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Neurotechnology: Methods, advances and applications [Kõva köide]

Edited by (Brain Institute, Federal University of Rio Grande do Norte, Brazil), Edited by (Aristotle University of Thessaloniki (AUTH), Laboratory of Medical Physics, Greece), Edited by (University of Fortaleza (UNIFOR), Brazil)
  • Formaat: Hardback, 312 pages, kõrgus x laius: 234x156 mm
  • Sari: Healthcare Technologies
  • Ilmumisaeg: 13-Jul-2020
  • Kirjastus: Institution of Engineering and Technology
  • ISBN-10: 178561813X
  • ISBN-13: 9781785618130
Teised raamatud teemal:
Neurotechnology: Methods, advances and applicationsSuurem pilt
  • Formaat: Hardback, 312 pages, kõrgus x laius: 234x156 mm
  • Sari: Healthcare Technologies
  • Ilmumisaeg: 13-Jul-2020
  • Kirjastus: Institution of Engineering and Technology
  • ISBN-10: 178561813X
  • ISBN-13: 9781785618130
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781785618130.html
Märksõnad:

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.



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.

About the editors xi
Foreword xiii
Acknowledgements xv
1 A brief introduction to neurotechnology: old challenges and new battlegrounds
1(6)
Victor Hugo C. de Albuquerque
Alkinoos Athanasiou
Sidarta Ribeiro
2 Current trends of biomedical signal processing in neuroscience
7(30)
Christiane M. Nday
Christina E. Plomariti
Vasilis D. Nigdelis
Giorgos Ntakakis
Manousos Klados
Panagiotis D. Bamidis
2.1 Introduction
7(3)
2.2 Main sections
10(18)
2.2.1 EEG pre-processing and feature extraction
10(1)
2.2.2 Inverse problem solution
11(5)
2.2.3 Principles of FC and NBS analysis
16(4)
2.2.4 Graph theory analysis of functional brain networks
20(4)
2.2.5 Biomedical signal-processing application on sleep analysis through PSG data
24(2)
2.2.6 Biomedical signal-processing application on psychiatric EEG data
26(2)
2.3 Open frontiers and conclusions
28(1)
References
29(8)
3 Neuroimage acquisition and analysis
37(32)
Thomas Schultz
3.1 Introduction
37(1)
3.2 Neuroimaging modalities
38(5)
3.2.1 Magnetic resonance imaging
38(1)
3.2.2 Functional MRI
39(2)
3.2.3 Diffusion MRI
41(1)
3.2.4 Positron emission tomography
42(1)
3.2.5 EEG and MEG
43(1)
3.3 Image registration
43(6)
3.3.1 Cost functions for registration
44(1)
3.3.2 Linear registration
45(2)
3.3.3 Nonlinear registration
47(1)
3.3.4 Standard spaces and templates
48(1)
3.4 Image segmentation
49(5)
3.4.1 Deformable models
50(1)
3.4.2 Markov random fields
51(2)
3.4.3 Convolutional neural networks
53(1)
3.5 Statistical testing
54(5)
3.5.1 Statistical parametric maps and familywise errors
55(1)
3.5.2 Voxel-based morphometry
56(1)
3.5.3 Modeling task-based fMRI
57(1)
3.5.4 Modeling resting-state fMRI
58(1)
3.5.5 Modeling dMRI
58(1)
3.6 Predictive modeling
59(3)
3.6.1 Supervised classification and regression
60(1)
3.6.2 Features for predictive modeling
60(1)
3.6.3 Hyperparameter tuning and evaluation
61(1)
3.7 Outlook
62(1)
References
62(7)
4 Virtual and augmented reality in neuroscience
69(28)
Panagiotis E. Antoniou
Alkinoos Athanasiou
Panagiotis D. Bamidis
4.1 Introduction
69(2)
4.2 BCI trends
71(2)
4.3 Neurorehabilitation and neurotherapy
73(3)
4.4 Operative virtual guidance and neurosurgical education
76(2)
4.5 Virtual reality, the virtual laboratory and the case for neuroanatomy
78(1)
4.6 Event related potentials (ERPs) from virtual stimuli
79(3)
4.7 Toward an integrated sensor immersion ecosystem
82(2)
4.8 Conclusions
84(2)
List of abbreviations
85(12)
References
86(11)
5 EEG-based biometric systems
97(58)
Jardel das C. Rodrigues
Pedro P. Rebougas Filho
