Skip to main content

Comparison of hemispheric asymmetry measurements for emotional recordings from controls

Neural Computing and Applications volume 30, pages 1341–1351 (2018)Cite this article

Abstract

Four asymmetry measurements (conventional coherence function (CCF), cross wavelet correlation (CWC), phase lag index (PLI), and mean phase coherence (MPC)) have been compared to each other for the first time in order to recognize emotional states (pleasant (P), neutral (N), unpleasant (UP)) from controls in EEG sub-bands (delta (0–4 Hz), theta (4–8 Hz), alpha (8–16 Hz), beta (16–32 Hz), gamma (32–64 Hz)) mediated by affective pictures from the International Affective Picture Archiving System (IAPS). Eight emotional features, computed as hemispheric asymmetry between eight electrode pairs (Fp1 − Fp2, F7 − F8, F3 − F4, C3 − C4, T7 − T8, P7 − P8, P3 − P4, and O1 − O2), have been classified by using data mining methods. Results show that inter-hemispheric emotional functions are mostly mediated by gamma. The best classification is provided by a neural network classifier, while the best features are provided by CWC in time-scale domain due to non-stationary nature of electroencephalographic (EEG) series. The highest asymmetry levels are provided by pleasant pictures at mostly anterio-frontal (F3 − F4) and central (C3 − C4) electrode pairs in gamma. Inter-hemispheric asymmetry levels are changed by each emotional state at all lobes. In conclusion, we can state the followings: (1) Nonlinear and wavelet transform-based methods are more suitable for characterization of EEG; (2) The highest difference in hemispheric asymmetry was observed among emotional states in gamma; (3) Cortical emotional functions are not region-specific, since all lobes are effected by emotional stimuli at different levels; and (4) Pleasant stimuli can strongly mediate the brain in comparison to unpleasant and neutral stimuli.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Fig. 1

References

  1. David O, Kilner JM, Friston KJ (2006) Mechanisms of evoked and induced responses in MEG/EEG. NeuroImage 31:1580–1591

    Article  Google Scholar 

  2. Galambos R (1992) Induced rhythms in the brain. Birkhauser, Basel

  3. Tallon BC, Bertrand O (1999) Oscillatory gamma activity in humans and its role in object representation. Trends Cogn Sci 3(4):151–162

    Article  Google Scholar 

  4. Singer W, Kreiter AK, Engel AK, Fries P, Roelfsema PR, Volgushev M (1996) Precise timing of neuronal discharges within and across cortical areas: implications for synaptic transmission. J Physiol Paris 90 (3–4):221–222

    Article  Google Scholar 

  5. Singer W (2011) Dynamic formation of functional networks by synchronization. Neuron 69(2):191–193

    Article  Google Scholar 

  6. Aftanas LI, Varlamov AA, Pavlov SV, Makhnev VP, Reva NV (2002) Time-dependent cortical asymmetries induced by emotional arousal: EEG analysis of event-related synchronization and desynchronization in individually defined frequency BAs. Int J Psychophysiol 44(1):67–82

    Article  Google Scholar 

  7. Keil A, Bradley MM, Hauk O, Rockstroh B, Elbert T, Lang PJ (2002) Large-scale neural correlates of affective picture processing. Psychophysiology 39:641–649

    Article  Google Scholar 

  8. Coan JA, Allen JJB (2004) Frontal EEG asymmetry as a moderator and mediator of emotion. Biol Psychol 67:7–49

    Article  Google Scholar 

  9. Kisley MA, Cornwell ZM (2006) Gamma and beta neural activity evoked during a sensory gating paradigm: effects of auditory, somatosensory and cross-modal stimulation. Clin Neurophysiol 117(11):2549–2563

    Article  Google Scholar 

  10. Yu K, Prasad I, Mir H, Thakor N, Al-Nashash H (2015) Cognitive workload modulation through degraded visual stimuli: a single trial EEG study. J Neural Eng 12(4):046020

    Article  Google Scholar 

  11. Acqualagna L, Bosse S, Porbadnigk AK, Curio G, Müller KR, Wiegand T, Blankertz B (2015) EEG-based classification of video quality perception using steady state visual evoked potentials. J Neural Eng 12 (2):026012

    Article  Google Scholar 

  12. Kozma R, Freeman WJ (2002) Classification of EEG patterns using nonlinear dynamics and identifying chaotic phase transitions. Neurocomputing 44-46:1107–1112

    Article  MATH  Google Scholar 

  13. Koelstra S, Yazdani A, Soleymani M, Mühl C, Lee J-S, Nijholt A, Pun T, Ebrahimi T, Patras I (2010) Single trial classifications of EEG and peripheral physiological signals for recognition of emotions induced by music videos. Brain Informatics Chapter of Series Lecture Notes in Computer Sciences 6334:89–100

