Biometry: Technology, Trends and Applications

Can You Order Klonopin Online Cover Title Page Copyright Page Preface Table of Contents Symbol Description 1. Current and Future Biometrics: Technology and Applications 1.1 Introduction 1.2 Biometrics 1.2.1 Physiological biometrics 1.2.2 Behavioral biometrics 1.3 Technology 1.3.1 Sensors 1.3.2 Typical setups 1.3.3 Methods 1.3.4 Commercial technology 1.3.4.1 Wearable biometrics 1.3.4.2 Laboratory biometric systems 1.4 Trends 1.4.1 Connected systems 1.4.2 Digital twins 1.4.2.1 Behavioral 1.4.2.2 Human body 1.4.2.3 Sports 1.4.2.4 Healthcare 1.4.3 Smart communities 1.5 Applications 1.5.1 Health 1.5.2 Education 1.5.2.1 Neural imaging 1.5.2.2 GSR 1.5.2.3 Camera 1.5.2.4 Others 1.5.3 Business 1.5.3.1 Biometrics as a service 1.5.3.2 Financial services user authentication 1.5.3.3 Workplace biometrics 1.5.4 Marketing 1.5.4.1 Neuromarketing 1.5.4.2 General biometrics 1.5.5 Industry 1.5.5.1 How and why biometry has been applied in industries 1.5.5.2 Biometric applications which have been proven to have an impact on industries and their workers 1.5.5.3 Future research proposals and opportunities 1.5.6 Sports 1.5.6.1 Biometrics relevance in sports 1.5.6.2 Biometric applications in sports and results 1.5.6.3 Future research areas and important considerations 1.6 Glossary 2. Analysis of Electrophysiological Activity of the Nervous System: Towards Neural Engineering Applications 2.1 Bioelectrical human information 2.1.1 Electrophysiological activity of the nervous system 2.1.2 What is digital biosignal processing for? 2.1.3 PhyGUI: A didactic tool to teach DBP 2.1.3.1 Data acquisition 2.1.3.2 Time-domain analysis 2.1.3.3 Frequency-domain analysis 2.1.3.4 Time-frequency analysis 2.2 Analysis of electrophysiological activity 2.2.1 Preprocessing of electrophysiological signals 2.2.1.1 Referencing methods 2.2.1.2 Signal conditioning 2.2.1.3 Rejection and removal of artifacts 2.2.2 Processing of electrophysiological signals 2.2.2.1 Time Domain 2.2.2.2 Frequency domain 2.2.2.3 Time-frequency domain 2.2.3 Processing of EEG signals 2.2.3.1 Spontaneous activity 2.2.3.2 Evoked activity 2.2.3.3 Induced activity 2.2.4 Tools for electrophysiological analysis 2.3 Neural engineering technology 2.3.1 Neuronal interface for the evaluation of mental rotation tasks 2.3.2 EEG monitoring of vigil and fatigue states during Laparoscopic tasks 2.3.3 Environmental noise at library learning commons affects electrophysiological functioning 2.3.4 Do user-centered designed paradigms for BCIs improve the modulation of EEG signals? 2.3.5 How effective are acoustic therapies for tinnitus? A psychometric and neurophysiological evaluation 3. Applications of Machine Learning Classifiers in Epileptic Seizure Detection 3.1 Introduction 3.1.1 Impact of epilepsy 3.1.2 Phases of seizure 3.1.3 Seizure types 3.1.3.1 Partial seizure 3.1.3.2 Generalized seizure 3.1.4 Lobes of brain 3.2 Epileptic seizure monitoring tool 3.2.1 Electroencephalography 3.2.1.1 10–20 international system 3.2.2 Electrocorticography 3.3 Machine learning methods 3.3.1 Supervised learning methods 3.3.1.1 Decision tree 3.3.1.2 Decision forest 3.3.1.3 Random forest 3.3.1.4 Support vector machine 3.3.1.5 K-Nearest Neighbour 3.3.1.6 Artificial neural network 3.3.2 Unsupervised