@article{Al-Taie2022,

title = {Sonic Enhancement of Virtual Exhibits},

author = {Inas Al-Taie and Paola Di Giuseppantonio Di Franco and Michael Tymkiw and Duncan Williams and Ian Daly},

year  = {2022},

date = {2022-08-11},

journal = {PLoS One},

keywords = {Affective computing, Art, Music generation, Soundscaping},

pubstate = {published},

tppubtype = {article}

}

@article{Daly2021Art,

title = {Removal of physiological artifacts from simultaneous EEG and fMRI recordings},

author = {Ian Daly},

year  = {2021},

date = {2021-06-01},

journal = {Clinical Neurophysiology},

keywords = {Affective computing, Artefact removal, Classification, EEG, fMRI},

pubstate = {published},

tppubtype = {article}

}

@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,

title = {Mathematical mindsets increase student motivation: Evidence from the EEG},

author = {Ian Daly and Jake Bourgaize and Alexei Vernitski},

year  = {2019},

date = {2019-04-11},

journal = {Trends in Neuroscience and Education},

keywords = {Affective computing, Education, EEG, Emotion, Mathematical mindsets},

pubstate = {published},

tppubtype = {article}

}

@article{Daly2017b,

title = {Personalised, Multi-modal, Affective State Detection for Hybrid Brain-Computer Music Interfacing},

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

year  = {2017},

date = {2017-10-08},

journal = {IEEE Transactions on Affective Computing},

abstract = {Brain-computer music interfaces (BCMIs) may be used to modulate affective states, with applications in music therapy, composition, and entertainment. However, for such systems to work they need to be able to reliably detect their user’s current affective state.

We present a method for personalised affective state detection for use in BCMI. We compare it to a population-based detection method trained on 17 users and demonstrate that personalised affective state detection is significantly (p < 0:01) more accurate, with average improvements in accuracy of 10.2% for valence and 9.3% for arousal. We also compare a hybrid BCMI (a BCMI that combines physiological signals with neurological signals) to a conventional BCMI design 

 one based upon the use of only EEG features) and demonstrate that the hybrid design results in a significant (p < 0:01) 6.2% improvement in performance for arousal classification and a significant (p < 0:01) 5.9% improvement for valence classification.},

keywords = {Affective computing, BCI, Classification, Feature selection, Machine learning},

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}

}