    Google Scholar 

  14. Ceballos GA, Hernaindez LF (2015) Non-target adjacent stimuli classification improves performance of classical ERP-based brain computer interface. J Neural Eng 12:026009

    Article  Google Scholar 

  15. Aydın S (2011) Computer based synchronization analysis on sleep EEG in insomnia. J Med Syst 35(4):517–520

    Article  Google Scholar 

  16. Aydın S, Arıca N, Ergul E, Tan O (2015) Classification of obsessive compulsive disorder by EEG complexity and hemispheric dependency measurements. Int J Neural Syst 25(3):155001

    Google Scholar 

  17. Teixeira AR, Tome AM, Böhm M, Puntonet CG, Lang EW (2009) How to apply nonlinear subspace techniques to univariate biomedical time series. IEEE Trans Instrum Meas 58(8):2433–2443

    Article  Google Scholar 

  18. Lang PJ, Bradley MM, Cuthbert BN (1999) International Affective Picture System (IAPS): instruction manual and affective ratings. Florida: the Center for Research in Psychophysiology, University of Florida, pp A–4

  19. Mima T, Matsuoka T, Hallett M (2000) Functional coupling of human right and left cortical motor areas demonstrated with partial coherence analysis. Neurosci Lett 287:93–96

    Article  Google Scholar 

  20. Nolte G, Wheaton OBL, Mari Z, Vorbach S, Hallett M (2004) Identifying true brain interaction from EEG data using the imaginary part of coherency. Clin Neurophysiol 115:2292–2307

    Article  Google Scholar 

  21. Stam CJ, Nolte G, Daffertshofer A (2007) Phase lag index: assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources. Hum Brain Mapp 28:1178–1193

    Article  Google Scholar 

  22. Tass P, Rosenblum MG, Weule J, Kurths J, Pikovsky AS, Volkmann J, Schnitzler A, Freund HJ (1998) Detection of n:m phase locking from noisy data: application to magnetoencephalography. Phys Rev Lett 81:3291–3294

    Article  Google Scholar 

  23. Mormann F, Lehnertz K, David P, Elger CE (2000) Mean phase coherence as a measure for phase synchronization and its application to the EEG of epilepsy patients. Physica D 144:358–369

    Article  MATH  Google Scholar 

  24. Rosenblum MG, Kurths J (1998) Analysing synchronization phenomena from bivariate data by means of the Hilbert transform. Nonlinear analysis of physiological data. Springer, Berlin

    MATH  Google Scholar 

  25. Grinsted A, Moore JC, Jevrejeva S (2004) Application of the cross wavelet transform and wavelet coherence to geophysical time series. Nonlinear Processes Geophys 11:561–566

    Article  Google Scholar 

  26. Lachaux JP, Rodriguez E, Martinerie J, Varela FJ (1999) Measuring phase synchrony in brain signals. Hum Brain Mapp 8(4):194–208

    Article  Google Scholar 

  27. Lachaux JP, Lutz A, Rudrauf D, Cosmelli D, Quyen MLV, Martinerie J, Varela F (2002) Estimating the time-course of coherence between single-trial brain signals: an introduction to wavelet coherence. Neurophysiol Clin 32:157–174

    Article  Google Scholar 

  28. Rulkov N, Sushchik M, Tsimring L, Abarbanel H (1995) Generalized synchronization of chaos in directionally coupled chaotic systems. Phys Rev E 51:980–994

    Article  Google Scholar 

  29. Schiff S, So P, Taeun C, Burke R, Sauer T (1997) Detecting dynamical interdependence and generalized synchrony through mutual prediction in a neural ensemble. Phys Rev E 54:6708–6724

    Article  Google Scholar 

  30. Roebuck A, Monasterio V, Gederi E, Osipov M, Behar J, Malhotra A, Penzel T, Clifford GD (2014) A review of signals used in sleep analysis. Physiol Meas 35(1):R1–R57

    Article  Google Scholar 

  31. Jamal W, Das S, Maharatna K, Apicella F, Chronaki G, Sicca F, Cohen D, Muratori F (2015) On the existence of synchrostates in multichannel EEG signals during face-perception tasks. Biomed Phys Eng Express 1:015002

    Article  Google Scholar 

  32. Bai O, Lin P, Vorbach S, Floeter MK, Hattori N, Hallett M (2008) A high performance sensorimotor beta rhythm-based brain-computer interface associated with human natural motor behavior. J Neural Eng 5(1):24–35

    Article  Google Scholar 

  33. Muller M, Keil A, Gruber T, Elbert T (1999) Processing of affective pictures modulates right-hemispheric gamma band EEG activity. Clin Neurophysiol 110(11):1913–1920

    Article  Google Scholar 

  34. Keil A, Müller MM, Gruber T, Wienbruch C, Stolarova M, Elbert T (2001) Effects of emotional arousal in the cerebral hemispheres: a study of oscillatory brain activity and event-related potentials. Clin Neurophysiol 112:2057–2068

