Signal Processing and Machine Learning for Brain–Machine Interfaces

Signal Processing and Machine Learning for Brain–Machine Interfaces Toshihisa Tanaka, Mahnaz Arvaneh
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. Categories:
Computers – Artificial Intelligence (AI)
Year:
2018
Publisher:
The Institution of Engineering and Technology
Language:
english
Pages:
356
ISBN 10:
1785613987
ISBN 13:
9781785613982
Series:
Control, Robotics and Sensors Series 114
File:
63 MB