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Ronald Younes,Nabih Nader,Georges Khouryional models and their adequacy in describing emotional responses to music are described and discussed in different applications. The underlying emotion induction mechanisms beside cognitive appraisal are presented, and their implications on the field are analyzed. Musical characteristics such as te

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Debility

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Raj Senani,D. R. Bhaskar,R. K. Sharma expected or required to be in a particular state of alertness. For instance, pilots, security personnel, or medical personnel are expected to be in a highly alert state, and this method could help to confirm this or detect possible problems. In this work, electroencephalographic (EEG) data from 58

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https://doi.org/10.1007/978-3-319-42931-1erface (BCMI) system has recently become a realistic achievement. This chapter discusses previous research in the fields of mapping, sonification and musification in the context of designing a BCMI system and will be of particular interest to those who seek to develop their own. Design of a BCMI req

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https://doi.org/10.1007/978-3-663-05437-5ins a wide unexplored field of study. When one starts analyzing physiological signals measured on a person listening to music, one has to firstly define models to know what information could be observed with these signals. Conversely, when one starts trying to generate some music from physiological

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Keynes and the Cambridge Economics Schoolwithout the need for the user to control his brain activity. Passive BCI seem particularly relevant in the context of music creation where they can provide novel information to adapt the music creation process (e.g., user mental concentration state to adapt the music tempo). In this chapter, we pres

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