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Personalised, Multi-modal, Affective State Detection for Hybrid Brain-Computer Music Interfacing

Daly, Ian and Williams, D and Malik, A and Weaver, J and Kirke, A and Hwang, F and Miranda, E and Nasuto, SJ (2020) 'Personalised, Multi-modal, Affective State Detection for Hybrid Brain-Computer Music Interfacing.' IEEE Transactions on Affective Computing, 11 (1). pp. 111-124. ISSN 1949-3045

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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.

Item Type: Article
Uncontrolled Keywords: EEG, GSR, Affective state detection, BCMI, Personalised affective state detection
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 31 Jan 2018 12:56
Last Modified: 23 Sep 2022 19:22

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