    Article  Google Scholar 

  35. Spinnato J, Roubaud MC, Burle B, Torresani B (2015) Detecting single-trial EEG evoked potential using a wavelet domain linear mixed model: application to error potentials classification. J Neural Eng 1:036013

    Article  Google Scholar 

  36. Cubero JA, Gan JQ, Palaniappan R (2013) Multiresolution analysis over simple graphs for brain computer interfaces. J Neural Eng 046014:10

    Google Scholar 

  37. Yang B, Yan GZ, Yan R, Wu T (2006) Feature extraction for EEG-based brain-computer interfaces by wavelet packet best basis decomposition. J Neural Eng 3:251–256

    Article  Google Scholar 

  38. Wang D, Miao D, Xie C (2011) Best basis-based wavelet packet entropy feature extraction and hierarchical EEG classification for epileptic detection. Expert Syst Appl 38:14314–14320

    Google Scholar 

  39. Samar VJ, Bopardikar A, Rao R, Swartz K (1999) Wavelet analysis of neuroelectric waveforms: a conceptual tutorial. Brain Lang 66(1):7–60

    Article  Google Scholar 

  40. Klein A, Sauer T, Jedynak A, Skrandies W (2006) Conventional and wavelet coherence applied to sensory-evoked electrical brain activity. IEEE Trans Biomed Eng 53:266–272

    Article  Google Scholar 

  41. Zhana Y, Hallidaya D, Jiange P, Liu X, Feng J (2006) Detecting time-dependent coherence between non-stationary electrophysiological signals—a combined statistical and time-frequency approach. J Neurosci Methods 156(1–2):322–332

    Article  Google Scholar 

  42. Rosenblum M, Pikovsky A, Kurths J, Schafer C, Tass PA (2001) Phase synchronization: from theory to data analysis. Handbook of biological physics neuro-informatica. Elsevier, Amsterdam

    Google Scholar 

  43. Quyen MLV, Foucher J, Lachaux JP, Rodriguez E, Lutz A, Martinerie J, Varela FJ (2001) Comparison of Hilbert transform and wavelet methods for the analysis of neuronal synchrony. J Neurosci Methods 111(2):83–98

    Article  Google Scholar 

  44. Quiroga Q, Kraskov A, Kreuz T, Grassberger P (2002) Performance of different synchronization measures in real data: a case study on electroencephalographic signals. Phys Rev E 041903 :65

    Google Scholar 

  45. Chavez M, Quyen MLV, Navarro V, Baulac M, Martinerie J (2003) Spatio-temporal dynamics prior to neocortical seizures: amplitude versus phase couplings. IEEE Trans on BME 50(5):571–583

    Article  Google Scholar 

  46. Poil S-S, de Haan W, van der Flier WM, Mansvelder HD, Scheltens P (2013) Linkenkaer-Hansen K. integrative EEG biomarkers predict progression to Alzheimer’s disease at the MCI stage. Front Aging Neurosci 5:58

  47. Mohammadi M, Al-Azab F, Raahemi B, et al. (2015) Data mining EEG signals in depression for their diagnostic value. BMC Med Inform Decis Mak 15:108

    Article  Google Scholar 

  48. Aydın S, Demirtasş S, Tunga MA, Ateş K (2016) Emotion recognition with eigen features of frequency band activities embedded in induced brain oscillations mediated by affective pictures. Int J Neural Syst 26(3):1650013

    Article  Google Scholar 

  49. Frank E, Witten IH (2005) Data mining: practical machine learning tools and techniques. Morgan Kaufmann, San Francisco

  50. Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The WEKA data mining software: an update. SIGKDD Explorations 11(1):10–18

    Article  Google Scholar 

  51. Chanel G, Kronegg J, Grandjean D, Pun T (2006) Emotion assessment: arousal evaluation using EEGs and peripheral physiological signals Proceedings of multimedia content representation classification and security, pp 530–537

    Chapter  Google Scholar 

  52. Li M, Lu B (2009) Emotion classification based on gamma band activity of EEG Proceedings of international conference of the IEEE engineering in medicine and biology society, pp 1223– 1226

    Google Scholar 

  53. Zhang Q, Lee M (2009) Analysis of positive and negative emotions in natural scene using brain activity and gist. Neurocomputing 72:1302–1306

    Article  Google Scholar 

  54. Engel AK, Fries P, Singer W (2001) Dynamic predictions: oscillations and synchrony in top-down processing. Nat Rev Neurosci 2:704–716

    Article  Google Scholar 

  55. Pesaran B, Pezaris JS, Sahani M, Mitra PP, Andersen RA (2002) Temporal structure in neuronal activity during working memory in macaque parietal cortex. Nat Neurosci 5:805–811

