IEEE Transactions on Knowledge and Data Engineering
A novel method for personalized music recommendation
Expert Systems with Applications: An International Journal
A literature review and classification of recommender systems research
Expert Systems with Applications: An International Journal
Electroencephalogram-Based Control of an Electric Wheelchair
IEEE Transactions on Robotics
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Electroencephalography (EEG) technology has gained growing popularity in various applications. In this paper we propose a system based on affordable EEG devices to enhance music experience. Music is one of the major stimuli to which a brain responds. And the effect of music to our mood has long been recognized. Traditional music recommendation systems usually ignore the emotional effects of music on the users, but depend only on users' feedback through rating. With EEG device, it's possible to establish one's emotional profile while music listening, and thus design an emotion-based music recommendation engine. In this work, we present our effort on this research by exploiting how EEG could be applied to enhance the traditional music listening experience. Our research demonstrated that EEG applications should not be just limited in clinical field, but can be accessible to the public for broad use. In our system, we adopt an inexpensive EEG device (Emotiv EEG) to monitor brain activity in music listening to reflect emotional responses, and use mobile phone and Cloud based architecture to host the processing and recommendation algorithms to recognize, interpret and process EEG/music data. Such architecture is low cost, publicly accessible and generic to realize a wide class of brain informatics applications based on EEG.