@article {2021,
	title = {Music, Computing, and Health: A roadmap for the current and future roles of music technology for health care and well-being},
	journal = {Music \& Science},
	year = {2021},
	abstract = {The fields of music, health, and technology have seen significant interactions in recent years in
developing music technology for health care and well-being. In an effort to strengthen the
collaboration between the involved disciplines, the workshop {\textquoteleft}Music, Computing, and Health{\textquoteright} was
held to discuss best practices and state-of-the-art at the intersection of these areas with
researchers from music psychology and neuroscience, music therapy, music information retrieval,
music technology, medical technology (medtech) and robotics. Following the discussions at the
workshop, this paper provides an overview of the different methods of the involved disciplines
and their potential contributions to developing music technology for health and well-being.
Furthermore, the paper summarizes the state of the art in music technology that can be applied in
various health scenarios and provides a perspective on challenges and opportunities for
developing music technology that 1) supports person-centered care and evidence-based
treatments, and 2) contributes to developing standardized, large-scale research on music-based
interventions in an interdisciplinary manner. The paper provides a resource for those seeking to
engage in interdisciplinary research using music-based computational methods to develop
technology for health care, and aims to inspire future research directions by evaluating the state
of the art with respect to the challenges facing each field.},
	keywords = {Computing, Health, Music Information Retrieval, Music Psychology, Music Therapy, Roadmap, Technology},
	doi = {https://doi.org/10.1177/2059204321997709},
	author = {Agres, Kat R and Schaefer, Rebecca and Volk, Anja and van Hooren, Susan and Holzapfel, Andre and DallaBella, Simone and Müller, Meinard and de Witte, Martina and Herremans, Dorien and Ramirez-Melendez, Rafael and Neerincx, Mark and Ruiz, Sebastian and Meredith, David and Dimitriadis, Theo and Magee, Wendy}
}
@proceedings {2019,
	title = {A Comparative Study of Mental States in 2D and 3D Virtual Environments Using EEG},
	year = {2019},
	month = {10/2019},
	publisher = {IEEE},
	address = {Bari, Italy},
	abstract = {There is growing evidence that virtual reality (VR) can be used as an optional therapy method for stress relief and treatment of mental disorders such as a variety of anxiety disorders, depression and psychosis. However, more systematic studies to quantify the effects of VR for emotion elicitation and compare the effects of 3D and 2D environment for the same is necessary to design feasible and effective treatments. In this study, we design a cross-over experiment protocol comprising of two emotion eliciting environments relaxation and arousal - that is presented to the participant either in a 2D monitor or a 3D head-mounted (HMD) VR display. The EEG data is collected during the experiment and analyzed offline to classify emotion elicited by low and high arousal environments using SVM classification of band power features. A 10-fold cross validation is performed and classification accuracy for each subject is computed for 3D-VR and 2D-screen display. The average classification accuracies over subjects are obtained as 66.88\% and 59.27\% in 3D-VR and 2D-screen group respectively. The performance difference between 3D-VR and 2D screen is statistically significant (p < 0.05), indicating that 3D-VR generates more distinct EEG patterns associated with emotion elicitation. Further, significant differences in band powers from alpha, theta and beta bands are also observed between both groups. The results presented in this paper support the use of VR as an effective tool to study emotion elicitation and regulation, compared to a 2D screen.},
	author = {Pinar Bilgin and Kat Agres and Neethu Robinson and Aung Aung Phyo Wai and Cuntai Guan}
}
@article {2019,
	title = {Re-Representing Metaphor: Modeling Metaphor Perception Using Dynamically Contextual Distributional Semantics},
	journal = {Frontiers in Psychology: Cognitive Science},
	volume = {10},
	year = {2019},
	month = {04/2019},
	pages = {1-18},
	type = {Original Research},
	abstract = {In this paper, we present a novel context-dependent approach to modeling word meaning, and apply it to the modeling of metaphor. In distributional semantic
approaches, words are represented as points in a high dimensional space generated from co-occurrence statistics; the distances between points may then be used to quantifying semantic relationships. Contrary to other approaches which use static, global representations, our approach discovers contextualized representations by dynamically projecting low-dimensional subspaces; in these ad hoc spaces, words can be re-represented in an open-ended assortment of geometrical and conceptual configurations as appropriate for particular contexts. We hypothesize that this context-specific re-representation enables a more effective model of the semantics of metaphor than standard static approaches. We test this hypothesis on a dataset of English word dyads rated for degrees of metaphoricity, meaningfulness, and familiarity by human participants. We demonstrate that our model captures these ratings more effectively than a state-of-the-art static model, and does so via the amount of contextualizing work inherent in the re-representational process.},
	keywords = {computational creativity, computational linguistics, conceptual models, distributional semantics, metaphor, vector space models},
	doi = {doi: 10.3389/fpsyg.2019.00765},
	author = {Stephen McGregor and Kat Agres and Karolina Rataj and Matthew Purver and Geraint Wiggins}
}
@article {2019,
	title = {Statistical Learning Ability as a Measure of Cognitive Function},
	year = {2019},
	month = {07/2019},
	pages = {1-7},
	publisher = {PsyArXiv},
	type = {Archive},
	keywords = {Auditory Domain, Cognitive Assessment, Continuous Paradigm, Elderly, Statistical Learning},
	doi = {doi.org/10.31234/osf.io/u4ry6},
	author = {Herff, S and Rashid, N.A and Lee, J and Lee, T.S and Agres, K}
}
@proceedings {2016,
	title = {Modeling metaphor perception with distributional semantics vector space models},
	year = {2016},
	month = {08/2016},
	abstract = {In this paper, we present a novel application of a computational model of word meaning to capture human judgments of the linguistic properties of metaphoricity, familiarity, and meaningfulness. We present data gathered from human subjects regarding their ratings of these properties over a set of word pairs specifically designed to exhibit varying degrees of metaphoricity. We then investigate whether these properties can be measured in terms of geometric features of a model of distributional lexical semantics. We compare the performance of two models, our own Concept Discovery Model which dynamically constructs context-sensitive subspaces, and a state-of-the-art static distributional semantic model, and find that our dynamic model performs significantly better in its measurement of metaphoricity.},
	keywords = {computational creativity, distributional semantics, metaphor, vector space models},
	author = {Kat R. Agres and Stephen McGregor and Karolina Rataj and Matthew Purver and Geraint A. Wiggins}
}