Robertas Damasevicius
Victor Hugo C. de Albuquerque
5.1 Survey scope
100(1)
5.2 Contributions
100(1)
5.3 EEG-based person authentication and identification systems
101(19)
5.3.1 Artificial neural networks, convolutional neural networks and extensions
101(3)
5.3.2 Cross correlation
104(2)
5.3.3 L1/L2 and cosine distance
106(2)
5.3.4 Random forest
108(1)
5.3.5 SVM, support vector data description, and extensions
109(4)
5.3.6 Bayes classifier
113(1)
5.3.7 k-Nearest neighbors
113(1)
5.3.8 Gaussian mixture model
114(1)
5.3.9 Linear/quadratic classifiers
115(2)
5.3.10 Classifiers not defined
117(1)
5.3.11 Final considerations
118(2)
5.4 Paradigms for signals acquisition
120(7)
5.4.1 REO and REC
120(1)
5.4.2 ERP
120(2)
5.4.3 RSVP
122(5)
5.4.4 Motor movement/motor imagery
127(1)
5.4.5 Steady-state evoked potentials (SSEVP)
127(1)
5.5 Datasets and devices
127(9)
5.5.1 UCI EEG dataset
127(2)
5.5.2 Graz-BCI dataset
129(1)
5.5.3 Australian EEG dataset (AED)
130(1)
5.5.4 Poulos dataset
131(1)
5.5.5 Keirn dataset
131(1)
5.5.6 BCI CSU dataset
131(1)
5.5.7 PhysioNet EEGMMI dataset
131(1)
5.5.8 Yeom dataset
132(1)
5.5.9 Miyamoto dataset
132(1)
5.5.10 DEAP dataset
133(1)
5.5.11 Ullsperger dataset
133(1)
5.5.12 Mu dataset
133(1)
5.5.13 Cho dataset
134(1)
5.5.14 PhysioUnicaDB dataset
134(1)
5.5.15 Shin dataset
134(1)
5.5.16 EEG devices
134(2)
5.6 Biometric systems: general characteristics
136(1)
5.6.1 Performance metrics
136(1)
5.7 Requirements for security based on EEG authentication
137(3)
5.7.1 Advantages and disadvantages of EEG biometrics
139(1)
5.7.2 Feasibility of EEG signals for security -- perspectives
139(1)
5.8 Discussion, open issues, and directions for future works
140(1)
5.9 Learned lessons and conclusions
141(1)
References
142(13)
6 The evolution of passive brain---computer interfaces: enhancing the human---machine interaction
155(26)
Nicolina Sciaraffa
Pietro Arico
Gianluca Borghini
Gianluca Di Flumeri
Antonio Di Florio
Fabio Babiloni
6.1 Passive BCI as mind---computer interface
156(3)
6.1.1 Passive BCI applications
157(2)
6.2 Passive BCI system description
159(5)
6.2.1 New technology for passive BCI
160(1)
6.2.2 Signal processing
161(1)
6.2.3 Features extraction
161(1)
6.2.4 Classification techniques
162(2)
6.3 Laboratory vs. realistic passive BCI example applications
164(6)
6.3.1 Datasets
164(2)
6.3.2 Methods
166(2)
6.3.3 Results
168(1)
6.3.4 Discussion
169(1)
6.4 Limits, possible solutions, and future trends
170(2)
References
172(9)
7 Neurorobotics: review of underlying technologies, current developments, and future directions
181(34)
Christos Dimitrousis
Sofia Almpani
Petros Stefaneas
Jan Veneman
Kostas Nizamis
Alexander Astaras
7.1 Introduction
181(1)
7.2 State of the art: underlying technologies
182(8)
7.2.1 Advances in electronics
183(3)
7.2.2 Advances in software design
186(2)
7.2.3 Advances in electromechanical engineering design
188(1)
7.2.4 Improvements in electronics---neuron interfaces
189(1)
7.3 Neural human---robot interfaces
190(4)
7.3.1 Neural---electronics interfaces
192(1)
7.3.2 Affective robotics
193(1)
7.4 Neural rehabilitation robotics
194(4)
7.4.1 Robotic technologies for neural rehabilitation of the lower and upper limb
194(3)
7.4.2 Motor intention decoding for robotic exoskeleton control
197(1)
7.5 Robotic prosthesis
198(2)
7.5.1 Neuroprosthetics
199(1)
7.6 Future directions
200(6)
7.6.1 Expected advances in key technologies
200(1)
7.6.2 Convergence of key technologies
201(1)
7.6.3 Expected demand
202(1)
7.6.4 Home-based rehabilitation
203(1)
7.6.5 Research into consciousness
203(1)
7.6.6 Legal and ethical issues
204(2)
7.7 Conclusions
206(1)
