Why Eeg Of Brain Is Done
Download Why Eeg Of Brain Is Done PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Why Eeg Of Brain Is Done book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Approaches and Applications of Deep Learning in Virtual Medical Care
The recent advancements in the machine learning paradigm have various applications, specifically in the field of medical data analysis. Research has proven the high accuracy of deep learning algorithms, and they have become a standard choice for analyzing medical data, especially medical images, video, and electronic health records. Deep learning methods applied to electronic health records are contributing to understanding the evolution of chronic diseases and predicting the risk of developing those diseases. Approaches and Applications of Deep Learning in Virtual Medical Care considers the applications of deep learning in virtual medical care and delves into complex deep learning algorithms, calibrates models, and improves the predictions of the trained model on medical imaging. Covering topics such as big data and medical sensors, this critical reference source is ideal for researchers, academicians, practitioners, industry professionals, hospital workers, scholars, instructors, and students.
EEG Signal Analysis and Classification
This book presents advanced methodologies in two areas related to electroencephalogram (EEG) signals: detection of epileptic seizures and identification of mental states in brain computer interface (BCI) systems. The proposed methods enable the extraction of this vital information from EEG signals in order to accurately detect abnormalities revealed by the EEG. New methods will relieve the time-consuming and error-prone practices that are currently in use. Common signal processing methodologies include wavelet transformation and Fourier transformation, but these methods are not capable of managing the size of EEG data. Addressing the issue, this book examines new EEG signal analysis approaches with a combination of statistical techniques (e.g. random sampling, optimum allocation) and machine learning methods. The developed methods provide better results than the existing methods. The book also offers applications of the developed methodologies that have been tested on several real-time benchmark databases. This book concludes with thoughts on the future of the field and anticipated research challenges. It gives new direction to the field of analysis and classification of EEG signals through these more efficient methodologies. Researchers and experts will benefit from its suggested improvements to the current computer-aided based diagnostic systems for the precise analysis and management of EEG signals. /div
Mind Over Brain, Brain Over Mind: Cognitive Causes and Consequences of Controlling Brain Activity
Author: Elisabeth V. C. Friedrich
language: en
Publisher: Frontiers Media SA
Release Date: 2015-11-30
This Research Topic combines articles aiming to gain a better understanding on different factors that determine whether people are successful or not in controlling computerized devices with brain signals. Since decades, technological advancements in neuroscience allow the interpretation of brain signals and their translation into control messages (Brain-computer interface (BCI)). Moreover, the control of brain signals can be used to induce changes in cognition and behavior (Neurofeedback (NF)). However, the break-through of this technology for the broad population in real-world applications has not yet arrived. Various factors have been related to the individual success in controlling computerized devices with brain signals, but to date, no general theoretical framework is available. In this Research Topic, aspects of the training protocol such as instructions, task and feedback as well as cognitive and psychological traits such as motivation, mood, locus of control and empathy are investigated as determinants of BCI or NF performance. Moreover, the mechanisms and networks involved in gaining and maintaining control over brain activity as well as its prediction are addressed. Finally, as the ultimate goal of this research is to use BCI and NF for communication or control and therapy, respectively, novel applications for individuals with disabilities or disorders are discussed.