and devices
5.5.1 UCI EEG dataset
5.5.2 Graz-BCI dataset
5.5.3 Australian EEG dataset (AED)
5.5.4 Poulos dataset
5.5.5 Keirn dataset
5.5.6 BCI CSU dataset
5.5.7 PhysioNet EEGMMI dataset
5.5.8 Yeom dataset
5.5.9 Miyamoto dataset
5.5.10 DEAP dataset
5.5.11 Ullsperger dataset
5.5.12 Mu dataset
5.5.13 Cho dataset
5.5.14 PhysioUnicaDB dataset
5.5.15 Shin dataset
5.5.16 EEG devices
5.6 Biometric systems: general characteristics
5.6.1 Performance metrics
5.7 Requirements for security based on EEG authentication
5.7.1 Advantages and disadvantages of EEG biometrics
5.7.2 Feasibility of EEG signals for security – perspectives
5.8 Discussion, open issues, and directions for future works
5.9 Learned lessons and conclusions
References
6 The evolution of passive brain–computer interfaces: enhancing the human–machine interaction
6.1 Passive BCI as mind–computer interface
6.1.1 Passive BCI applications
6.1.1.1 User-state monitoring
6.1.1.2 Training, education, and cognitive improvement
6.1.1.3 Gaming and entertainment
6.1.1.4 Evaluation
6.1.1.5 Safety and security
6.2 Passive BCI system description
6.2.1 New technology for passive BCI
6.2.2 Signal processing
6.2.3 Features extraction
6.2.4 Classification techniques
6.3 Laboratory vs. realistic passive BCI example applications
6.3.1 Datasets
6.3.1.1 Laboratory dataset
6.3.1.2 Operational dataset
6.3.2 Methods
6.3.2.1 Signal processing
6.3.2.2 Features extraction
6.3.2.3 Classification
6.3.3 Results
6.3.4 Discussion
6.4 Limits, possible solutions, and future trends
References
7 Neurorobotics: review of underlying technologies, current developments, and future directions
7.1 Introduction
7.2 State of the art: underlying technologies
7.2.1 Advances in electronics
7.2.1.1 Deep sub-micron VLSI and neural computation
7.2.1.2 Parallel distributed processing and neural computation
7.2.1.3 Memristors, a missing piece of the technological puzzle
7.2.1.4 From MEMS to NEMS and beyond
7.2.2 Advances in software design
7.2.2.1 Artificial intelligence
7.2.2.2 Machine learning
7.2.2.3 Robot vision
7.2.3 Advances in electromechanical engineering design
7.2.4 Improvements in electronics–neuron interfaces
7.2.4.1 Advances in electrode design
7.3 Neural human–robot interfaces
7.3.1 Neural–electronics interfaces
7.3.2 Affective robotics
7.4 Neural rehabilitation robotics
7.4.1 Robotic technologies for neural rehabilitation of the lower and upper limb
7.4.1.1 Lower limb robotic exoskeletons
7.4.1.2 Upper limb robotic exoskeletons
7.4.2 Motor intention decoding for robotic exoskeleton control
7.4.2.1 Noninvasive HMIs
7.4.2.2 Invasive HMIs
7.5 Robotic prosthesis
7.5.1 Neuroprosthetics
7.6 Future directions
7.6.1 Expected advances in key technologies
7.6.2 Convergence of key technologies
7.6.3 Expected demand
7.6.4 Home-based rehabilitation
7.6.5 Research into consciousness
7.6.6 Legal and ethical issues
7.7 Conclusions
Acknowledgments
References
8 Mobile apps for neuroscience
8.1 Introduction
8.2 Platforms for apps
8.2.1 Smartphones and tablets
8.2.2 Smartwatches and fitness trackers
8.2.3 IoT and wearables
8.2.3.1 What is IoT?
8.2.3.2 What are connected wearables?
8.2.3.3 Use cases of wearables in the medical sector
8.2.4 Cloud vs. edge layer
8.2.5 Hardware add-ons for smartphones
8.3 Use cases of mobile apps
8.3.1 Research
8.3.1.1 Advantages
8.3.1.2 Apple's ResearchKit
8.3.2 Diagnoses
8.3.2.1 Tremor detection
8.3.2.2 Detection of a stroke
8.3.2.3 Motor performance testing
8.3.2.4 Mobile clinical decision support systems
8.3.3 Pre-surgical planning
8.3.4 Predicting
8.3.4.1 Self-monitoring, follow-up and treatment intervention
8.3.4.2 Monitoring a stroke
8.3.5 Training
8.3.6 Communication
8.3.7 Patient education
8.4 Risks and limitations
8.4.1 Risks
8.4.1.1 General
8.4.1.2 Research
8.4.2 Privacy and security
8.4.2.1 General
8.4.2.2 GDPR and HIPAA
8.4.3 Quality control
8.4.3.1 FDA
8.4.3.2 The European Union
8.5 Benefits
8.5.1 Data collecting and analysis
8.5.2 Simultaneous reporting and monitoring
8.5.3 End-to-end connectivity
8.5.4 Reducing costs and time
8.6 Developing apps
8.6.1 Native vs. Hybrid
8.6.1.1 Native apps
8.6.1.2 Hybrid apps
8.6.2 Native apps from a single source code
References
9 Ideas for a school of the future
9.1 Introduction
9.1.1 Mens sana in corpore sano
9.1.2 Mangia que te fa bene
9.2 Sleep before and after learning
9.3 Move on!
9.4 Game-based education and assessment of individual learning
9.5 To read, perchance to learn
9.6 Improving retention of academic content by practicing retrieval
9.7 Repeat yet surprise
9.8 Brains in synchrony: a bridge between neuroscience and education
9.9 Conclusions
References
Index
Back Cover