World Scientific
  • Search
  •   
Skip main navigation

Cookies Notification

We use cookies on this site to enhance your user experience. By continuing to browse the site, you consent to the use of our cookies. Learn More
×

System Upgrade on Tue, May 28th, 2024 at 2am (EDT)

Existing users will be able to log into the site and access content. However, E-commerce and registration of new users may not be available for up to 12 hours.
For online purchase, please visit us again. Contact us at [email protected] for any enquiries.
Special Issue — Neuroengineering: from Neurosciences to Computations; Guest Editors: Diego Andina, Kunihiko Fukushima, Javier Ropero Peláez and Duc Truong Pham; Research ArticlesNo Access

Neural Correlates of Phrase Quadrature Perception in Harmonic Rhythm: An EEG Study Using a Brain–Computer Interface

    https://doi.org/10.1142/S012906571750054XCited by:15 (Source: Crossref)

    For the sake of establishing the neural correlates of phrase quadrature perception in harmonic rhythm, a musical experiment has been designed to induce music-evoked stimuli related to one important aspect of harmonic rhythm, namely the phrase quadrature. Brain activity is translated to action through electroencephalography (EEG) by using a brain–computer interface. The power spectral value of each EEG channel is estimated to obtain how power variance distributes as a function of frequency. The results of processing the acquired signals are in line with previous studies that use different musical parameters to induce emotions. Indeed, our experiment shows statistical differences in theta and alpha bands between the fulfillment and break of phrase quadrature, an important cue of harmonic rhythm, in two classical sonatas.

