Advertisement

Brain Topography

, Volume 31, Issue 2, pp 153–160 | Cite as

EEG Frequency-Tagging and Input–Output Comparison in Rhythm Perception

  • Sylvie Nozaradan
  • Peter E. Keller
  • Bruno Rossion
  • André Mouraux
Review

Abstract

The combination of frequency-tagging with electroencephalography (EEG) has recently proved fruitful for understanding the perception of beat and meter in musical rhythm, a common behavior shared by humans of all cultures. EEG frequency-tagging allows the objective measurement of input–output transforms to investigate beat perception, its modulation by exogenous and endogenous factors, development, and neural basis. Recent doubt has been raised about the validity of comparing frequency-domain representations of auditory rhythmic stimuli and corresponding EEG responses, assuming that it implies a one-to-one mapping between the envelope of the rhythmic input and the neural output, and that it neglects the sensitivity of frequency-domain representations to acoustic features making up the rhythms. Here we argue that these elements actually reinforce the strengths of the approach. The obvious fact that acoustic features influence the frequency spectrum of the sound envelope precisely justifies taking into consideration the sounds used to generate a beat percept for interpreting neural responses to auditory rhythms. Most importantly, the many-to-one relationship between rhythmic input and perceived beat actually validates an approach that objectively measures the input–output transforms underlying the perceptual categorization of rhythmic inputs. Hence, provided that a number of potential pitfalls and fallacies are avoided, EEG frequency-tagging to study input–output relationships appears valuable for understanding rhythm perception.

Keywords

EEG Frequency-tagging Rhythm and beat perception Auditory system Perceptual categorization Neural transform 

Notes

Acknowledgements

S.N. is supported by an Australian Research Council (ARC) DECRA DE160101064 and FRSM 3.4558.12 Convention Grant from the Belgian National Fund for Scientific Research (F.R.S.-FNRS) (to Pr. A. Mouraux). P.K. is supported by a Future Fellowship grant from the Australian Research Council (FT140101162).

Compliance with Ethical Standards

Conflict of interest

The authors declare that they have no conflict of interests.

