Signal Processing and Machine Learning for Brain-Machine Interfaces


Signal Processing and Machine Learning for Brain-Machine Interfaces (Control, Robotics and Sensors)
by Toshihisa Tanaka and Mahnaz Arvaneh
ISBN-10 书号: 1785613987
ISBN-13 书号: 9781785613982
Release Finelybook 出版日期: 2018-11-25
Pages 页数: (360 )

The Book Description robot was collected from Amazon and arranged by Finelybook

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.

Contents

Preface
1 Brain-computer interfaces and electroencephalogram:basics and practical issues
2Discriminative learning of connectivity pattern of motor imagery EEG
3 An experimental study to compare CSP and TSM techniques to extract features during motor imagery tasks
4Robust EEG signal processing with signal structures
5 A review on transfer learning approaches in brain-computer interface
6 Unsupervised learning for brain-computer interfaces based on event-related potentials
7 Covariate shift detection-based nonstationary adaptation in motor-imagery-based brain-computer interface
8A BCI challenge for the signal-processing community. considering the user in the loop
9 Feedforward artificial neural networks for event-related potential detection
10 Signal models for brain interfaces based on evoked response potential in EEG
11 Spatial filtering techniques for improving individual template-based SSVEP detection
12 A review of feature extraction and classification algorithms for image RSVP-based BCI
13 Decoding music perception and imagination using deep-learning techniques
14 Neurofeedback games using EEG-based brain-computer interface technology Index

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