Signal Processing and Machine Learning for Brain-Machine Interfaces
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.

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Signal Processing and Machine Learning for Brain-Machine Interfaces
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.

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Signal Processing and Machine Learning for Brain-Machine Interfaces

Signal Processing and Machine Learning for Brain-Machine Interfaces

Signal Processing and Machine Learning for Brain-Machine Interfaces

Signal Processing and Machine Learning for Brain-Machine Interfaces

Overview

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.

Product Details

ISBN-13: 9781785613982
Publisher: The Institution of Engineering and Technology
Publication date: 11/18/2018
Series: Control, Robotics and Sensors
Pages: 360
Product dimensions: 6.14(w) x 9.21(h) x (d)

About the Author

Toshihisa Tanaka is an Associate Professor at the Department of Electrical and Electronic Engineering of Tokyo Universityof 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 Universityof 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.

Table of Contents

  • 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
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