منابع مشابه
Personalised, Multi-modal, Affective State Detection for Hybrid Brain-Computer Music Interfacing
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
متن کاملNew Approaches in Brain-Computer Music Interfacing
This paper presents on-going research into the creation of performance and compositional tools using a BrainComputer Music Interface (BCMI). The research demonstrates the suitability of the SSVEP (Steady-State Visual Evoked Potentials) technique of generating brainwave information to cognitively control music. Furthermore, it considers the practical implications of using brainwaves in music, an...
متن کاملAffective Music Information Retrieval
Much of the appeal of music lies in its power to convey emotions/moods and to evoke them in listeners. In consequence, the past decade witnessed a growing interest in modeling emotions from musical signals in the music information retrieval (MIR) community. In this article, we present a novel generative approach to music emotion modeling, with a specific focus on the valence-arousal (VA) dimens...
متن کاملAffective Feature Design and Predicting Continuous Affective Dimensions from Music
This paper presents affective features designed for music and develops a method to predict dynamic emotion ratings along the arousal and valence dimensions. We learn a model to predict continuous time emotion ratings based on combination of global and local features. This allows us to exploit information from both the scales to make a more robust prediction.
متن کاملBrain-computer music interfacing for continuous control of musical tempo
A Brain-computer music interface (BCMI) is developed to allow for continuous modification of the tempo of dynamically generated music. Six out of seven participants are able to control the BCMI at significant accuracies and their performance is observed to increase over time.
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ژورنال
عنوان ژورنال: Journal of Neural Engineering
سال: 2016
ISSN: 1741-2560,1741-2552
DOI: 10.1088/1741-2560/13/4/046022