Skip to main content
Log in

Cortical correlations in wavelet domain for estimation of emotional dysfunctions

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

In the present study, the level of nonlinear inter-hemispheric synchronization has been estimated by using wavelet correlation (WC) method for detection of emotional dysfunctions. Due to non-stationary nature of EEG series in addition to the assumption that the high-frequency band is possibly associated with emotional activation, WC has been applied to five distinct frequency band activities (fba) (Delta: \(0.5{-}4\,\hbox {Hz}\), Theta: \(4{-}8\,\hbox {Hz}\), Alpha: \(8{-}16\,\hbox {Hz}\), Beta: \(16{-}32\,\hbox {Hz}\), Gamma: \(32{-}64\,\hbox {Hz}\)) embedded in non-averaged single-trial EEG series mediated by convenient affective pictures from International Affective Picture System. Experimental data were collected from both healthy controls and patients, diagnosed with first-episode psychosis, through a 16-channel EEG cap. WC estimations, which are computed for eight electrode pairs (pre-frontal, anterio-frontal, central, parietal, occipital, posterio-frontal, anterio-temporal, posterio-temporal), in accordance with each specified fba and emotional state (pleasant, un-pleasant, neutral) have been classified by using Least Squares Support Vector Machines with tenfold cross-validation to distinguish controls from patients. Results show that the highest classification accuracies of 88.06, 86.39, 83.89% are obtained in Gamma with respect to neutral, un-pleasant, and pleasant stimuli, respectively. In each group (controls and patients), the largest WCs are observed at anterio-frontal and central lobes; however, controls generate the high WC in response to pleasant stimuli, whereas the patients generate the high WC in response to neutral stimuli in Gamma. In conclusion, fronto-central lobes are the most activated brain regions during emotional stimulation by means of inter-hemispheric correlation. Gamma is the most sensitive fba to visual affective pictures. Emotional dysfunctions are found to be characterized by decreased WC in pleasant state, increased WC in neutral state in Gamma.

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

Access this article

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Chang C, Glove GH (2010) Time--frequency dynamics of resting-state brain connectivity measured with fMRI. NeuroImage 50(1):81–98

    Article  Google Scholar 

  2. Aydın S, Demirtaş S, Tunga M, 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 

  3. Lachaux JP, Lutz A, Rudrauf D et al (2002) Estimating the time-course of coherence between single-trial brain signals: an introduction to wavelet coherence. Clin Neurophysiol 32(3):157–174

    Article  Google Scholar 

  4. Klein A, Sauer T, Jedynak A, Skrandies W (2006) Conventional and wavelet coherence applied to sensoryevoked electrical brain activity. IEEE Trans BME 53(2):266–272

    Article  Google Scholar 

  5. Wyczesany M, Grzybowski SJ, Barry RJ et al (2011) Covariation of EEG synchronization and emotional state as modified by anxiolytics. J Clin Neurophysiol 28(3):289–296

    Article  Google Scholar 

  6. Miskovic V, Schmidt LA (2010) Cross-regional cortical synchronization during affective image viewing. Brain Res 1362:102–111

    Article  Google Scholar 

  7. Martini N, Menicucci D, Sebastianietal L (2012) The dynamics of EEG gamma responses to unpleasant visual stimuli: from local activity to functional connectivity. NeuroImage 60(2):922–932

    Article  Google Scholar 

  8. Aydin SG, Kaya T, Guler H (2016) Wavelet-based study of valencearousal model of emotions on EEG signals with LabVIEW. Brain Inf 3(2):109–117

    Article  Google Scholar 

  9. Mohammadi Z, Frounchi J, Amiri M (2016) Wavelet-based emotion recognition system using EEG signal. Neural Comput Appl 1–6. doi:10.1007/s00521-015-2149-8

    Google Scholar 

  10. Lang PJ, Bradley MM, Cuthbert BN (1999) International affective picture system (IAPS): instruction manual and affective ratings. The Center for Research in Psychophysiology, University of Florida, Florida, A-4

  11. Koukkou M, Federspiel A, Braker E et al (2000) An EEG approach to the neuro developmental hypothesis of schizophrenia studying schizophrenics, normal controls and adolescents. J Psychiatry Res 34:57–73

    Article  Google Scholar 

  12. Koenig T, Lehmann D, Saito N et al (2001) Decreased functional connectivity of EEG theta-frequency activity in first-episode, neurolepticnaive patients with schizophrenia: preliminary results. Schizophr Res 50:55–60

    Article  Google Scholar 

  13. Oknina LB, Wild-Wall N, Oades RD et al (2005) Frontal and temporal sources of mismatch negativity in healthy controls, patients at onset of schizophrenia in adolescence and others at 15 years after onset. Schizophr Res 76:25–41

