Affective Brain-Computer Interfaces for Arts

Gürkök, Hayrettin; Nijholt, Anton

doi:10.1109/acii.2013.155

Cited by 18 publications

(12 citation statements)

References 29 publications

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“…P300, Stimulus Intensity, Modality, Brain-computer interfaces (BCIs) allow users to control a computer or other device with their brain activity. While BCI technology has been developed and used primarily in medical contexts, a broad spectrum of non-clinical applications is on the horizon, including fields like concentration management, sleep improvement, music, and painting (Gürkök and Nijholt, 2013;Coates McCall and Wexler, 2020;Saha et al, 2021).…”

Section: Fundingmentioning

confidence: 99%

Brain-Computer Interfaces for Non-clinical (Home, Sports, Art, Entertainment, Education, Well-being) Applications

2022

Frontiers Research Topics

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Frontiers is more than just an open-access publisher of scholarly articles: it is a pioneering approach to the world of academia, radically improving the way scholarly research is managed. The grand vision of Frontiers is a world where all people have an equal opportunity to seek, share and generate knowledge. Frontiers provides immediate and permanent online open access to all its publications, but this alone is not enough to realize our grand goals. Frontiers Journal SeriesThe Frontiers Journal Series is a multi-tier and interdisciplinary set of open-access, online journals, promising a paradigm shift from the current review, selection and dissemination processes in academic publishing. All Frontiers journals are driven by researchers for researchers; therefore, they constitute a service to the scholarly community. At the same time, the Frontiers Journal Series operates on a revolutionary invention, the tiered publishing system, initially addressing specific communities of scholars, and gradually climbing up to broader public understanding, thus serving the interests of the lay society, too. Dedication to QualityEach Frontiers article is a landmark of the highest quality, thanks to genuinely collaborative interactions between authors and review editors, who include some of the world's best academicians. Research must be certified by peers before entering a stream of knowledge that may eventually reach the public -and shape society; therefore, Frontiers only applies the most rigorous and unbiased reviews. Frontiers revolutionizes research publishing by freely delivering the most outstanding research, evaluated with no bias from both the academic and social point of view. By applying the most advanced information technologies, Frontiers is catapulting scholarly publishing into a new generation.

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Section: Fundingmentioning

confidence: 99%

Brain-Computer Interfaces for Non-clinical (Home, Sports, Art, Entertainment, Education, Well-being) Applications

2022

Frontiers Research Topics

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“…With recent advances in affective response measurement, for example in determining neurophysiological correlates of affect (Mühl et al 2015), it appears that the distinction between perceived and induced emotion is a challenge which BCI may help to address in this musical context. In a visual context or multimodal context several systems harness this potential in a variety of BCI for arts systemssee (Gurkok and Nijholt 2013) (Hunter et al 2010;Huron 2011;Manuel 2005) and would be a logical area for further exploration given the startling advances in BCI technology for estimation of affective state; such applications are uniquely afforded by BCI -for example if used to generate music that gradually improves the mood of the patient in an autonomic process without the need for a therapist (Daly et al 2014b(Daly et al , 2016b.…”

Section: N22 Overcoming the Self-report Confoundmentioning

confidence: 99%

Evaluating BCI for Musical Expression: Historical Approaches, Challenges and Benefits

Williams

2019

Brain Art

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A recurring challenge in the use of BCI (and more generally HCI) for musical expression is in the design and conduct of appropriate evaluation strategies when considering BCI systems for music composition or performance. Assessing the value of computationally assisted creativity is challenging in most artistic domains, and the assessment of computer assisted (or entirely computer generated) music is no different. BCI provides two unique possibilities over traditional evaluation strategies: firstly, the possibility of devising evaluations which do not require conscious input from the listener (and therefore do not detract from the immersive experience of performing, creating, or listening to music), and secondly in devising neurofeedback loops to actively maneuver the creator or listener through an expressive musical experience. Music offers some unusual challenges in comparison to other artistic interfaces: for example, often it is made in ensemble, and there is evidence to suggest neurophysiological differences are evident in ensemble measurement when compared to solo performance activities, for example see (Babiloni et al. 2011). Moreover, a central purpose of music is often to incite movement (swaying, nodding head, dancing) -both in performer and audience -and as such this also offers up challenges for BCI/HCI design. This chapter considers historical approaches as well as making proposals for borrowing solutions from the world of auditory display (also referred to as sonification) and psychoacoustic evaluation techniques, to propose a hybrid paradigm for the evaluation of expression in BCI music applications.

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“…Engagement in computer games is continuously achieved by This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/ reading the emotional state of the player [14], [29], [30] and consequently adapting the game according to the available cognitive resources or the type of experience the player is looking for in that moment [31]. There is also growing interest in stretching the challenge by considering noncontrolled environments (e.g., changes in illumination, perspective, etc.…”

Section: Time-delay Neural Network For Continuousmentioning

confidence: 99%

Time-Delay Neural Network for Continuous Emotional Dimension Prediction From Facial Expression Sequences

Meng

Bianchi-Berthouze

Deng

et al. 2016

IEEE Trans. Cybern.

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Automatic continuous affective state prediction from naturalistic facial expression is a very challenging research topic but very important in human-computer interaction. One of the main challenges is modeling the dynamics that characterize naturalistic expressions. In this paper, a novel two-stage automatic system is proposed to continuously predict affective dimension values from facial expression videos. In the first stage, traditional regression methods are used to classify each individual video frame, while in the second stage, a time-delay neural network (TDNN) is proposed to model the temporal relationships between consecutive predictions. The two-stage approach separates the emotional state dynamics modeling from an individual emotional state prediction step based on input features. In doing so, the temporal information used by the TDNN is not biased by the high variability between features of consecutive frames and allows the network to more easily exploit the slow changing dynamics between emotional states. The system was fully tested and evaluated on three different facial expression video datasets. Our experimental results demonstrate that the use of a two-stage approach combined with the TDNN to take into account previously classified frames significantly improves the overall performance of continuous emotional state estimation in naturalistic facial expressions. The proposed approach has won the affect recognition sub-challenge of the Third International Audio/Visual Emotion Recognition Challenge.

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Affective Brain-Computer Interfaces for Arts

Cited by 18 publications

References 29 publications

Brain-Computer Interfaces for Non-clinical (Home, Sports, Art, Entertainment, Education, Well-being) Applications

Brain-Computer Interfaces for Non-clinical (Home, Sports, Art, Entertainment, Education, Well-being) Applications

Evaluating BCI for Musical Expression: Historical Approaches, Challenges and Benefits

Time-Delay Neural Network for Continuous Emotional Dimension Prediction From Facial Expression Sequences

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