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Sinterwerkstoffe aus Nickelaluminid,ter, we provide an introduction to the use of machine learning methods for identifying neural correlates of musical perception and emotion. We then provide examples of machine learning methods used to study the complex relationships between neurological activity, musical stimuli, and/or emotional re

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Ronald Younes,Nabih Nader,Georges Khouryresponses to music listening are briefly discussed, and their application to emotion recognition and to emotion intelligence in human–machine interaction is described. Music processing in the brain involves different brain areas and several studies attempted to investigate brain activity in relation

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https://doi.org/10.1007/978-3-663-05437-5ween musical and physiological models. We suggest in this article an experimental real-time system aiming at studying the interactions and retroactions between music and physiology, based on the paradigm of emotions.

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Diffusion in Non-Stoichiometric Compoundsmanating from the brain, the design of generative music techniques that respond to such information and the definition of ways in which such technology can effectively improve the lives of people with special needs and address therapeutic needs. This chapter discusses the first two challenges, in pa

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Variables Influencing the Sintering of MgO–computer interface (BCI) is a revolutionary new area using EEG that is most useful for the severely disabled individuals for hands-off device control and communication as they create a direct interface from the brain to the external environment, therefore circumventing the use of peripheral muscles

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https://doi.org/10.1007/978-3-642-99398-5uter music interfaces (BCMIs). It also includes results of research in refining digital signal processing (DSP) approaches and models of interaction using low-cost, portable BCIs. We will look at a range of designs for BCMIs using ERP techniques. These include the P300 Composer, the P300 Scale Playe

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