    References

    • 1. S. Aydin, S. Demitras, K. Ates and M. A. Tunga, Emotion recognition with eigen features of frequency band activities embedded in induced brain oscillations mediated by affective pictures, Int. J. Neural Syst. 26(3) (2016) 1650013. Link, Web of ScienceGoogle Scholar
    • 2. A. Banerjee, S. Sanyal, A. Patranabis, K. Banerjee, T. Guhathakurta, R. Sengupta, D. Ghosh and P. Ghose, Study on brain dynamics by non linear analysis of music induced EEG signals, Physica A 444 (2016) 110–120. Crossref, Web of ScienceGoogle Scholar
    • 3. J. S. Barlow, Artifact processing (rejection and minimization) in EEG data processing, in Clinical Applications of Computer Analysis of EEG and Other Neurophysiological Signals (Amsterdam, 1986), pp. 15–62. Google Scholar
    • 4. A. Burns, H. Adeli and J. A. Buford, Brain–computer interface after nervous system injury, Neuroscientist 20(6) (2014) 639–651. Crossref, Medline, Web of ScienceGoogle Scholar
    • 5. J. C. Castillo, Á. Castro-González, A. Fernández-Caballero, J. M. Latorre, J. M. Pastor, A. Fernández-Sotos and M. A. Salichs, Software architecture for smart emotion recognition and regulation of the ageing adult, Cogn. Comput. 8(2) (2016) 357–367. Crossref, Web of ScienceGoogle Scholar
    • 6. S. E. Da Costa, L. M. Miller, W. van der Zwaag, S. Clarke and M. Saenz, Tuning in to sound: Frequency-selective attentional filter in human primary auditory cortex, J. Neurosci. 33(5) (2013) 1858–1863. Crossref, Medline, Web of ScienceGoogle Scholar
    • 7. I. Daly, A. Malik, F. Hwang, E. Roesch, J. Weaver, A. Kirke, D. Williams, E. Miranda and S. J. Nasuto, Neural correlates of emotional responses to music: An EEG study, Neurosci. Lett. 573 (2014) 52–57. Crossref, Medline, Web of ScienceGoogle Scholar
    • 8. I. Daly, J. Hallowell, F. Hwang, A. Kirke, A. Malik, E. Roesch, J. Weaver, D. Williams, E. Miranda and S. J. Nasuto, Changes in music tempo entrain movement related brain activity, in Proc. Conf. IEEE Engineering in Medicine and Biological Society (Chicago, IL, 2014), pp. 4595–4598. Google Scholar
    • 9. A. Fernández-Caballero, A. Martínez-Rodrigo, J. M. Pastor, J. C. Castillo, E. Lozano-Monasor, M. T. López, R. Zangróniz, J. M. Latorre and A. Fernández-Sotos, Smart environment architecture for emotion recognition and regulation, J. Biomed. Inform. 64 (2016) 55–73. Crossref, Medline, Web of ScienceGoogle Scholar
    • 10. A. Fernández-Caballero, J. M. Latorre, J. M. Pastor and A. Fernández-Sotos, Improvement of the elderly quality of life and care through smart emotion regulation, in Ambient Assisted Living and Daily Activities (Springer, New York, NY, 2014), pp. 348–355. CrossrefGoogle Scholar
    • 11. A. Fernández-Sotos, A. Fernández-Caballero and J. M. Latorre, Influence of tempo and rhythmic unit in musical emotion regulation, Front. Comput. Neurosci. 10 (2016) 80. Crossref, Medline, Web of ScienceGoogle Scholar
    • 12. A. Fernández-Sotos, A. Fernández-Caballero and J. M. Latorre, Elicitation of emotions through music: The influence of note value, in Artificial Computation in Biology and Medicine (Springer, New York, NY, 2015), pp. 488–497. CrossrefGoogle Scholar
    • 13. P. Fossati, Neural correlates of emotion processing: From emotional to social brain, Eur. Neuropsychopharmacol. 22(Suppl. 3) (2012) S487–S491. Crossref, Medline, Web of ScienceGoogle Scholar
    • 14. I. I. Goncharova, D. J. McFarland, T. M. Vaughan and J. R. Wolpaw, EMG contamination of EEG: Spectral and topographical characteristics, Clin. Neurophysiol. 114(9) (2003) 1580–1593. Crossref, Medline, Web of ScienceGoogle Scholar
    • 15. W. Y. Hsu, Assembling a multi-feature EEG classifier for left–right motor imagery data using wavelet-based fuzzy approximate entropy for improved accuracy, Int. J. Neural Syst. 25(8) (2015) 1550037. Link, Web of ScienceGoogle Scholar
    • 16. A. K. Jain, R. P. W. Duin and J. Mao, Statistical pattern recognition: A review, IEEE Trans. Pattern Anal. Mach. Intell. 22(1) (2000) 4–37. Crossref, Web of ScienceGoogle Scholar
    • 17. P. Janata, Neural basis of music perception, Handb. Clin. Neurol. 129 (2015) 187–205. Crossref, MedlineGoogle Scholar
    • 18. N. Jausovec and K. Habe, The “Mozart effect”: An electroencephalographic analysis employing the methods of induced event-related desynchronization/synchronization and event-related coherence, Brain Topogr. 16(2) (2003) 73–84. Crossref, Medline, Web of ScienceGoogle Scholar
    • 19. J. Jin, E. W. Sellers, S. Zhou, Y. Zhang, X. Wang and A. Cichocki, A P300 brain–computer interface based on a modification of the mismatch negativity paradigm, Int. J. Neural Syst. 25(3) (2015) 1550011. Link, Web of ScienceGoogle Scholar
    • 20. I. T. Jolliffe, Principal Component Analysis, 2nd edn. (Springer, New York, NY, 2002). Google Scholar
    • 21. G. H. Klem, H. Lüders, H. Jasper and C. Elger, The ten–twenty electrode system of the international federation of clinical neurophysiology, Clin. Neurophysiol. 52 (1999) 3–6. Google Scholar
    • 22. T. R. Knösche, C. Neuhaus, J. Haueisen, K. Alter, B. Maess, O. W. Witte and A. D. Friederici, Perception of phrase structure in music, Hum. Brain Mapp. 24(4) (2005) 259–273. Crossref, Medline, Web of ScienceGoogle Scholar
    • 23. S. Koelsch, Brain correlates of music-evoked emotions, Nat. Rev. Neurosci. 15(3) (2014) 170–180. Crossref, Medline, Web of ScienceGoogle Scholar
    • 24. S. Koelsch, Toward a neural basis of music perception — A review and updated model, Front. Psychol. 2 (2011) 110. Crossref, Medline, Web of ScienceGoogle Scholar
