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

Daly, Ian; Williams, Duncan; Malik, Asad; Weaver, James; Kirke, Alexis; Hwang, Faustina; Miranda, Eduardo Reck; Nasuto, Slawomir J.

doi:10.1109/taffc.2018.2801811

Cited by 19 publications

(9 citation statements)

References 59 publications

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“… Daly et al (2016) developed an affective brain-computer music interfacing that can regulate users’ emotions, which has the potential to be used in music therapy and entertainment. Daly et al (2020) further proposed a personalized method that could be used for BCMI emotional state detection. Compared with population-based detection method, the accuracy of personalized emotion detection was significantly improved.…”

Section: Resultsmentioning

confidence: 99%

Research on the Emotions Based on Brain-Computer Technology: A Bibliometric Analysis and Research Agenda

et al. 2021

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This study conducts a scientific analysis of 249 literature on the application of brain-computer technology in emotion research. We find that existing researches mainly focus on engineering, computer science, neurosciences neurology and psychology. PR China, United States, and Germany have the largest number of publications. Authors can be divided into four groups: real-time functional magnetic resonance imaging (rtfMRI) research group, brain-computer interface (BCI) impact factors analysis group, brain-computer music interfacing (BCMI) group, and user status research group. Clustering results can be divided into five categories, including external stimulus and event-related potential (ERP), electroencephalography (EEG), and information collection, support vector machine (SVM) and information processing, deep learning and emotion recognition, neurofeedback, and self-regulation. Based on prior researches, this study points out that individual differences, privacy risk, the extended study of BCI application scenarios and others deserve further research.

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Section: Resultsmentioning

confidence: 99%

Research on the Emotions Based on Brain-Computer Technology: A Bibliometric Analysis and Research Agenda

et al. 2021

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“…In the future we hope to build on our research exploring how sound may be used to modify the affective state of an individual [18], as well as our research which showed how state-of-the-art affective computing technology may be used to dynamically modulate sound and music over time to optimise an "affective trajectory" (a change in felt affect in an audience over time) [19], [20]. However, one of the most exciting aspects of this work is the set of questions it has raised for future investigation.…”

Section: Discussionmentioning

confidence: 99%

Neuro-curation

Williams

Daly

2021

Audio Mostly 2021

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For the past several years, museums have widely embraced virtual exhibits-certainly before COVID-19, but especially after the virus's outbreak, which has required cultural institutions to temporarily close their physical sites to audiences. Indeed, even once these institutions reopen and the world returns to a new normal, virtual exhibits will remain a defining feature of museums: partly as a means to expand audiences, and partly as a way to increase revenue generation. This paper describes a case study in which a variety of soundscapes were presented accompanying a number of VR objects from the British Museum, in order to determine whether there was any appreciable improvement in viewer engagement with different types of soundscape. Soundscapes were created using synthesis, combinations of foley style effects, spoken word narration, and musique concrete based on a palette drawn from the International Affective Sounds Database. Participants (N=95) were asked to rate their engagement in an online experiment -engagement was highest in the foley-style soundscape condition. This work has implications for future targeted soundscape design, in order to target individual engagement and to facilitate exhibit evaluation, a field we describe as "neuro-curation".

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“…Most of the works of this group propose the development of interfaces for monitoring and modulating mental and affective states through music (LingHu & Shu, 2018;Mideska et al, 2016;Shan et al, 2018;Daly et al, 2016;S. K. Ehrlich et al, 2019;Kobayashi & Fujishiro, 2016;Daly et al, 2020). In Desai et al (2018)'s work they present an approach in the opposite direction.…”

Section: Non-invasive Assistive Solutions Based On Human-machine Musical Interfacesmentioning

confidence: 99%

Affective computing in the context of music therapy: a systematic review

Santana

Lima

Torcate

et al. 2021

RSD

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Music therapy is an effective tool to slow down the progress of dementia since interaction with music may evoke emotions that stimulates brain areas responsible for memory. This therapy is most successful when therapists provide adequate and personalized stimuli for each patient. This personalization is often hard. Thus, Artificial Intelligence (AI) methods may help in this task. This paper brings a systematic review of the literature in the field of affective computing in the context of music therapy. We particularly aim to assess AI methods to perform automatic emotion recognition applied to Human-Machine Musical Interfaces (HMMI). To perform the review, we conducted an automatic search in five of the main scientific databases on the fields of intelligent computing, engineering, and medicine. We search all papers released from 2016 and 2020, whose metadata, title or abstract contains the terms defined in the search string. The systematic review protocol resulted in the inclusion of 144 works from the 290 publications returned from the search. Through this review of the state-of-the-art, it was possible to list the current challenges in the automatic recognition of emotions. It was also possible to realize the potential of automatic emotion recognition to build non-invasive assistive solutions based on human-machine musical interfaces, as well as the artificial intelligence techniques in use in emotion recognition from multimodality data. Thus, machine learning for recognition of emotions from different data sources can be an important approach to optimize the clinical goals to be achieved through music therapy.

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

Cited by 19 publications

References 59 publications

Research on the Emotions Based on Brain-Computer Technology: A Bibliometric Analysis and Research Agenda

Research on the Emotions Based on Brain-Computer Technology: A Bibliometric Analysis and Research Agenda

Neuro-curation

Affective computing in the context of music therapy: a systematic review

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