Abstract
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.
Keywords
- Music Therapy
- Music Listening
- Music Recommendation
- Recommendation Engine
- Sequential Floating Forward Selection
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Adomavicius, G.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering 17(6), 734–749 (2005)
Chang, C.-Y.: A music recommendation system with consideration of personal emotion (2010)
Claypool, M., Gokhale, A., Miranda, T., Murnikov, P., Netes, D., Sartin, M.: Combining content-based and collaborative filters in an online newspaper (1999)
Ekman, P.: Universals and cultural differences in facial expressions of emotion (1971)
Ko, K.-E.: Emotion recognition using EEG signals with relative power values and bayesian network. International Journal of Control, Automation, and Systems 7(5), 865–870 (2009)
Lu, C.-C.: A novel method for personalized music recommendation. Expert Systems with Applications 36(6), 10035–10044 (2009)
Park, D.: A literature review and classification of recommender systems research. Expert Systems with Applications 39(11), 10059–10072 (2012)
Petrantonakis, P.: Adaptive emotional information retrieval from EEG signals in the time-frequency domain. IEEE Transactions on Signal Processing 60(5), 2604–2616 (2012)
Radinsky, K., Kapoor, A., Oron, A., Master, K.: Brain-computer interfaces for music recommendation. In: Neural Information Processing Systems Foundations Workshop 2011 (2011); 5M.Sc. Individual Project Report (June 7, 2012)
Sourina, O.: Real-time EEG-based emotion recognition for music therapy. Journal on Multimodal User Interfaces 5(1), 27–35 (2012)
Krusienski, D.J.: A comparison of classification techniques for the p300 speller. Journal of Neural Engineering 3, 299 (2006)
Tanaka, K., Matsunaga, K., Wang, H.O.: Electroencephalogram-based control of an electric wheelchair. IEEE Transactions on Robotics 21(4), 762–766 (2005)
Hamadicharef, B., Zhang, H., Guan, C., Wang, C., Phua, K.S., Tee, K.P., Ang, K.K.: Learning EEG-based spectral-spatial patterns for attention level measurement. In: ISCAS 2009, pp. 1465–1468 (2009)
Emotiv. Emotiv - Brain Computer Interface Technology. http:// (2011), http://www.emotiv.com/
Higuchi, T.: Approach to an irregular time series on the basis of the fractal theory. Physica D: Nonlinear Phenomena 31(2), 277–283 (1988)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Guo, Y., Wu, C., Peteiro-Barral, D. (2012). An EEG-Based Brain Informatics Application for Enhancing Music Experience. In: Zanzotto, F.M., Tsumoto, S., Taatgen, N., Yao, Y. (eds) Brain Informatics. BI 2012. Lecture Notes in Computer Science(), vol 7670. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35139-6_25
Download citation
DOI: https://doi.org/10.1007/978-3-642-35139-6_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35138-9
Online ISBN: 978-3-642-35139-6
eBook Packages: Computer ScienceComputer Science (R0)