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.
Inspec keywords: neurophysiology; brain-computer interfaces; medical robotics; virtual reality; medical computing
Other keywords: augmented reality; neuroimaging; biomedical signal processing; virtual reality; mobile apps; EEG-based biometric systems; neurorobotics; brain-computer interfaces
Subjects: Virtual reality; Biophysics of neurophysiological processes; Biological and medical control systems; Biology and medical computing; User interfaces
This book is comprised of ten (including this introduction) highly thematic chapters on the various aspects of neurotechnology. In each of the remaining chapters, an expert scientist or a group of experts, focus on a central advanced theme of each field as a vantage point, in order to explore those previously mentioned aspects.
Modern neuroscientific approaches aim to detect the neurophysiological patterns associated with pathological conditions as early as possible with great sensitivity and specificity. Emerging evidence suggests that quantification of the functional brain interactions through a dynamic temporal manner may contribute to the diagnosis of several diseases such as psychiatric disturbances, neurodegeneration and sleep disorders. So, it is important to provide robust computational frameworks based on interdisciplinary concepts stemming from neuroscience, medicine, mathematics as well as computer science.
Neuroimaging has facilitated major advances in understanding the human brain, and provides invaluable guidance for diagnosis and therapy of disease. This chapter provides a broad overview of human in-vivo neuroimaging techniques, and introduces algorithms for neuroimage processing and analysis. Covered modalities include structural, functional, and diffusion MRI (dMRI), positron emission tomography (PET), and magnetoencephalography (MEG). We discuss state-of-the-art algorithms for image registration and segmentation, for statistical analysis, and for predictive modeling.
Neuroscience encompasses the interpretive layers of a person with the environment, implemented as the central and peripheral nervous systems. In neuroscientific research, normal and abnormal neurophysiological function is studied for diagnosis, or altered through treatment by affecting paired or evoked responses from environmental sensory input. With the advent of virtual reality (VR) and augmented reality (AR), as human-computer interaction (HCI) modalities of highly immersiveness and malleability, a new avenue for previously unfeasible environmental input has been realized. This chapter explores the potential of VR/AR in the axes that these modalities are currently being deployed in neuroscience. Virtual reality environments (VREs) are implemented as alternative customizable platforms for brain-computer interface (BCI) applications but are also exploited in neurorehabilitation paradigms. In medical neurosciences, VR/ AR have proven added value in the visualization of the nervous system both for operative enhancement and for surgical training. Additionally, in medical education there is a strong case to be made in favor of the “virtual lab” (VL) for neuroanatomy offering added value to teaching of the complexity of the neural structures and networks. Furthermore, the controllability of VREs and VR/AR render them excellent candidates for studying cognition through event related potentials (ERPs). Finally, we explore the combined role of VR/AR and affect in educational and internal learning processes, aiming toward an integrated sensor immersion ecosystem.
This chapter presented a detailed review about EEG -based authentication/identification systems. In comparison to other neuroimaging techniques, the EEG data bring numerous advantages, such as relatively simple and inexpensive equipment for signals acquisition and the possibility to be used in most environments. A large number of studies found in the state-of-the-art were approached and analyzed in this work. More than 190 papers were analyzed in detail in order to present the main methodologies used by the authors over the last few years. The main datasets and their characteristics were also exposed, such as the number of electrodes, sampling rate, number of subjects, and filtering techniques. The feature extraction and classification techniques of the related works demonstrated high accuracy rates at the same time that the number of the channels is getting reduced and the signals acquisition process is getting less complicated.
In the last decade, a real revolution in the field of brain -computer interfaces (BCI) led from the "overt" detection of human intention to the "covert" assessment of the actual human mental states. While the first aspect is the basis of the traditional BCI systems, the latter represents the outcome of the passive BCI applications. In fact, passive BCI derives its outputs from brain activity arising without the purpose of voluntary control, but implicitly related to the human mental state. The necessity of monitoring human mental states driven by safety -critical application has been just the boost to the passive BCIs developing: more in general, passive BCI represents the implicit channel of information that enhances the goal -oriented cooperation of humans and machines as a whole, the so-called human -machine interaction. So far, there have been countless passive BCI applications in a wide range of contexts such as driving, gaming, and surgery. If on the one hand, this has been possible thanks to the development of more and more discrete neurotechnological devices, on the other hand, we must not overlook the significant step forward in the employed algorithms, with the adoption in this field of machine learning and deep learning enhancements. This chapter will retrace not only the major achievements but also the future trends, in terms of technologies, methods, and applications of what concerns the field of passive BCIs. The final aim of the work is to draw a mark on where we are nowadays and the future challenges, in order to make passive BCIs closer to being integrated into day -life applications.
Neurorobotics is an interdisciplinary scientific field which focuses on embodied neural systems and spans scientific theory, research, development and clinical medical practice. It involves a variety of science and engineering disciplines, most frequently electronic, mechanical and control engineering (sometimes alternatively described as electrical, automation, computer or automation engineering), informatics, computer science, software engineering and artificial intelligence (Al), while in the faculty of health sciences it is often associated with neuroscience, neurophysiology and neurosurgery.
This book chapter will provide the reader with an overview of recent developments in mobile device technologies and development opportunities, focusing on iOS and Android. It will offer an overview of scientific evidence regarding mobile clinical decision support systems and elaborate on upcoming areas for innovation such as machine learning (ML) and augmented reality (AR).
Schooling has changed surprisingly little since its origins 5 millennia ago, despite efforts toward massification and inclusion. Research on a wide arch of fields indicates that schools must undergo major transformation in order to benefit from the current science of learning. This chapter outlines some of the seemingly utopic main kernels of this necessary transformation. The school of the future will fully embrace the physiological aspects of learning, and strive to optimize sleep, nutrition, and exercise for children and adults of all ages, so as to improve intrapersonal and interpersonal relationships. Learning will have computer games as scaffold for the crystallization the link between phonemes and graphemes during literacy acquisition, and for the generation of individual learning curves for the personalized tracking of performance. The retention of academic contents will be greatly improved by frequent cumulative tests and other opportunities for retrieval practice, and the use of novelty as an extrinsic adjuvant of learning will be emphasized.