Acknowledgments
207(1)
References
207(8)
8 Mobile apps for neuroscience
215(32)
Albert-Jan Plate
Pieter Kubben
8.1 Introduction
215(1)
8.2 Platforms for apps
216(6)
8.2.1 Smartphones and tablets
216(1)
8.2.2 Smartwatches and fitness trackers
217(1)
8.2.3 IoT and wearables
218(2)
8.2.4 Cloud vs. edge layer
220(1)
8.2.5 Hardware add-ons for smartphones
221(1)
8.3 Use cases of mobile apps
222(12)
8.3.1 Research
222(2)
8.3.2 Diagnoses
224(5)
8.3.3 Pre-surgical planning
229(1)
8.3.4 Predicting
230(2)
8.3.5 Training
232(1)
8.3.6 Communication
233(1)
8.3.7 Patient education
233(1)
8.4 Risks and limitations
234(5)
8.4.1 Risks
234(1)
8.4.2 Privacy and security
235(2)
8.4.3 Quality control
237(2)
8.5 Benefits
239(1)
8.5.1 Data collecting and analysis
239(1)
8.5.2 Simultaneous reporting and monitoring
240(1)
8.5.3 End-to-end connectivity
240(1)
8.5.4 Reducing costs and time
240(1)
8.6 Developing apps
240(2)
8.6.1 Native vs. Hybrid
240(2)
8.6.2 Native apps from a single source code
242(1)
References
242(5)
9 Ideas for a school of the future
247(34)
Sidarta Ribeiro
Valter Fernandes
Natalia Bezerra Mota
Guilherme Brockington
Sabine Pompeia
Roberta Ekuni
Felipe Pegado
Ana Raquel Torres
Patrick Coquerel
Angela Naschold
Andrea Deslandes
Mauro Copelli
Janaina Weissheimer
9.1 Introduction
248(2)
9.1.1 Mens sana in corpore sano
248(1)
9.1.2 Mangia que te fa bene
248(2)
9.2 Sleep before and after learning
250(1)
9.3 Move on!
250(5)
9.4 Game-based education and assessment of individual learning
255(2)
9.5 To read, perchance to learn
257(2)
9.6 Improving retention of academic content by practicing retrieval
259(3)
9.7 Repeat yet surprise
262(1)
9.8 Brains in synchrony: a bridge between neuroscience and education
262(3)
9.9 Conclusions
265(16)
References
266(15)
Index 281
Victor Hugo C. de Albuquerque [ M'17, SM'19] is a professor and senior researcher at the University of Fortaleza (UNIFOR), Brazil and director of Data Science at the Superintendency for Research and Public Safety Strategy of Ceará State (SUPESP/CE), Brazil. He has a Ph.D. in mechanical engineering from the Federal University of Paraíba, an M.Sc. in teleinformatics engineering from the Federal University of Cearaá, and he graduated in mechatronics engineering at the Federal Center of Technological Education of Ceará. He is currently a full professor of the Graduate Program in Applied Informatics of UNIFOR and leader of the Industrial Informatics, Electronics and Health Research Group (CNPq). He is an expert, mainly, in IoT, machine/deep learning, pattern recognition, and robotics.



Alkinoos Athanasiou is a board-certified neurosurgeon and a post-doc researcher at the Medical Physics Lab, School of Medicine, Aristotle University of Thessaloniki, Greece. He is employed as associated scientific staff at the Hellenic Open University, teaching Neuronal Restoration Engineering. He serves as elected Secretary and member of the administrative board of the Hellenic Society for Biomedical Technology. His research interests lie with neurorehabilitation and neurotechnology. He has been awarded more than 15 research grants and awards in total.



Sidarta Ribeiro is a professor of neuroscience and director of the Brain Institute at the Federal University of Rio Grande do Norte, Brazil. He has considerable experience in the areas of neuroethology, molecular neurobiology, and systems neurophysiology. He currently coordinates the Brazilian committee of the Pew Latin American Fellows Program in the Biomedical Sciences, and is a member of the steering committee of the Latin American School of Education, Cognitive and Neural Sciences.