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            <title xml:lang="en">A Consumer BCI for Automated Music Evaluation Within a Popular On-Demand Music Streaming Service “Taking Listener’s Brainwaves to Extremes”</title>
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            <idno type="halRefHtml">&lt;i&gt;12th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI)&lt;/i&gt;, Sep 2016, Thessaloniki, Greece. pp.429-440, &lt;a target="_blank" href="https://dx.doi.org/10.1007/978-3-319-44944-9_37"&gt;&amp;#x27E8;10.1007/978-3-319-44944-9_37&amp;#x27E9;&lt;/a&gt;</idno>
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                <term xml:lang="en">Spotify</term>
                <term xml:lang="en">Human machine interaction</term>
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              <p>We investigated the possibility of a using a machine-learning scheme in conjunction with commercial wearable EEG-devices for translating listener’s subjective experience of music into scores that can be used for the automated annotation of music in popular on-demand streaming services.Based on the established -neuroscientifically sound- concepts of brainwave frequency bands, activation asymmetry index and cross-frequency-coupling (CFC), we introduce a Brain Computer Interface (BCI) system that automatically assigns a rating score to the listened song.Our research operated in two distinct stages: (i) a generic feature engineering stage, in which features from signal-analytics were ranked and selected based on their ability to associate music induced perturbations in brainwaves with listener’s appraisal of music. (ii) a personalization stage, during which the efficiency of extreme learning machines (ELMs) is exploited so as to translate the derived patterns into a listener’s score. Encouraging experimental results, from a pragmatic use of the system, are presented.</p>
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