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.

Author:

Toshihisa Tanaka, Mahnaz Arvaneh, Mahnaz Arvaneh

Publisher:

The Institution Of Engineering And Technology

Publication Date:

Nov 18, 2018

Number of pages:

355 pages

Language:

English

Binding:

Hardcover

ISBN-10:

1785613987

ISBN-13:

9781785613982