@article{Daly2020data,

title = {Neural and physiological data from participants listening to affective music},

author = {Ian Daly and Nicoletta Nicolaou and Duncan Williams and Faustina Hwang and Alexis Kirke and Eduardo Miranda and Slawomir J. Nasuto},

year  = {2020},

date = {2020-05-07},

journal = {Scientific Data},

abstract = {Music provides a means of communicating affective meaning. However, the neurological mechanisms by which music induces affect are not fully understood. Our project sought to investigate this through a series of experiments into how humans react to affective musical stimuli and how physiological and neurological signals recorded from those participants change in accordance with self-reported changes in affect. In this paper, the datasets recorded over the course of this project are presented, including details of the musical stimuli, participant reports of their felt changes in affective states as they listened to the music, and concomitant recordings of physiological and neurological activity. We also include non-identifying meta data on our participant populations for purposes of further exploratory analysis. This data provides a large and valuable novel resource for researchers investigating emotion, music, and how they affect our neural and physiological activity.},

keywords = {Affective composition, Affective computing, BCI, BCMI, Data, EEG, Emotion, fMRI, Music},

pubstate = {published},

tppubtype = {article}

}

@article{Daly2019-fMRI,

title = {Electroencephalography reflects the activity of sub-cortical brain regions during approach-withdrawal behaviour while listening to music},

author = {Ian Daly and Duncan Williams and Faustina Hwang and Alexis Kirke and Eduardo Miranda and Slawomir J. Nasuto},

url = {https://www.nature.com/articles/s41598-019-45105-2},

doi = {https://doi.org/10.1038/s41598-019-45105-2},

year  = {2019},

date = {2019-06-03},

journal = {Scientific Reports},

keywords = {Affective composition, EEG, Emotion, fMRI, Music, Music generation},

pubstate = {published},

tppubtype = {article}

}

@article{Williams2017,

title = { Affective Calibration of Musical Feature Sets in an Emotionally Intelligent Music Composition System},

author = {Duncan Williams and Alexis Kirke and Eduardo Miranda and Ian Daly and Faustina Hwang and James Weaver and Slawomir J. Nasuto},

url = {http://dl.acm.org/citation.cfm?id=3059005},

year  = {2017},

date = {2017-05-22},

journal = {ACM Transactions on Applied Perception (TAP)},

volume = {14},

number = {3},

keywords = {Affective composition, Affective computing, Music, Music generation},

pubstate = {published},

tppubtype = {article}

}

@article{Williams2016,

title = {A Perceptual and Affective Evaluation of an Affectively -Driven Engine for Video Game Soundtracking},

author = {Duncan Williams and Jamie Mears and Alexis Kirke and Eduardo Miranda and Ian Daly and Asad Malik and James Weaver and Faustina Hwang and Slawomir Nasuto},

year  = {2016},

date = {2016-06-29},

journal = {ACM Computers in Entertainment},

keywords = {Affective composition, Affective computing, Emotion, Music generation},

pubstate = {published},

tppubtype = {article}

}

@article{Daly2016aBCMI,

title = {Affective Brain-Computer Music Interfacing},

author = {Ian Daly and Duncan Williams and Alexis Kirke and James Weaver and Asad Malik and Faustina Hwang and Eduardo Miranda and Slawomir J. Nasuto},

year  = {2016},

date = {2016-06-21},

journal = {Journal of Neural Engineering},

volume = {(accepted)},

keywords = {aBCMI, Affective composition, BCI, BCMI, Case based reasoning, EEG, Emotion, Hybrid BCI, Music generation},

pubstate = {published},

tppubtype = {article}

}

@article{WilliamsRD2015,

title = {Investigating Perceived Emotional Correlates of Rhythmic Density in Algorithmic Music Composition},

author = {Duncan Williams and Alexis Kirke and Eduardo Miranda and Ian Daly and James Hallowell and James Weaver and Asad Malik and Etienne Roesch and Faustina Hwang and Slawomir Nasuto},

doi = {10.1145/2749466},

year  = {2015},

date = {2015-07-01},

journal = {ACM Transactions on Applied Perception (TAP)},

volume = {12},

number = {3},

pages = {1-21},

abstract = {Affective algorithmic composition is a growing field that combines perceptually motivated affective computing strategies with novel music generation. This article presents work toward the development of one application. The long-term goal is to develop a responsive and adaptive system for inducing affect that is both controlled and validated by biophysical measures. Literature documenting perceptual responses to music identifies a variety of musical features and possible affective correlations, but perceptual evaluations of these musical features for the purposes of inclusion in a music generation system are not readily available. A discrete feature, rhythmic density (a function of note duration in each musical bar, regardless of tempo), was selected because it was shown to be well-correlated with affective responses in existing literature. A prototype system was then designed to produce controlled degrees of variation in rhythmic density via a transformative algorithm. A two-stage perceptual evaluation of a stimulus set created by this prototype was then undertaken. First, listener responses from a pairwise scaling experiment were analyzed via Multidimensional Scaling Analysis (MDS). The statistical best-fit solution was rotated such that stimuli with the largest range of variation were placed across the horizontal plane in two dimensions. In this orientation, stimuli with deliberate variation in rhythmic density appeared farther from the source material used to generate them than from stimuli generated by random permutation. Second, the same stimulus set was then evaluated according to the order suggested in the rotated two-dimensional solution in a verbal elicitation experiment. A Verbal Protocol Analysis (VPA) found that listener perception of the stimulus set varied in at least two commonly understood emotional descriptors, which might be considered affective correlates of rhythmic density. Thus, these results further corroborate previous studies wherein musical parameters are monitored for changes in emotional expression and that some similarly parameterized control of perceived emotional content in an affective algorithmic composition system can be achieved and provide a methodology for evaluating and including further possible musical features in such a system. Some suggestions regarding the test procedure and analysis techniques are also documented here.},

keywords = {Affective composition, Emotion, Music generation},

pubstate = {published},

tppubtype = {article}

}