@inbook{Daly2014mu,

title = {Machine learning to identify neural correlates of music and emotions},

author = {Ian Daly and Etienne Roesch and James Weaver and Slawomir J. Nasuto},

editor = {Eduardo Reck Miranda, Julien Castet},

url = {http://www.iandaly.co.uk/wp-content/uploads/2016/01/Machine-learning-to-identify-neural-correlates-of-music-and-emotions.pdf},

doi = {10.1007/978-1-4471-6584-2_5},

isbn = {978-1-4471-6583-5},

year  = {2014},

date = {2014-10-04},

pages = {89-103},

publisher = {Springer},

abstract = {While music is widely understood to induce an emotional response in the listener, the exact nature of that response and its neural correlates are not yet fully explored. Furthermore, the large number of features which may be extracted from, and used to describe, neurological data, music stimuli, and emotional responses, means that the relationships between these datasets produced during music listening tasks or the operation of a brain–computer music interface (BCMI) are likely to be complex and multidimensional. As such, they may not be apparent from simple visual inspection of the data alone. Machine learning, which is a field of computer science that aims at extracting information from data, provides an attractive framework for uncovering stable relationships between datasets and has been suggested as a tool by which neural correlates of music and emotion may be revealed. In this chapter, we provide an introduction to the use of machine learning methods for identifying neural correlates of musical perception and emotion. We then provide examples of machine learning methods used to study the complex relationships between neurological activity, musical stimuli, and/or emotional responses.},

keywords = {EEG, Emotion, Machine learning, Models of emotion, Music},

pubstate = {published},

tppubtype = {inbook}

}