learning methods 3.3.2.1 Clustering 3.3.2.2 k-means 3.3.2.3 Genetic clustering 3.4 Epileptic seizure detection 3.4.1 Onset of seizure detection 3.4.2 Quick seizure detection 3.4.3 Seizure detection on class imbalance EEG dataset 3.4.4 Seizure localization 3.4.5 Challenges in seizure detection 3.5 Case study 3.5.1 Child hospital Boston-Massachusetts Institute of Technology dataset 3.6 Conclusions 4. Simultaneous Evaluation of Children Epileptic Encephalopathies with Long-Term EEG via Space-Time Dynamic Entropies 4.1 Introduction 4.1.1 The Doose syndrome 4.1.2 The Lennox-Gastaut syndrome 4.1.3 Raison d’être 4.2 Methods: subjects and metrics for DS and LGS 4.2.1 Conditions 4.2.2 Subjects 4.2.3 Antiepileptic drugs (AED) prescribed to the subjects 4.2.4 Patient A 4.2.5 Patient B 4.2.6 Patient C 4.2.7 Patient D 4.2.8 Patient E 4.2.9 EEG general information 4.2.10 Entropy metrics 4.2.11 Modified MSE basic algorithm 4.2.12 Modified MSE algorithm 4.2.13 MSE index 4.2.14 BMSE and BMSE index: three dimensional complexity information 4.2.15 Dynamic MSE index: dynamic complexity plots (complexity movie) 4.2.16 Dynamic complexity parameters: DBMSE, μ, κ 4.3 Results: Entropy paths 4.4 Conclusions 5. Mobile and Home Electroencephalography in the Usual Environment of Children 5.1 History of telemetry in electroencephalograms (EEGs) 5.2 Neurophysiological basis of EEG 5.3 Wireless and wearable devices for mobile EEG 5.3.1 Signal aquisition: Non invasive electrodes 5.3.2 Signal acquisition and registration 5.3.3 Automated software for mobile EEG interpretation 5.3.4 Mobile EEG advantages and disadvantages 5.3.5 Mobile EEG for continuous epilepsy monitoring 5.3.6 Mobile EEG for nonepileptic attacks 5.3.7 Mobile EEG for neurodevelopmental screening 5.4 Future trends towards mobility 5.5 Conclusion 6. Health: Human-Machine Interaction, Medical Robotics, Patient Rehabilitation 6.1 Introduction 6.2 Related work 6.2.1 Electric wheelchair and assistive navigation devices 6.2.2 Electric wheelchairs as health monitoring enablers 6.2.2.1 Head movements and health monitoring 6.2.2.2 Voice signal processing for health monitoring 6.2.2.3 EOG as health monitoring 6.2.3 Serious games as an assistive tool in HMI 6.3 Proposal 6.3.1 Assistive navigation device and posture monitoring system 6.3.1.1 Obstacle avoidance navigation system 6.3.1.2 Posture monitoring system 6.3.2 Health monitoring enablers 6.3.2.1 Head movement interface 6.3.2.2 Voice command interface 6.3.2.3 EOG interface 6.3.3 Serious games 6.4 Results 6.4.1 Intelligent electric wheelchair in a COVID-19 context 6.5 Conclusions 6.6 Glossary 7. APSoC-Based Implementation of an EEG Classifier using Chaotic Descriptors 7.1 Introduction 7.2 Basis for EEG measurements 7.3 Feature extraction and chaotic descriptors 7.3.1 Attractor reconstruction 7.3.2 Average Mutual Information (AMI) 7.3.3 False Nearest Neighbors (FNN) 7.3.4 Correlation dimension (Dg) 7.3.5 Largest Lyapunov exponent (λ) 7.3.6 Hurst exponent: H 7.4 Support Vector Machines (SVM) 7.5 Embedded classifier 7.5.1 Model-based design 7.5.2 Zynq-7000 APSoC 7.5.3 Classifier model 7.5.4 Strategy of the proposed classification 7.6 Case study 7.6.1 Preprocessing 7.6.2 Postprocessing 7.6.3 Processing results 7.6.4 Classification results 7.7 Discussion Bibliography Index