Signal processing and machine learning for brain-machine interfaces /

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Bibliographic Details
Imprint:Stevenage, United Kingdom : Institution of Engineering and Technology, 2018.
©2018
Description:1 online resource : illustrations
Language:English
Series:IET Control, Robotics and Sensors series ; 114
IET control, robotics and sensors series ; 114.
Subject:
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/12020626
Hidden Bibliographic Details
Other authors / contributors:Tanaka, Toshihisa (Engineer), editor.
Arvaneh, Mahnaz, editor.
ISBN:9781785613999
1785613995
9781523119837
1523119837
9781785613982
1785613987
Notes:Includes bibliographical references and index.
Print version record.
Summary: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.
Other form:Print version: Signal processing and machine learning for brain-machine interfaces. London, United Kingdom : Institution of Engineering and Technology, 2018 1785613987