Toward personalized music-therapy: a neurocomputational modeling perspective

Lai-Tan, N., Philiastides, M. G. , Kawsar, F. and Deligianni, F. (2023) Toward personalized music-therapy: a neurocomputational modeling perspective. IEEE Pervasive Computing, (doi: 10.1109/MPRV.2023.3285087) (Early Online Publication)

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Music therapy has emerged recently as a successful intervention that improves patient outcomes in a large range of neurological and mood disorders without adverse effects. Brain networks are entrained to music in ways that can be explained both via top-down and bottom-up processes. In particular, the direct interaction of auditory with the motor and the reward system via a predictive framework explains the efficacy of music-based interventions in motor rehabilitation. In this article, we provide a brief overview of current theories of music perception and processing. Subsequently, we summarize the evidence of music-based interventions primarily in motor, emotional, and cardiovascular regulation. We highlight opportunities to improve the quality of life and reduce the stress beyond the clinic environment and in healthy individuals. This relatively unexplored area requires an understanding of how we can personalize and automate music selection processes to fit individual needs and tasks via feedback loops mediated by measurements of neurophysiological responses.

Item Type:Articles
Status:Early Online Publication
Glasgow Author(s) Enlighten ID:Kawsar, Professor Fahim and Philiastides, Professor Marios and Deligianni, Dr Fani and Lai, Nicole
Authors: Lai-Tan, N., Philiastides, M. G., Kawsar, F., and Deligianni, F.
College/School:College of Medical Veterinary and Life Sciences > School of Psychology & Neuroscience
College of Science and Engineering > School of Computing Science
Journal Name:IEEE Pervasive Computing
ISSN (Online):1558-2590
Published Online:30 June 2023
Copyright Holders:Copyright © 2023 IEEE
First Published:First published in IEEE Pervasive Computing 2023
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
315206Privacy-Preserved Human Motion Analysis for Healthcare ApplicationsFani DeligianniEngineering and Physical Sciences Research Council (EPSRC)EP/W01212X/1Computing Science
315626Privacy-preserved Human Motion AnalysisFani DeligianniThe Royal Society (ROYSOC)RGS\R2\212199Computing Science
303764EPSRC CDT - Socially Intelligent Artificial AgentsAlessandro VinciarelliEngineering and Physical Sciences Research Council (EPSRC)EP/S02266X/1Computing Science