    Article  Google Scholar 

  56. Schnitzler A, Gross J (2005) Normal and pathological oscillatory communication in the brain. Nat Rev Neurosci 6:285– 296

    Article  Google Scholar 

  57. Fries P (2005) A mechanism for cognitive dynamics: neuronal communication through neuronal coherence. Trends Cogn Sci 9:474–479

    Article  Google Scholar 

  58. Gray CM, König P, Engel AK, Singer W (1989) Oscillatory responses in cat visual cortex exhibit inter-columnar synchronization which reflects global stimulus properties. Nature 338:334–337

    Article  Google Scholar 

  59. Llinas RR, Ribary U (1992) Rostrocaudal scan in human brain: a global characteristic of the 40-Hz response during sensory. Induced rhythms in the brain. Birkhauser, Boston

  60. Güntekin B, Başar E (2014) A review of brain oscillations in perception of faces and emotional pictures. Neuropsychologia 58:33–51

    Article  Google Scholar 

  61. Li Y, Cao D, Wei L, Tang Y, Wang J (2015) Abnormal functional connectivity of EEG gamma band in patients with depression during emotional face processing. Clin Neurophysiol 126(11):2078–2089

    Article  Google Scholar 

  62. Taylor SF, Liberzon I, Koeppe RA (2000) The effect of graded aversive stimuli on limbic and visual activation. Neuropsychologia 38:1415–1425

    Article  Google Scholar 

  63. Oya H, Kawasaki H, Howard MA, Adolphs R (2002) Electrophysiological responses in the human amygdala discriminate emotion categories of complex visual stimuli. J Neurosci 22:9502–9512

    Article  Google Scholar 

  64. Luo Q, Holroyd T, Jones M, Hendler T, Blair J (2007) Neural dynamics for facial threat processing as revealed by gamma band synchronization using MEG. Neuroimage 34:839–847

    Article  Google Scholar 

  65. Matsumoto A, Ichikawa Y, Kanayama N, Ohira H, Iidaka T (2006) Gamma band activity and its synchronization reflect the dysfunctional emotional processing in alexithymic persons. Psychophysiology 43 (6):533–540

    Article  Google Scholar 

  66. Muller MM, Gruber T, Keil A (2000) Modulation of induced gamma band activity in the human EEG by attention and visual information processing. Int J Psychophysiol 28:283–299

    Article  Google Scholar 

  67. Nunez PL, Srinivasan R (2006) Electric fields of the brain: the neurophysics of EEG, 2nd edn. Oxford University Press, Oxford, p 611

    Book  Google Scholar 

Download references

Acknowledgements

For collecting experimental EEG data and selecting visual stimuli from affective pictures, the authors thank Prof. Dr. Cüneyt Göksoy and his staff (in Department of Biophysics) and Psychiatrist Taner Öznur (in Department of Mental Health and Disease) at Faculty of Medicine in University of Health Sciences.

Author information

Authors and Affiliations

  1. Biomedical Engineering Department, Bahçeşehir University, Beşiktaş, 34353, Istanbul, Turkey

    Serap Aydın

  2. Department of Biophysics, Faculty of Medicine, University of Health Sciences, 06010, Ankara, Turkey

    Serdar Demirtaş

  3. Software Engineering Department, Bahçeşehir University, Beşiktaş, 34353, Istanbul, Turkey

    M. Alper Tunga

  4. Department of Biophysics, Gülhane Military Medical Academy, 06010, Ankara, Turkey

    Kahraman Ateş

Authors
  1. Serap Aydın
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Serdar Demirtaş
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. M. Alper Tunga
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kahraman Ateş
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Serap Aydın.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Appendix

Appendix

In the present study, several pictures were selected from the IAPS as emotional stimuli as follows: Adaptation (Neutral) pictures: 2745 and 2191. Pleasant pictures: 1440, 1460, 1610, 1710, 1920, 2035, 2071, 2311, 2347, 2550, 4626, 5210, 5621, 5760, 5780, 5833, 7330, and 8170. Unpleasant pictures: 1111, 3185, 3195, 3213, 3550.1, 6312, 6313, 6520, 7359, 8230, 9043, 9075, 9291, 9300, 9413, 9560, 9600, and 9940. Neutral pictures: 2026, 2102, 2273, 2377, 2411, 2512, 7001, 7002, 7004,7009, 7014, 7019, 7032, 7050, 7052, 7081, 7179, and 7211.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Aydın, S., Demirtaş, S., Tunga, M.A. et al. Comparison of hemispheric asymmetry measurements for emotional recordings from controls. Neural Comput & Applic 30, 1341–1351 (2018). https://doi.org/10.1007/s00521-017-3006-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-017-3006-8

Keywords

  • Gamma
  • Emotional state
  • Hemispheric asymmetry
  • Data mining