Signal Processing and Machine Learning for Brain-Machine Interfaces

book image

IET Digital Library

This title is available electronically through the IET Digital Library

  • Book title: Signal Processing and Machine Learning for Brain-Machine Interfaces

  • Author:

  • Year: 2018

  • Format: Hardback

  • Product Code: PBCE1140

  • ISBN: 978-1-78561-398-2

  • Pagination: 360pp

  • Stock Status: In stock

£78.00 Member price

£120.00 Full price


Brain-machine interfacing or brain-computer interfacing (BMI/BCI) is an emerging and challenging technology used in engineering and neuroscience. The ultimate goal is to provide a pathway from the brain to the external world via mapping, assisting, augmenting or repairing human cognitive or sensory-motor functions.

In this book an international panel of experts introduce signal processing and machine learning techniques for BMI/BCI and outline their practical and future applications in neuroscience, medicine, and rehabilitation, with a focus on EEG-based BMI/BCI methods and technologies. Topics covered include discriminative learning of connectivity pattern of EEG; feature extraction from EEG recordings; EEG signal processing; transfer learning algorithms in BCI; convolutional neural networks for event-related potential detection; spatial filtering techniques for improving individual template-based SSVEP detection; feature extraction and classification algorithms for image RSVP based BCI; decoding music perception and imagination using deep learning techniques; neurofeedback Games using EEG-based Brain-Computer Interface Technology; Affective Computing System, and more.

About the Editors

Toshihisa Tanaka is an Associate Professor at the Department of Electrical and Electronic Engineering of Tokyo University of Agriculture and Technology. He is Co-editor of Signal Processing Techniques for Knowledge Extraction and Information Fusion, and Associate Editor of IEEE Transactions on Neural Networks and Learning Systems, Computational Intelligence and Neuroscience, and Advances in Data Science and Adaptive Analysis. He is also a member-at-large of the board of governors of Asia-Pacific Signal and Information Processing Association (APSIPA), a senior member of the IEEE, and a member of IEICE and APSIPA.

Mahnaz Arvaneh is a Lecturer in the Department of Automatic Control and Systems Engineering and a member of Centre for Assistive Technology and Connected Health (CATCH) at the University of Sheffield, UK. She is an Associate Editor in IEEE Transaction on Neural Systems and Rehabilitation Engineering, as well as technical committee member for APSIPA and the IEEE Systems, Man, Cybernetics conference. Through her research, she aims to improve our understanding of the human body, both to address fundamental questions in the control of physiological systems and to develop improved therapeutic, assistive, adaptive and rehabilitative technologies for a variety of medical conditions.

Book readership

This book is ideal for researchers, engineers, professionals and specialists in signal processing, computer science, biomedical engineering, computational engineering and machine learning, imaging, neural networks and control engineering who need to gain fundamental and cutting edge knowledge in the fields of BMI/BCI, as well as advanced students and young researchers who are planning to study and/or enter this field.

It is also for neuroscientists who do not have signal processing and machine learning background and need to know about BMI/BCI concepts and technologies.

Book contents

View table of contents


Powered by Google
Search the full text of this book