About this product

Product Identifiers

PublisherSpringer London, The Limited

ISBN-101447172108

ISBN-139781447172109

eBay Product ID (ePID)5038381266

Product Key Features

Number of PagesXviii, 313 Pages

Publication NameGuide to Brain-Computer Music Interfacing

LanguageEnglish

SubjectNeurology, User Interfaces, General, Genres & Styles / Electronic, Social Aspects / Human-Computer Interaction

Publication Year2016

TypeTextbook

Subject AreaMusic, Computers, Medical

AuthorJulien Castet, Eduardo Reck Miranda

FormatTrade Paperback

Dimensions

Item Weight177.7 Oz

Item Length9.3 in

Item Width6.1 in

Additional Product Features

Dewey Edition23

Number of Volumes1 vol.

IllustratedYes

Dewey Decimal780.285

Table Of ContentBrain-Computer Music Interfacing.- Electroencephalogram-Based Brain-Computer Interface.- Contemporary Approaches to BCMI Using P300 Event Related Potentials.- Prospective View on Sound Synthesis BCI Control in Light of Two Paradigms of Cognitive Neuroscience.- Machine Learning to Identify Neural Correlates of Music and Emotions.- Emotional Responses During Music Listening.- A Tutorial on EEG Signal Processing Techniques for Mental State Recognition in Brain-Computer Interfaces.- An Introduction to EEG Source Analysis with an Illustration of a Study on Error-Related Potentials.- Feature Extraction and Classification of EEG Signals.- On Mapping EEG Information into Music.- Retroaction Between Music and Physiology.- Creative Music Neurotechnology with Symphony of Minds Listening.- Passive Brain-Computer Interfaces

SynopsisThe emergence of more affordable EEG equipment is fostering a renaissance of approaches to making music with brain signals. This Guide to Brain-Computer Music Interfacing (BCMI) presents a world-class collection of BCMI tools with which adventurous explorers may pursue practical and propositional models in music neurotechnology. The text focuses on how these tools enable the extraction of meaningful control information from brain signals, and discusses how to design effective generative music techniques that respond to this information. Topics and features: reviews important techniques for hands-free interaction with computers, including event-related potentials with P300 waves; explores questions of semiotic brain-computer interfacing (BCI), and the use of machine learning to dig into relationships among music and emotions; offers tutorials on signal extraction, brain electric fields, passive BCI, and applications for genetic algorithms, along with historical surveys; describes how BCMI research advocates the importance of better scientific understanding of the brain for its potential impact on musical creativity; presents broad coverage of this emerging, interdisciplinary area, from hard-core EEG analysis to practical musical applications. This unique and pioneering text/reference will appeal to researchers, graduates and advanced undergraduates from a range of different domains within computer science and beyond, such as music technology and biomedical engineering., This book presents a world-class collection of Brain-Computer Music Interfacing (BCMI) tools. The text focuses on how these tools enable the extraction of meaningful control information from brain signals, and discusses how to design effective generative music techniques that respond to this information. Features: reviews important techniques for hands-free interaction with computers, including event-related potentials with P300 waves; explores questions of semiotic brain-computer interfacing (BCI), and the use of machine learning to dig into relationships among music and emotions; offers tutorials on signal extraction, brain electric fields, passive BCI, and applications for genetic algorithms, along with historical surveys; describes how BCMI research advocates the importance of better scientific understanding of the brain for its potential impact on musical creativity; presents broad coverage of this emerging, interdisciplinary area, from hard-core EEG analysis to practical musical applications.

LC Classification NumberQA76.9.U83