    Article  Google Scholar 

  14. Alexander DM, Flynn GJ, Wong W et al (2009) Spatio-temporal EEG waves in first episode schizophrenia. Clin Neurophysiol 120:1667–82

    Article  Google Scholar 

  15. Sols-Vivanco R, Mondragn-Maya A, Len-Ortiz P et al (2014) Mismatch negativity reduction in the left cortical regions in first-episode psychosis and in individuals at ultra high-risk for psychosis. Schizophrenia 158(1–3):58–63

    Article  Google Scholar 

  16. Missonnier P, Herrmann FR, Zanello A et al (2012) Event-related potentials and changes of brain rhythm oscillations during working memory activation in patients with first-episode psychosis. J Psychiatry 37(2):95–105

    Google Scholar 

  17. Garakh Z, Zaytseva Y, Kapranova A, Fiala O et al (2015) EEG correlates of a mental arithmetic task in patients with first episode schizophrenia and schizoaffective disorder. Clin Neurophysiol 126(11):2090–2098

    Article  Google Scholar 

  18. Balconi M, Mazza G (2009) Brain oscillations and BIS/BAS (behavioralinhibition/activationsystem) effects on processing masked emotional cues: ERS/ERD and coherence measures of alpha band. Int J Psychophysiol 74(2):158–165

    Article  Google Scholar 

  19. Davidson RJ (1992) Anterior cerebral asymmetry and the nature of emotion. Brain Cogn 20(1):125–151

    Article  Google Scholar 

  20. Gotlib IH, Ranganath C, Rosenfeld JP (1998) Frontal EEG alpha asymmetry, depression, and cognitive functioning. Cogn Emot 12(3):449–478

    Article  Google Scholar 

  21. Balconi M, Lucchiari C (2008) Consciousness and arousal effects on emotional face processing as revealed by brain oscillations. A gamma band analysis. Int J Psychophysiol 67(1):41–46

    Article  Google Scholar 

  22. Keil A, Muller MM, Gruber T et al (2001) Effects of emotional arousal in the cerebral hemispheres: a study of oscillatory brain activity and event related potentials. Clin Neurophysiol 112(11):2057–2068

    Article  Google Scholar 

  23. Muller MM, 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 

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

    Article  Google Scholar 

  25. Balconi M, Lucchiari C (2006) EEG correlates (event-related desynchronization) of emotional face elaboration: a temporal analysis. Neurosci Lett 392(1–2):118–123

    Article  Google Scholar 

  26. Davidson PR, Jones RD, Peiris MT (2007) EEG-based lapse detection with high temporal resolution. IEEE Trans BME 54(5):832–839

    Article  Google Scholar 

  27. Johnston PJ, Devir H, Karayanidis F (2006) Facial emotion processing in schizophrenia: no evidence for a deficit specific to negative emotions in a differential deficit design. Psychiatry Res 143(1):51–61

    Article  Google Scholar 

  28. Petrantonakis PC (2009) Emotion recognition from EEG using higher order crossings. IEEE Trans Inf Techonol Biomed 14(2):186–197

    Article  Google Scholar 

  29. Peyk P, Schupp HT, Elbert T et al (2008) Emotion processing in the visual brain: an MEG analysis. Brain Topogr 20(4):205–215

    Article  Google Scholar 

  30. Vytal K, Hamann S (2010) Neuroimaging support for discrete neural correlates of basic emotions: a voxel-based meta analysis. J Cogn Neurosci 22(12):2864–2885

    Article  Google Scholar 

  31. Kisley MA, Cornwell MA (2006) Gamma and beta neural activity evoked during a sensory gating paradigm: Effects of auditory, somatosensory and cross-modal stimulation. Clin Neurophysiol 117(11):2549–63

    Article  Google Scholar 

  32. Melloni L, Molina C, Pena M, Torres D, Singer W, Rodriguez E (2007) Synchronization of neural activity across cortical areas correlates with conscious perception. J Neurosci 27(11):2858–2865

    Article  Google Scholar 

  33. Doesburg SM, Roggeveen AB, Kitajo K, Ward LM (2008) Large-scale gamma band activity phase synchronization and selective attention. Cereb Cortex 18(2):386–396

    Article  Google Scholar 

  34. Fries P (2009) Neuronal gamma band activity synchronization as a fundamental process in cortical computation. Annu Rev Neurosci 32:209–224

    Article  Google Scholar 

  35. Buzsaki G, Schomburg EW (2015) What does gamma coherence tell us about inter-regional neural communication. Nat Neurosci 18(4):484–489

    Article  Google Scholar 

  36. Tomarken AJ, Davidson RJ, Wheeler RE, Kinney L (1992) Psychometric properties of resting anterior EEG asymmetry: temporal stability and internal consistency. Psychophysiology 29:576–592

