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. DOI https://doi.org/10.1109/TAFFC.2018.2801811
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. DOI https://doi.org/10.1109/TAFFC.2018.2801811
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. DOI https://doi.org/10.1109/TAFFC.2018.2801811
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.
Item Type: | Article |
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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: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 31 Jan 2018 12:56 |
Last Modified: | 30 Oct 2024 17:37 |
URI: | http://repository.essex.ac.uk/id/eprint/21304 |
Available files
Filename: draft_5-0.pdf