Skip to main content
SpringerLink
Log in
Book cover

Guide to Brain-Computer Music Interfacing pp 89–103Cite as

Machine Learning to Identify Neural Correlates of Music and Emotions

  • Chapter
  • First Online:

Abstract

While music is widely understood to induce an emotional response in the listener, the exact nature of that response and its neural correlates are not yet fully explored. Furthermore, the large number of features which may be extracted from, and used to describe, neurological data, music stimuli, and emotional responses, means that the relationships between these datasets produced during music listening tasks or the operation of a brain–computer music interface (BCMI) are likely to be complex and multidimensional. As such, they may not be apparent from simple visual inspection of the data alone. Machine learning, which is a field of computer science that aims at extracting information from data, provides an attractive framework for uncovering stable relationships between datasets and has been suggested as a tool by which neural correlates of music and emotion may be revealed. In this chapter, we provide an introduction to the use of machine learning methods for identifying neural correlates of musical perception and emotion. We then provide examples of machine learning methods used to study the complex relationships between neurological activity, musical stimuli, and/or emotional responses.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD   54.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    Please refer to Chap. 2 for an introduction to EEG electrode placement systems.

References

  • Albrecht R, Ewing S (1989) Standardizing the administration of the profile of mood states (POMS): development of alternative word lists. J Pers Assess 53(1):31–39

    Article  Google Scholar 

  • Aloise F, Schettini F, Aricó P et al (2012) A comparison of classification techniques for a gaze-independent P300-based brain–computer interface. J Neural Eng 9(4):045012

    Article  Google Scholar 

  • Alpaydin E (2004) Introduction to machine learning. MIT Press, Cambridge

    Google Scholar 

  • Bradley MM, Lang PJ (1994) Measuring emotion: the self-assessment manikin and the semantic differential. J Behav Ther Experim Psychiatry 25(1):49–59

    Article  Google Scholar 

  • Bradley MM, Lang PJ, Margaret M et al (2007) The international affective digitized sounds affective ratings of sounds and instruction manual. Technical report B-3, University of Florida, Gainesville, Fl

    Google Scholar 

  • Christensen T (2002) The Cambridge history of western music theory. Cambridge University Press, Cambridge

    Google Scholar 

  • Comon P (1994) Independent component analysis, a new concept? Sig Process 36(3):287–314

    Article  MATH  Google Scholar 

  • Cong F, Alluri V, Nandi AK et al (2013) Linking brain responses to naturalistic music through analysis of ongoing EEG and stimulus features. IEEE Trans Multimedia 15(5):1060–1069

    Article  Google Scholar 

  • Cornelius RR (1996) The science of emotion. Prentice Hall, Upper Saddle River

    Google Scholar 

  • Cowie R, Douglas-Cowie E (2000) “FEELTRACE”: an instrument for recording perceived emotion in real time. In: Proceedings of the ISCA workshop on speech and emotion: a conceptual framework for research, Belfast, pp 19–24

    Google Scholar 

  • Daly I, Nasuto SJ, Warwick K (2011) Single tap identification for fast BCI control. Cogn Neurodyn 5(1):21–30

    Article  Google Scholar 

  • Daly I, Nasuto SJ, Warwick K (2012) Brain computer interface control via functional connectivity dynamics. Pattern Recogn 45(6):2123–2136

    Article  Google Scholar 

  • Dy J (2004) Feature selection for unsupervised learning. J Mach Learn Res 5:845–889

    MathSciNet  MATH  Google Scholar 

  • Ekman P (2003) Emotions revealed: recognizing faces and feelings to improve communication and emotional life. Weidenfeld & Nicolson, London

    Google Scholar 

  • Fontaine JRJ, Scherer KR, Roesch EB et al (2007) The world of emotions is not two-dimensional. Psychol Sci 18(12):1050–1057

    Article  Google Scholar 

  • Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157–1182

    MATH  Google Scholar 

  • Hevner SK (2007) EMuJoy: software for continuous measurement. Behav Res Methods 39(2):283–290