    • 25. S. Koelstra, C. Mühl, M. Soleymani, J.-S. Lee, A. Yazdani, T. Ebrahimi, T. Pun, A. Nijholt and I. Patras, Deap: A database for emotion analysis; using physiological signals, IEEE Trans. Affect. Comput. 3(1) (2012) 18–31. Crossref, Web of ScienceGoogle Scholar
    • 26. J. Li, H. Ji, L. Cao, D. Zang, R. Gu, B. Xia and Q. Wu, Evaluation and application of a hybrid brain computer interface for real wheelchair parallel control with multi-degree of freedom, Int. J. Neural Syst. 24(4) (2014) 1450014. Link, Web of ScienceGoogle Scholar
    • 27. C. Liu, B. Abu-Jamous, E. Brattico and A. K. Nandi, Towards tunable consensus clustering for studying functional brain connectivity during affective processing, Int. J. Neural Syst. 27(2) (2017) 1650042. Link, Web of ScienceGoogle Scholar
    • 28. Y. Liu and O. Sourina, EEG databases for emotion recognition, in 2013 Int. Conf. Cyberworlds (IEEE, 2013), pp. 302–309. Google Scholar
    • 29. M. A. Lopez-Gordo, F. Pelayo, A. Prieto and E. Fernandez, An auditory brain–computer interface with accuracy prediction, Int. J. Neural Syst. 22(3) (2012) 1250009. Link, Web of ScienceGoogle Scholar
    • 30. A. V. Maslennikova, A. A. Varlamov and V. B. Strelets, Characteristics of evoked changes in EEG spectral power and evoked potentials on perception of musical harmonies in musicians and nonmusicians, Neurosci. Behav. Physiol. 45(1) (2015) 78–83. CrossrefGoogle Scholar
    • 31. C. M. Michel, D. Lehmann, B. Henggeler and D. Brandeis, Localization of the sources of EEG delta, theta, alpha and beta frequency bands using the FFT dipole approximation, Electroencephalogr. Clin. Neurophysiol. 82(1) (1992) 38–44. Crossref, MedlineGoogle Scholar
    • 32. L. Minato, C. Rosazza, L. DIncerti et al., Functional MRI/event-related potential study of sensory consonance and dissonance in musicians and nonmusicians, Neuroreport 20(1) (2009) 87–92. Crossref, Medline, Web of ScienceGoogle Scholar
    • 33. A. Ortiz-Rosario and H. Adeli, Brain–computer interface technologies: From signal to action, Rev. Neurosci. 24(5) (2013) 537–552. Crossref, Medline, Web of ScienceGoogle Scholar
    • 34. N. Passynkova, H. Neubauer and H. Scheich, Spatial organization of EEG coherence during listening to consonant and dissonant chords, Neurosci. Lett. 412(1) (2007) 6–11. Crossref, Medline, Web of ScienceGoogle Scholar
    • 35. M. P. Paulraj, Y. B. Sazali and C. K. Yogesh, Fractal feature based detection of muscular and ocular artifacts in EEG signals, in IEEE Conf. Biomedical Engineering and Sciences (IEEE, 2014), pp. 916–921. Google Scholar
    • 36. T. D. Pham and D. Tran, Emotion recognition using the emotiv EPOC device, Lect. Notes Comput. Sci. 7667 (2012) 394–399. CrossrefGoogle Scholar
    • 37. G. N. Ranky and S. Adamovich, Analysis of a commercial EEG device for the control of a robot arm, in The 36th Annual Northeast Bioengineering Conf. (IEEE, 2010), pp. 1–2. Google Scholar
    • 38. A. G. Reddy and S. Narava, Artifact removal from EEG signals, Int. J. Comput. Appl. 77(13) (2013) 16–19. Google Scholar
    • 39. P. M. R. Reis, F. Hebenstreit, F. Gabsteiger, V. von Tscharner and M. Lochmann, Methodological aspects of EEG and body dynamics measurements during motion, Front. Hum. Neurosci. 8(156) (2014) 156. Medline, Web of ScienceGoogle Scholar
    • 40. W. Russo, Jazz Composition and Orchestration (University of Chicago Press, Chicago, IL, 1997). Google Scholar
    • 41. S. Sanei, Adaptive Processing of Brain Signals (John Wiley & Sons, Hoboken, NJ, USA, 2013). CrossrefGoogle Scholar
    • 42. L. A. Schmidt and L. J. Trainor, Frontal brain electrical activity (EEG) distinguishes valence and intensity of musical emotions, Cogn. Emot. 15 (2001) 487–500. Crossref, Web of ScienceGoogle Scholar
    • 43. B. Schmidt and S. Hanslmayr, Resting frontal EEG alpha-asymmetry predicts the evaluation of affective musical stimuli, Neurosci. Lett. 460(3) (2009) 237–240. Crossref, Medline, Web of ScienceGoogle Scholar
    • 44. J. P. Swain, Harmonic Rhythm: Analysis and Interpretation (Oxford University Press, New York, NY, 2002). CrossrefGoogle Scholar
    • 45. W. O. Tatum and R. Ellen, Grass lecture: Extraordinary EEG, Neurodiagn. J. 54(1) (2014) 3–21. MedlineGoogle Scholar
    • 46. Y. Tian, W. Ma, C. Tian, P. Xu and D. Yao, Brain oscillations and electroencephalography scalp networks during tempo perception, Neurosci. Bull. 29(6) (2013) 731–736. Crossref, Medline, Web of ScienceGoogle Scholar
    • 47. Y. Tonoyan, D. Looney, D. P. Mandic and M. M. Van Hulle, Discriminating multiple emotional states from EEG using a data-adaptive, multiscale information-theoretic approach, Int. J. Neural Syst. 26(2) (2016) 1650005. Link, Web of ScienceGoogle Scholar
    • 48. R. Trogan, The Circle and the Diamond: The Odyssey of Music (XLIBRIS, Bloomingdale, IN, 2013). Google Scholar
    • 49. K. Vytal and S. Hamann, Neuroimaging support for discrete neural correlates of basic emotions: A voxel-based meta-analysis, J. Cogn. Neurosci. 22(12) (2010) 2864–2885. Crossref, Medline, Web of ScienceGoogle Scholar
    • 50. D. Willimek and B. Willimek, Music and emotions — Research on the theory of musical equilibration (die Strebetendenz-Theorie), http://www. willimekmusic.de/music-and-emotions.pdf. Google Scholar
    • 51. E. Yin, T. Zeyl, R. Saab, D. Hu, Z. Zhou and T. Chau, An auditory-tactile visual saccade-independent P300 brain–computer interface, Int. J. Neural Syst. 26(1) (2016) 1650001. Link, Web of ScienceGoogle Scholar
    Remember to check out the Most Cited Articles!

    Check out our titles in neural networks today!