    Article  Google Scholar 

  37. Harmon-Jones E, Allen JJB et al (1997) Behavioral activation sensitivity and resting frontal EEG asymmetry: co variation of putative indicators related to risk for mood disorders. J Abnorm Psychol 106:159–163

    Article  Google Scholar 

  38. Kay SR, Fiszbein A, Opler LA (1987) The positive and negative syndrome scale (PANSS) for schizophrenia. Schizophr Bull 13(2):261–76

    Article  Google Scholar 

  39. Kay SR (1991) Positive and negative syndromes in schizophrenia. Routledge Mental Health 33–36

  40. Hunsley J, Mash EJ (2008) A guide to assessments that work. Oxford University Press, Oxford. ISBN: 0-19-531064-0

  41. Bradley MM, Codispoti M, Cuthbert BN, Lang PJ (2001) Emotion and motivation I: defensive and appetitive reactions in picture processing. Emotion 1(3):276–98

    Article  Google Scholar 

  42. Bradley MM, Codispoti M, Cuthbert BN, Lang PJ (2001) Emotion and motivation II: defensive and appetitive reactions in picture processing. Emotion 1(3):300–19

    Article  Google Scholar 

  43. Junghfer M, Elbert T, Tucker D (2000) Statistical control of artifacts in dense array EEG/MEG studies. Psychophysiology 37:523532

    Google Scholar 

  44. Roebuck A, Monasterio V, Gederi E, Osipov M, Behar J, Malhotra A et al (2014) A review of signals used in sleep analysis. Physiol Meas 35(1):R1–57

    Article  Google Scholar 

  45. 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 1:01500

    Article  Google Scholar 

  46. 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 

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

    Article  Google Scholar 

  48. 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 

  49. Li X, Yao X, Fox J, Jefferys JG (2007) Interaction dynamics of neuronal oscillations analyzed using wavelet transforms. J Neurosci Methods 160:178–185

    Article  Google Scholar 

  50. Rowley AB, Payne SJ, Tachtsidis I et al (2007) Synchronization between arterial blood pressure and cerebral oxyhaemoglobin concentration investigated by wavelet cross-correlation. Physiol Meas 28(2):161–173

    Article  Google Scholar 

  51. Maraun D, Kurths J (2004) Cross wavelet analysis: significance testing and pitfalls. Nonlinear Process Geophys 11:505–514

    Article  Google Scholar 

  52. Grinsted A, Moore JC, Jevrejeva S (2004) Application of the cross wavelet transform and wavelet coherence to geophysical time series. Nonlinear Process Geophys 11:561–6

    Article  Google Scholar 

  53. De Carli F, Nobili L, Beelke M, Watanabe T et al (2004) Quantitative analysis of sleep EEG microstructure in the time–frequency domain. Brain Res Bull 63:399–405

    Article  Google Scholar 

  54. Rezaei MA, Abdolmaleki P, Karami Z et al (2008) Prediction of membrane protein types by means of wavelet analysis and cascaded neural networks. J Theor Biol 254:817–820

    Article  Google Scholar 

  55. Cao J, Lin Z, Huang G (2010) Composite function wavelet neural networks with extreme learning machine. Neurocomputing 73:1405–1416

    Article  Google Scholar 

  56. Westin J, Ghiamati S, Memedi M et al (2010) A new computer method for assessing drawing impairment in Parkinsons disease. J Neurosci Methods 190:143–148

    Article  Google Scholar 

  57. 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 

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

    Article  Google Scholar 

  59. Vapnik V (1995) The nature of statistical learning theory. Springer, New York

    Book  MATH  Google Scholar 

  60. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297

    MATH  Google Scholar 

  61. Castillo E, Peteiro-Barral D et al (2015) Distributed one-class support vector machine. Int J Neural Syst 25(7):1550029

    Article  Google Scholar 

  62. Chou JS, Pham AD (2015) Smart artificial firefly colony-based support vector regression for enhanced forecasting in civil engineering. Comput Aided Civ Infrastruct Eng 30(9):715–732

    Article  Google Scholar 

  63. Zhang Y, Zhou W (2015) Multifractal analysis and relevance vector machine-based automatic seizure detection in intracranial. Int J Neural Syst 25(6):1550020

    Article  Google Scholar 

  64. Petrantonakis PC, Hadjileontiadis LJ (2010) Emotion recognition from EEG using higher order crossings. IEEE Trans Inf Technol Biomed 14(2):186–197

    Article  Google Scholar 

  65. Zheng J, Huang L, Zhao J (2012) Energy fetaure extraction and SVM classification of motor imagery-induced electroencephalograms. J Innov Opt Health Sci 05(02):1250006

    Article  Google Scholar 

  66. Martis RJ, Tan JH, Chua CK et al (2015) Epileptic EEG classification using nonlinear parameters on different frequency bands. J Mech Med Biol 15(03):1550040

    Article  Google Scholar 

  67. 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 

  68. Makeig S, Onton J (2009) ERP features and EEG dynamics: an ICA perspective. In: Oxford handbook of event-related potential components. Oxford University Press, New York, pp 1–51

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

    Article  Google Scholar 

  70. Buzski G, Schomburg EW (2015) What does gamma coherence tell us about inter-regional neural communication? Nat Neurosci 18(4):484489

    Google Scholar 

  71. Coan JA, Allen JJB (2004) Frontal EEG asymmetry as a moderator and mediator of emotion. Biol Psychol 67:749

    Article  Google Scholar 

  72. Adolphs R (2002) Neural systems for recognizing emotion. Curr Opin Neurobiol 12(2):169–177

    Article  Google Scholar 

  73. Keil A, Bradley MM et al (2002) Large-scale neural correlates of affective picture processing. Psychophysiology 39:641–649

    Article  Google Scholar 

  74. Bostanov V (2004) BCI competition 2003-data sets Ib and IIb: feature extraction from event-related brain potentials with the continuous wavelet transform and the t-value scalogram. IEEE Trans BME 51:1057–1061

    Article  Google Scholar 

  75. Blankertz B, Muller K-R, Curio G, Vaughan T et al (2004) The BCI competition 2003: progress and perspectives in detection and discrimination of EEG single trials. IEEE Trans BME 51:1044–1051

    Article  Google Scholar 

  76. Blankertz B, Mller KR, Krusienski D, Schalk G et al (2006) The BCI competition III: validating alternative approaches to actual BCI problems. IEEE Trans Neural Syst Rehabil Eng 14:153–159

    Article  Google Scholar 

  77. Valenzi S, Islam T, Jurica P et al (2014) Individual classification of emotions using EEG. Biomed Sci Eng 7:604–620

    Article  Google Scholar 

  78. Jirayucharoensak S, Ngum SP, Israsena P (2014) EEG-based emotion recognition using deep learning network with principal component based covariate shift adaptation. Sci World J. doi:10.1155/2014/627892

    Google Scholar 

  79. Davidson RJ (1993) Cerebral asymmetry and emotion. Concept Methodol Conundrums Cogn Emot 7:115–138

    Article  Google Scholar 

  80. Lee AL, Ogle WO, Sapolsky RM (2002) Stress and depression: possible links to neuron death in the hippocampus. Bipolar Disord 4:117–128

    Article  Google Scholar 

  81. Rotenberg VS (2004) The peculiarity of the right-hemisphere function in depression: solving the paradoxes. Prog. Neuropsychopharmacol Biol Psychiatry 28:1–13

    Article  Google Scholar 

  82. 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 

  83. Leuchter AF, Cook IA, Hunter AM, Cai C, Horvath S (2012) Resting state quantitative EEG reveals increased neurophysiologic connectivity in depression. PLoS One 7:32508

    Article  Google Scholar 

  84. Jackie K, Gollan D (2014) Frontal alpha EEG asymmetry before and after behavioral activation treatment for depression. Biol Psychol 99:198–208

    Article  Google Scholar 

  85. Debener S, Beauducel A, Nessler D et al (2000) Is resting anterior EEG alpha asymmetry a trait marker for depression? Findings for healthy adults and clinically depressed patients. Neuropsychobiology 41(1):31–37

    Article  Google Scholar 

  86. Li Y, Zhou H, Chen J, Huang J, Chen M et al (2016) Human emotion variation analysis based on EEG signal and POMS scale. Brain Inf Health, The series of Lecture Notes in Computer Science 9919:75–84

  87. Machinskaya RI, Rozovskaya RI, Kurgansky AV, Pechenkova EV (2016) Cortical functional connectivity during the retention of affective pictures in working memory: EEG-source theta coherence analysis. Hum Physiol 42(3):279–293

    Article  Google Scholar 

Download references

Acknowledgements

Authors thank to 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, for providing experimental data and selecting affective pictures as visual stimuli.

Author information

Authors and Affiliations

Authors

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 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, 8170. Unpleasant pictures: 1111, 3185, 3195, 3213, 3550.1, 6312, 6313, 6520, 7359, 8230, 9043, 9075, 9291, 9300, 9413, 9560, 9600, 9940. Neutral pictures: 2026, 2102, 2273, 2377, 2411, 2512, 7001, 7002, 7004,7009, 7014, 7019, 7032, 7050, 7052, 7081, 7179, 7211.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Aydın, S., Demirtaş, S. & Yetkin, S. Cortical correlations in wavelet domain for estimation of emotional dysfunctions. Neural Comput & Applic 30, 1085–1094 (2018). https://doi.org/10.1007/s00521-016-2731-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-016-2731-8

Keywords

Navigation