MIRUtrecht participation in MediaEval 2013: Emotion in Music task

This working notes paper describes the system proposed by the MIRUtrecht team 1 for static emotion recognition from audio (task Emotion in Music) in the MediaEval evaluation contest 2013. We approach the problem by proposing a scheme comprising data filtering, feature extraction, attribute selection and multivariate regression. The system is based on state-of-the art research in the field and achieved performance of (in terms of R 2, i.e. proportion of variance explained by the model) 0.64 for arousal and 0.36 for valence. 1

Using neurophysiological signals that reflect cognitive or affective state

What can we learn from spontaneously occurring brain and other physiological signals about an individual’s cognitive and affective state and how can we make use of this information? \ud \ud One line of research that is actively involved with this question is Passive Brain-Computer-Interfaces (BCI). To date most BCIs are aimed at assisting patients for whom brain signals could form an alternative output channel as opposed to more common human output channels, like speech and moving the hands. However, brain signals (possibly in combination with other physiological signals) also form an output channel above and beyond the more usual ones: they can potentially provide continuous, online information about an individual’s cognitive and affective state without the need of conscious or effortful communication. The provided information could be used in a number of ways. Examples include monitoring cognitive workload through EEG and skin conductance for adaptive automation or using ERPs in response to errors to correct for a behavioral response. While Passive BCIs make use of online (neuro)physiological responses and close the interaction cycle between a user and a computer system, (neuro)physiological responses can also be used in an offline fashion. Examples of this include detecting amygdala responses for neuromarketing, and measuring EEG and pupil dilation as indicators of mental effort for optimizing information systems. \ud \ud The described field of applied (neuro)physiology can strongly benefit from high quality scientific studies that control for confounding factors and use proper comparison conditions. Another area of relevance is ethics, ranging from dubious product claims, acceptance of the technology by the general public, privacy of users, to possible effects that these kinds of applications may have on society as a whole. \ud \ud In this Research Topic we aimed to publish studies of the highest scientific quality that are directed towards applications that utilize spontaneously, effortlessly generated neurophysiological signals (brain and/or other physiological signals) reflecting cognitive or affective state. We especially welcomed studies that describe specific real world applications demonstrating a significant benefit compared to standard applications. We also invited original, new kinds of (proposed) applications in this area as well as comprehensive review articles that point out what is and what is not possible (according to scientific standards) in this field. Finally, we welcomed manuscripts on the ethical issues that are involved. \ud \ud Connected to the Research Topic was a workshop (held on June 6, during the Fifth International Brain-Computer Interface Meeting, June 3-7, 2013, Asilomar, California) that brought together a diverse group of people who were working in this field. We discussed the state of the art and formulated major challenges, as reflected in the first paper of the Research Topic