References

  1. Adrian ED, Matthews BHC (1934) The interpretation of potential waves in the cortex. J Physiol 81:440–471.  https://doi.org/10.1113/jphysiol.1934.sp003147 CrossRefPubMedPubMedCentralGoogle Scholar
  2. Alonso-Prieto E, Belle GV, Liu-Shuang J, Norcia AM, Rossion B (2013) The 6 Hz fundamental stimulation frequency rate for individual face discrimination in the right occipito-temporal cortex. Neuropsychologia 51(13):2863–2875CrossRefPubMedGoogle Scholar
  3. Bruner JS (1957) Going beyond the information given. Norton, New YorkGoogle Scholar
  4. Celma-Miralles A, de Menezes RF, Toro JM (2017) Look at the beat, feel the meter: top-down effects of meter induction on auditory and visual modalities. Front Hum Neurosci 10:108.  https://doi.org/10.3389/fnhum.2016.00108 Google Scholar
  5. Chemin B, Mouraux A, Nozaradan S (2014) Body movement selectively shapes the neural representation of musical rhythm. Psychol Sci 25(12):2147–2159CrossRefPubMedGoogle Scholar
  6. Cirelli LK, Spinelli C, Nozaradan S, Trainor LJ (2016) Measuring neural entrainment to beat and meter in infants: effects of music background. Front Neurosci 10:229.  https://doi.org/10.3389/fnins.2016.00229 CrossRefPubMedPubMedCentralGoogle Scholar
  7. Desain P, Honing H (2003) The formation of rhythmic categories and metric priming. Perception 32(3):341–365CrossRefPubMedGoogle Scholar
  8. Ding et al (2016) Temporal modulations in speech and music. Neurosci Biobehav Rev.  https://doi.org/10.1016/j.neubiorev.2017.02.011 Google Scholar
  9. Edelman GM (1978) The mindful brain: cortical organization and the group-selective theory of higher brain function. MIT Press, Cambridge (ISBN 9780262050203) Google Scholar
  10. Edelman GM, Gally JA (2001) Degeneracy and complexity in biological systems. Proc Natl Acad Sci USA 98(24):13763–13768CrossRefPubMedPubMedCentralGoogle Scholar
  11. Eggermont JJ (2001) Between sound and perception: reviewing the search for a neural code. Hear Res 157(1–2):1–42CrossRefPubMedGoogle Scholar
  12. Galambos R (1982) Tactile and auditory stimuli repeated at high rates (30–50 per sec) produce similar event related potentials. Ann NY Acad Sci 388:722–728CrossRefPubMedGoogle Scholar
  13. Galambos R, Makeig S, Talmachoff PJ (1981) A 40-Hz auditory potential recorded from the human scalp. Proc Natl Acad Sci USA 78:2643–2647CrossRefPubMedPubMedCentralGoogle Scholar
  14. Helmholtz H (1866) Concerning the perceptions in general. Translated by Southall JPC Treatise on physiological optics, vol III, 3rd edn., Dover, New York (1925 Opt Soc Am Sect. 26)Google Scholar
  15. Henry MJ, Herrmann B, Grahn JA (2017) What can we learn about beat perception by comparing brain signals and stimulus envelopes? PLoS ONE 12(2):e0172454.  https://doi.org/10.1371/journal.pone.0172454 CrossRefPubMedPubMedCentralGoogle Scholar
  16. Joris PX, Schreiner CE, Rees A (2004) Neural processing of amplitude-modulated sounds. Physiol Rev 84(2):541–577CrossRefPubMedGoogle Scholar
  17. Lerdahl F, Jackendoff Ray (1983) A generative theory of tonal music. MIT Press, CambridgeGoogle Scholar
  18. London J (2004) Hearing in time: psychological aspects of musical meter. Oxford, LondonCrossRefGoogle Scholar
  19. McAuley JD (2010) Tempo and rhythm. In Jones MR et al (eds) Music perception, Springer handbook of auditory research 36, Springer, New YorkGoogle Scholar
  20. Moungou A, Thonnard JL, Mouraux A (2016) EEG frequency tagging to explore the cortical activity related to the tactile exploration of natural textures. Sci Rep 6:20738.  https://doi.org/10.1038/srep20738 CrossRefPubMedPubMedCentralGoogle Scholar
  21. Norcia AM, Appelbaum LG, Ales JM, Cottereau BR, Rossion B (2015) The steady-state visual evoked potential in vision research: a review. J Vis 15(6):4CrossRefPubMedPubMedCentralGoogle Scholar
  22. Nozaradan S (2014) Exploring how musical rhythm entrains brain activity with electroencephalogram frequency-tagging. Phil Trans B 369(1658):20130393.  https://doi.org/10.1098/rstb.2013.0393 CrossRefGoogle Scholar
  23. Nozaradan S, Peretz I, Missal M, Mouraux A (2011) Tagging the neuronal entrainment to beat and meter. J Neurosci 31:10234–10240CrossRefPubMedGoogle Scholar
  24. Nozaradan S, Peretz I, Mouraux A (2012b) Selective neuronal entrainment to the beat and meter embedded in a musical rhythm. J Neurosci 32(49):17572–17581CrossRefPubMedGoogle Scholar
  25. Nozaradan S, Zerouali Y, Peretz I, Mouraux A (2015) Capturing with EEG the neural entrainment and coupling underlying sensorimotor synchronization to the beat. Cereb Cortex 25(3):736–747CrossRefPubMedGoogle Scholar
  26. Nozaradan S, Schönwiesner M, Caron-Desrochers L, Lehmann A (2016a) Enhanced brainstem and cortical encoding of sound during synchronized movement. Neuroimage 142:231–240CrossRefPubMedGoogle Scholar
