An Electroencephalogram-driven Personalized Affective Music Player System: Algorithms and Preliminary Implementation_Journal of Biomedical Engineering

Authors：

MAYong , LIJuan ,  LUBin

CTTL-Terminals of China Academy of Information and Communication Technology, Beijing 100191, China;

Corresponding author：

LUBin, Email: lvbin@catr.cn

Keywords：

electroencephalogram; personalized music recommendation system; emotion; Mel frequency cepstrum coefficients; Android

DOI：

10.7507/1001-5515.20160008

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

In order to monitor the emotional state changes of audience on real-time and to adjust the music playlist, we proposed an algorithm framework of an electroencephalogram (EEG) driven personalized affective music recommendation system based on the portable dry electrode shown in this paper. We also further finished a preliminary implementation on the Android platform. We used a two-dimensional emotional model of arousal and valence as the reference, and mapped the EEG data and the corresponding seed songs to the emotional coordinate quadrant in order to establish the matching relationship. Then, Mel frequency cepstrum coefficients were applied to evaluate the similarity between the seed songs and the songs in music library. In the end, during the music playing state, we used the EEG data to identify the audience’s emotional state, and played and adjusted the corresponding song playlist based on the established matching relationship.

Citation： MAYong, LIJuan, LUBin. An Electroencephalogram-driven Personalized Affective Music Player System: Algorithms and Preliminary Implementation. Journal of Biomedical Engineering, 2016, 33(1): 38-41. doi: 10.7507/1001-5515.20160008 Copy

1.	ECK D, LAMERE P, BERTIN-MAHIEUX T, et al. Automatic generation of social tags for music recommendation[C]// Proceedings of the 22th Annual Conference on Neural Information Processing Systems. Vancouver, Canada, 2008: 385-392.
2.	CASEY M A, VELTKAMP R, GOTO M, et al. Content-based music information retrieval: Current directions and future challenges[J]. Proc IEEE, 2008, 96(4): 668-696.
3.	OLIVER N, FLORES-MANGAS F. MPTrain: a mobile, music and physiology-based personal trainer[C]// Proceedings of the 8th Conference on Human-computer Interaction with Mobile Devices and Services, ACM. Espoo, Finland: 2006: 21-28.
4.	JANSSEN J H, VAN DEN BROEK E L, WESTERINK J H. Tune in to your emotions: a robust personalized affective music player[J]. User Model User-Adap Inter, 2012, 22(3): 255-279.
5.	SATO T G, KAMAMOTO Y, HARADA N, et al. A playback system that synchronizes the musical phrases with listener’s respiration phases[C]// CHI’ 13 Extended Abstracts on Human Factors in Computing Systems. New York, NY: ACM: 1035-1040.
6.	LIU N H, CHIANG C Y, HSU H M. Improving driver alertness through music selection using a mobile EEG to detect brainwaves[J]. Sersors, 2013, 13(7): 8199-8221.
7.	RUSSELL J A. Core affect and the psychological construction of emotion[J]. Psychol Rev, 2003, 110(1): 145-172.
8.	DARWIN C, EKMAN P, PRODGER P. The expression of the emotions in man and animals[M]. 3rd ed. New York: Oxford University Press, USA, 1998.
9.	IZARD C E. Basic emotions, natural kinds, emotion schemas, and a new paradigm[J]. Perspect Psychol Sci, 2007, 2(3): 260-280.
10.	AUCOUTURIER J J, PACHET F. Music similarity measures: What’s the use?[C]// Proceedings of the 3rd International Conference on Music Information Retrieval. Paris, France, 2002: 1-7.
11.	JENSEN J, CHRISTENSEN M, MURTHI M, et al. Evaluation of mfcc estimation techniques for music similarity[C]// Proceedings of the 14th European Signal Processing Conference. Florence, Italy: 2006: 1-5.
12.	牛滨, 孔令志, 罗森林, 等. 基于MFCC和GMM的个性音乐推荐模型[J]. 北京理工大学学报, 2009, 29(4): 351-355.
13.	LEVINIA E, BICKEL P. The earth mover’s distance is the mallows distance: Some insights from statistics[C]// Proceedings of the 8th IEEE International Conference on Computer Vision. Vancouver, Canada: 2001: 251-256.

1. ECK D, LAMERE P, BERTIN-MAHIEUX T, et al. Automatic generation of social tags for music recommendation[C]// Proceedings of the 22th Annual Conference on Neural Information Processing Systems. Vancouver, Canada, 2008: 385-392.
2. CASEY M A, VELTKAMP R, GOTO M, et al. Content-based music information retrieval: Current directions and future challenges[J]. Proc IEEE, 2008, 96(4): 668-696.
3. OLIVER N, FLORES-MANGAS F. MPTrain: a mobile, music and physiology-based personal trainer[C]// Proceedings of the 8th Conference on Human-computer Interaction with Mobile Devices and Services, ACM. Espoo, Finland: 2006: 21-28.
4. JANSSEN J H, VAN DEN BROEK E L, WESTERINK J H. Tune in to your emotions: a robust personalized affective music player[J]. User Model User-Adap Inter, 2012, 22(3): 255-279.
5. SATO T G, KAMAMOTO Y, HARADA N, et al. A playback system that synchronizes the musical phrases with listener’s respiration phases[C]// CHI’ 13 Extended Abstracts on Human Factors in Computing Systems. New York, NY: ACM: 1035-1040.
6. LIU N H, CHIANG C Y, HSU H M. Improving driver alertness through music selection using a mobile EEG to detect brainwaves[J]. Sersors, 2013, 13(7): 8199-8221.
7. RUSSELL J A. Core affect and the psychological construction of emotion[J]. Psychol Rev, 2003, 110(1): 145-172.
8. DARWIN C, EKMAN P, PRODGER P. The expression of the emotions in man and animals[M]. 3rd ed. New York: Oxford University Press, USA, 1998.
9. IZARD C E. Basic emotions, natural kinds, emotion schemas, and a new paradigm[J]. Perspect Psychol Sci, 2007, 2(3): 260-280.
10. AUCOUTURIER J J, PACHET F. Music similarity measures: What’s the use?[C]// Proceedings of the 3rd International Conference on Music Information Retrieval. Paris, France, 2002: 1-7.
11. JENSEN J, CHRISTENSEN M, MURTHI M, et al. Evaluation of mfcc estimation techniques for music similarity[C]// Proceedings of the 14th European Signal Processing Conference. Florence, Italy: 2006: 1-5.
12. 牛滨, 孔令志, 罗森林, 等. 基于MFCC和GMM的个性音乐推荐模型[J]. 北京理工大学学报, 2009, 29(4): 351-355.
13. LEVINIA E, BICKEL P. The earth mover’s distance is the mallows distance: Some insights from statistics[C]// Proceedings of the 8th IEEE International Conference on Computer Vision. Vancouver, Canada: 2001: 251-256.

Previous Article
Portable Epileptic Seizure Monitoring Intelligent System Based on Android System
Next Article
Classification and Correlative Technology Development of Wearable Devices

Journal of Biomedical Engineering

An Electroencephalogram-driven Personalized Affective Music Player System: Algorithms and Preliminary Implementation

Abstract Full text Figures/Tables Video References Cited by

Previous Article

Next Article

Format

Content