Signal processing and machine learning for brain-machine interfaces / edited by Toshihisa Tanaka and Mahnaz Arvaneh.

Format:

Book

Contributor:

Arvaneh, Mahnaz, editor.

Tanaka, Toshihisa, editor.

Series:

IET control, robotics and sensors series ; 114.

IET Control, Robotics and Sensors series ; 114

Language:

English

Subjects (All):

Brain-computer interfaces.

Decoders (Electronics).

Electroencephalography.

Medical technology.

Signal processing.

Physical Description:

1 online resource (xiv, 340 pages) : illustrations.

Place of Publication:

London, United Kingdom : Institution of Engineering and Technology, 2018.

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.

Notes:

Includes bibliographical references and index.

Description based on print version record.

ISBN:

1-83724-811-7

1-5231-1983-7

1-78561-399-5