Personalised, multi-modal, affective state detection for hybrid brain-computer music interfacing

Daly, I; Williams, DAH; Malik, A; Weaver, J; Kirke, A; Hwang, F; Miranda, E; Nasuto, SJ

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.

• Journal Article Type: Article
• Online Publication Date: Feb 7, 2018
• Publication Date: Mar 1, 2020
• Deposit Date: Dec 12, 2019
• Journal: IEEE Transactions on Affective Computing
• Print ISSN: 1949-3045
• Publisher: Institute of Electrical and Electronics Engineers
• Volume: 11
• Issue: 1
• Pages: 111-124
• DOI: https://doi.org/10.1109/taffc.2018.2801811
• Publisher URL: https://doi.org/10.1109/taffc.2018.2801811
• Related Public URLs: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=5165369