    Article  Google Scholar 

  • Hwang HJ, Kim S, Choi S et al (2013) EEG-based brain–computer interfaces (BCIs): a thorough literature survey. Int J Human Comput Interact 29(12):814–826

    Article  Google Scholar 

  • Izard CE (2007) Basic emotions, natural kinds, emotion schemas, and a new paradigm. Perspect Psychol Sci 2:260–280

    Article  Google Scholar 

  • Kokkinos I, Maragos P (2005) Nonlinear speech analysis using models for chaotic systems. IEEE Trans Speech Audio Process 13(6):1098–1109

    Article  Google Scholar 

  • Lang PJ, Bradley MM, Cuthbert BN (2008) International affective picture system (IAPS): affective ratings of pictures and instruction manual. Technical Report A-6, University of Florida, Gainesville, Fl

    Google Scholar 

  • Larsen RJ, Diener E (1987) Affect intensity as an individual difference characteristic: a review. J Res Pers 21(1):1–39

    Article  Google Scholar 

  • Lee H, Seungjin C (2003) PCA + HMM + SVM for EEG pattern classification. In: Proceedings of signal processing and its applications, vol 1, pp 541–544

    Google Scholar 

  • Lin Y, Juny T, Chen J (2009) EEG dynamics during music appreciation. In: Annual international conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society, pp 5316–5319

    Google Scholar 

  • Lin Y-P, Wang C-H, Jung T-P et al (2010) EEG-based emotion recognition in music listening. IEEE Trans Biomed Eng 57(7):1798–1806

    Google Scholar 

  • Lin Y-P, Chen J-H, Duann J-R et al (2011) Generalizations of the subject-independent feature set for music-induced emotion recognition. In: 33rd annual international conference of the IEEE, EMBS, Boston, Massachusetts, USA

    Google Scholar 

  • Liu M, Wan C (2001) Feature selection for automatic classification of musical instrument sounds. In: Proceedings of the first ACM/IEEE-CS joint conference on digital libraries—JCDL ’01. ACM Press, New York, pp 247–248

    Google Scholar 

  • Lotte F, Congedo M, Lécuyer A et al (2007) A review of classification algorithms for EEG-based brain-computer interfaces. J Neural Eng 4(2):1–13

    Article  Google Scholar 

  • Matthews G (1990) Refining the measurement of mood: the UWIST mood adjective checklist. Br J Psychol 81(1):17–42

    Article  Google Scholar 

  • Mitrović D, Zeppelzauer M, Breiteneder C (2010) Features for content-based audio retrieval. Adv Comput 78:71–150

    Article  Google Scholar 

  • Müller KR, Krauledat M, Dornhege G et al (2004) Machine learning techniques for brain-computer interfaces. Biomed Eng 49(1):11–22

    Google Scholar 

  • Müller-Putz GR, Breitwieser C, Cincotti F et al (2011) Tools for brain–computer interaction: a general concept for a hybrid BCI. Front Neuroinform 5:30

    Google Scholar 

  • Murugappan M, Nagarajan R, Yaacob S (2010) Classification of human emotion from EEG using discrete wavelet transform. J Biomed Sci Eng 3:390–396

    Article  Google Scholar 

  • Obermaier B, Guger C, Neuper C et al (2001) Hidden Markov models for online classification of single trial EEG data. Pattern Recogn Lett 22(12):1299–1309

    Article  MATH  Google Scholar 

  • Ogawa S, Ota S, Ito S et al (2005) Influence of music listening on the cerebral activity by analyzing EEG. In: Proceedings of the 9th international conference on knowledge-based intelligent information and engineering systems (KES’05), pp 657–663

    Google Scholar 

  • Pfurtscheller G, Allison BZ, Brunner C et al (2010) The hybrid BCI. Front Neuroprosthetics 4:30

    Google Scholar 

  • Qin J, Li Y, Cichocki A (2005) ICA and committee machine-based algorithm for cursor control in a BCI system. In: Advances in neural networks—ISNN 2005. Springer, Berlin, pp 973–978

