Full Description
Biometrics provide quantitative representations of human features, physiological and behavioral. This book is a compilation of biometric technologies developed by various research groups in Tecnologico de Monterrey, Mexico. It provides a summary of biometric systems as a whole, explaining the principles behind physiological and behavioral biometrics and exploring different types of commercial and experimental technologies and current and future applications in the fields of security, military, criminology, healthcare education, business, and marketing.
Examples of biometric systems using brain signals or electroencephalography (EEG) are given. Mobile and home EEG use in children's natural environments is covered. At the same time, some examples focus on the relevance of such technology in monitoring epileptic encephalopathies in children.
Using reliable physiological signal acquisition techniques, functional Human Machine Interfaces (HMI) and Brain-Computer Interfaces (BCI) become possible. This is the case of an HMI used for assistive navigation systems, controlled via voice commands, head, and eye movements. A detailed description of the BCI framework is presented, and applications of user-centered BCIs, oriented towards rehabilitation, human performance, and treatment monitoring are explored.
Massive data acquisition also plays an essential role in the evolution of biometric systems. Machine learning, deep learning, and Artificial Intelligence (AI) are crucial allies here. They allow the construction of models that can aid in early diagnosis, seizure detection, and data-centered medical decisions. Such techniques will eventually lead to a more concise understanding of humans.
Contents
1. Current and Future Biometrics: Technology and Applications 2. Analysis of Electrophysiological Activity of the Nervous System: Towards Neural Engineering Applications 3. Applications of Machine Learning Classifiers in Epileptic Seizure Detection 4. Simultaneous Evaluation of Children Epileptic Encephalopathies with Long-Term EEG via Space-Time Dynamic Entropies 5. Mobile and Home Electroencephalography in the Usual Environment of Children 6. Health: Human-Machine Interaction, Medical Robotics, Patient Rehabilitation 7. APSoC-Based Implementation of an EEG Classifier using Chaotic Descriptors