  27. Nozaradan S, Mouraux A, Jonas J, Colnat-Coulbois S, Rossion B, Maillard L (2016b) Intracerebral evidence of rhythm transform in the human auditory cortex. Brain Struct Funct 222(5):2389–2404CrossRefPubMedGoogle Scholar
  28. Nozaradan S, Peretz I, Keller PE (2016c) Individual differences in rhythmic cortical entrainment correlate with predictive behavior in sensorimotor synchronization. Sci Rep 6:20612.  https://doi.org/10.1038/srep20612 CrossRefPubMedPubMedCentralGoogle Scholar
  29. Nozaradan S, Mouraux A, Cousineau M (2017a) Frequency-tagging to track the neural processing of contrast in fast continuous sound sequences. J Neurophysiol.  https://doi.org/10.1152/jn.00971.2016 PubMedGoogle Scholar
  30. Nozaradan S, Schwartze M, Obermeier C, Kotz SA (2017b) Specific contributions of basal ganglia and cerebellum to the neural tracking of rhythm. Cortex, (In press) Google Scholar
  31. Pantev C, Roberts LE, Elbert T, Ross B, Wienbruch C (1996) Tonotopic organization of the sources of human auditory steady-state responses. Hear Res 101(1–2):62–74CrossRefPubMedGoogle Scholar
  32. Picton TW, John MS, Dimitrijevic A, Purcell D (2003) Human auditory steady-state responses. Int J Audiol 42(4):177–219CrossRefPubMedGoogle Scholar
  33. Plack CJ, Barker D, Hall DA (2014) Pitch coding and pitch processing in the human brain. Hear Res 307:53–64CrossRefPubMedGoogle Scholar
  34. Povel DJ, Essens PJ (1985) Perception of temporal patterns. Music Percept 2:411–441CrossRefGoogle Scholar
  35. Rajendran VG, Garcia-Lazaro JA, Harper NS, Lesica NA, Schnupp JWH (2016) Asymmetry in neural responses to “on-beat” and “off-beat” sounds in the gerbil inferior colliculus. Proceedings of APAN XIV meeting San Diego, CA, USA, 2016Google Scholar
  36. Regan DM (1966) Some characteristics of average steady-state and transient responses evoked by modulated light. Electroencephalogr Clin Neurophysiol 20(3):238–248CrossRefPubMedGoogle Scholar
  37. Regan DM (1989) Human brain electrophysiology: evoked potentials and evoked magnetic fields in science and medicine. Elsevier, New YorkGoogle Scholar
  38. Repp BH (2005) Sensorimotor synchronization: a review of the tapping literature. Psychon Bull Rev 12(6):969–992CrossRefPubMedGoogle Scholar
  39. Retter TL, Rossion B (2016) Uncovering the neural magnitude and spatio-temporal dynamics of natural image categorization in a fast visual stream. Neuropsychologia 91:9–28CrossRefPubMedGoogle Scholar
  40. Ross B, Draganova R, Picton TW, Pantev C (2003) Frequency specificity of 40-Hz auditory steady-state responses. Hear Res 186(1–2):57–68CrossRefPubMedGoogle Scholar
  41. Rossion B (2014) Understanding individual face discrimination by means of fast periodic visual stimulation. Exp Brain Res 232(6):1599–1621CrossRefPubMedGoogle Scholar
  42. Rossion B, Boremanse A (2011) Robust sensitivity to facial identity in the right human occipito-temporal cortex as revealed by steady-state visual-evoked potentials. J Vis 11(2):16.  https://doi.org/10.1167/11.2.16 CrossRefPubMedGoogle Scholar
  43. Stupacher J, Wood G, Witte M (2017) Neural entrainment to polyrhythms: a comparison of musicians and non-musicians. Front Neurosci 11:208.  https://doi.org/10.3389/fnins.2017.00208 CrossRefPubMedPubMedCentralGoogle Scholar
  44. Tal I, Large EW, Rabinovitch E, Wei Y, Schroeder CE, Poeppel D, Zion Golumbic E (2017) Neural entrainment to the beat: the “missing-pulse” phenomenon. J Neurosci 37(26):6331–6341CrossRefPubMedPubMedCentralGoogle Scholar
  45. Town SM, Bizley JK (2013) Neural and behavioral investigations into timbre perception. Front Syst Neurosci 7:88.  https://doi.org/10.3389/fnsys.2013.00088 CrossRefPubMedPubMedCentralGoogle Scholar
  46. van Noorden L, Moelants D (1999) Resonance in the perception of musical pulse. J New Music Res 28:43–66CrossRefGoogle Scholar
  47. Wang X, Lu T, Bendor D, Bartlett E (2008) Neural coding of temporal information in auditory thalamus and cortex. Neuroscience 157(2):484–494CrossRefPubMedGoogle Scholar
  48. Zhou H, Melloni L, Poeppel D, Ding N (2016) Interpretations of frequency domain analyses of neural entrainment: periodicity, fundamental frequency, and harmonics. Front Hum Neurosci 10:274.  https://doi.org/10.3389/fnhum.2016.00274 PubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.The MARCS Institute for Brain, Behaviour and Development (WSU)SydneyAustralia
  2. 2.Institute of Neuroscience (Ions)Université catholique de Louvain (UCL)BrusselsBelgium
  3. 3.International Laboratory for Brain, Music and Sound Research (Brams)MontrealCanada
  4. 4.Neurology UnitCentre Hospitalier Régional Universitaire (CHRU) de NancyNancyFrance
  5. 5.MARCS Institute for Brain, Behaviour and DevelopmentWestern Sydney UniversityPenrithAustralia

Personalised recommendations