    Google Scholar 

  • Rezaei S, Tavakolian K, Nasrabadi AM et al (2006) Different classification techniques considering brain computer interface applications. J Neural Eng 3(2006):139–144

    Google Scholar 

  • Roesch EB, Fontaine JB, Scherer KR (2006) The world of emotion is two-dimensional or is it? Paper presented to the HUMAINE Summer School 2006, Genoa, Italy

    Google Scholar 

  • Ruiz VF, Nasuto SJ (2005) Biomedical image classification methods and techniques. In: Costaridou L (ed) Medical image analysis methods. Taylor & Francis, New York, p 504

    Google Scholar 

  • Salovey P, Pizarro DA (2003) The value of emotional intelligence. In: Sternberg RJ, Lautrey J, Lubart TI (eds) Models of intelligence: international perspectives. American Psychological Association, Washington, DC, pp 263–278

    Google Scholar 

  • Scherer KR, Schorr A, Johnstone T (2001) Appraisal processes in emotion: theory, methods, research. Oxford University Press, Oxford

    Google Scholar 

  • Smith LI (2002) A tutorial on principal components analysis. Technical report, Cornell University

    Google Scholar 

  • Sohaib AT, Qureshi S, Hagelbäck J et al (2013) Evaluating classifiers for emotion recognition using EEG. In: Foundations of augmented cognition. Lecture notes in computer science, vol 8027, pp 492–501

    Google Scholar 

  • Soleymani M, Member S, Lee J (2012) DEAP: a database for emotion analysis using physiological signals. IEEE Trans Affect Comput 3(1):18–31

    Article  Google Scholar 

  • Tomkins SS (1962) Affect, imagery, consciousness. Springer, New York

    Google Scholar 

  • Tzanetakis G, Cook P (2002) Musical genre classification of audio signals. IEEE Trans Speech Audio Process 10(5):293–302

    Article  Google Scholar 

  • Wang XW, Nie D, Lu BL (2011) EEG-based emotion recognition using frequency domain features and support vector machines. In: Lu B-L, Zhang L, Kwok J (eds) Proceedings of the 18th international conference on neural information processing (ICONIP’11), vol 7062, pp 734–743

    Google Scholar 

  • Wang X-W, Nie D, Lu B-L (2013) Emotional state classification from EEG data using machine learning approach. Neurocomputing 129:94

    Google Scholar 

  • Warwick K, Nasuto SJ (2006) Historical and current machine intelligence. IEEE Instrum Meas Mag 9(6):20–26

    Article  Google Scholar 

  • Yongjin W, Ling G (2005) Recognizing human emotion from audiovisual information. In: IEEE international conference on acoustics, speech, and signal processing (ICASSP ’05), vol 2, pp 1125–1128

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Systems Engineering, University of Reading, Reading, RG6 6AY, UK

    Ian Daly, Etienne B. Roesch, James Weaver & Slawomir J. Nasuto

Authors
  1. Ian Daly
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Etienne B. Roesch
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. James Weaver
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Slawomir J. Nasuto
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ian Daly .

Editor information

Editors and Affiliations

  1. Interdisciplinary Centre for Computer Music Research (ICCMR), Plymouth University, Plymouth, United Kingdom

    Eduardo Reck Miranda

  2. Immersion, Bordeaux, France

    Julien Castet

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer-Verlag London

About this chapter

Cite this chapter

Daly, I., Roesch, E.B., Weaver, J., Nasuto, S.J. (2014). Machine Learning to Identify Neural Correlates of Music and Emotions. In: Miranda, E., Castet, J. (eds) Guide to Brain-Computer Music Interfacing. Springer, London. https://doi.org/10.1007/978-1-4471-6584-2_5

Download citation

  • DOI: https://doi.org/10.1007/978-1-4471-6584-2_5

  • Published:

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-6583-5

  • Online ISBN: 978-1-4471-6584-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation