Signal Processing and Machine Learning for Brain-Machine Interfaces
Published on 18. November 2018
Book
Hardback
360 pages
978-1-78561-398-2 (ISBN)
Description
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.
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.
More details
Series
Language
English
Place of publication
Stevenage
United Kingdom
Target group
College/higher education
Professional and scholarly
Dimensions
Height: 236 mm
Width: 163 mm
Thickness: 23 mm
Weight
680 gr
ISBN-13
978-1-78561-398-2 (9781785613982)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Persons
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 the 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 a 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.
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 a 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.
Content
Chapter 1: Brain-computer interfaces and electroencephalogram: basics and practical issues
Chapter 2: Discriminative learning of connectivity pattern of motor imagery EEG
Chapter 3: An experimental study to compare CSP and TSM techniques to extract features during motor imagery tasks
Chapter 4: Robust EEG signal processing with signal structures
Chapter 5: A review on transfer learning approaches in brain-computer interface
Chapter 6: Unsupervised learning for brain-computer interfaces based on event-related potentials
Chapter 7: Covariate shift detection-based nonstationary adaptation in motor-imagery-based brain-computer interface
Chapter 8: A BCI challenge for the signal-processing community: considering the user in the loop
Chapter 9: Feedforward artificial neural networks for event-related potential detection
Chapter 10: Signal models for brain interfaces based on evoked response potential in EEG
Chapter 11: Spatial filtering techniques for improving individual template-based SSVEP detection
Chapter 12: A review of feature extraction and classification algorithms for image RSVP-based BCI
Chapter 13: Decoding music perception and imagination using deep-learning techniques
Chapter 14: Neurofeedback games using EEG-based brain-computer interface technology
Chapter 2: Discriminative learning of connectivity pattern of motor imagery EEG
Chapter 3: An experimental study to compare CSP and TSM techniques to extract features during motor imagery tasks
Chapter 4: Robust EEG signal processing with signal structures
Chapter 5: A review on transfer learning approaches in brain-computer interface
Chapter 6: Unsupervised learning for brain-computer interfaces based on event-related potentials
Chapter 7: Covariate shift detection-based nonstationary adaptation in motor-imagery-based brain-computer interface
Chapter 8: A BCI challenge for the signal-processing community: considering the user in the loop
Chapter 9: Feedforward artificial neural networks for event-related potential detection
Chapter 10: Signal models for brain interfaces based on evoked response potential in EEG
Chapter 11: Spatial filtering techniques for improving individual template-based SSVEP detection
Chapter 12: A review of feature extraction and classification algorithms for image RSVP-based BCI
Chapter 13: Decoding music perception and imagination using deep-learning techniques
Chapter 14: Neurofeedback games using EEG-based brain-computer interface technology