Advertisement

ERPs-Based Attention Analysis Using Continuous Wavelet Transform: the Bottom-up and Top-down Paradigms

  • Anastasia Karatzia
  • Despoina Petsani
  • Chrysoula Kaza
  • Christos-Rafail Argyriou
  • Anastasios Galanopoulos
  • Angeliki-Ιlektra Karaiskou
  • Pavlos Triantaris
  • Ioannis Xygonakis
  • Chrysa Papadaniil
  • Leontios J. HadjileontiadisEmail author
Conference paper
Part of the IFMBE Proceedings book series (IFMBE, volume 57)

Abstract

Evoked Related Potentials (ERPs) analysis for distinguishing between bottom-up and top-down attention, using 256-channel EEG signals obtained by measurements on humans, is investigated here. The three main ERPs, i.e., N170, P300, N400 were obtained after appropriate time windows selection for different response peaks and averaging EEG waveforms from all trials for each channel. Following that, Continuous Wavelet Transform (CWT) was applied on each ERP waveform and a vector of six morphological CWT-based features was constructed and fed to two well-known classifiers, i.e., SVM and k-NN. The selected ERPs were drawn from those channels where they are known to be observed more frequently. The experimental results have shown that P300 provides higher classification rates than N170 and N400, reaching a classification accuracy of 76%. Moreover, SVM and k-NN showed similar performance, with the latter being slightly more efficient. Finally, gender factorization of data contributed to a maximum classification accuracy of 80%. The proposed analysis paves the way for better understanding of the activity of the brain in different attention scenarios as reflected in the CWT domain, exploring the time-frequency characteristics of the related ERPs, contributing to the detection of potential attention disorders.

Keywords

Bottom-up Top-down ERPs CWT Classification 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Pashler H, Β (1998) The Psychology of Attention. Cambridge, MA: MIT PressGoogle Scholar
  2. 2.
    Corbetta M, Shulman G L (2002) Control of goal-directed and stimulus-driven attention in the brain. Nat. Rev. Neurosci. 3(3):215–229Google Scholar
  3. 3.
    Egeth H E, Yantis S (1997) Visual attention: control, representation, and time course. Annu. Rev. Psychol. 48:269–297Google Scholar
  4. 4.
    Treisman A M, Gelade G (1980) A feature - integration theory of attention Cognitive Psychol. 12:97–136Google Scholar
  5. 5.
    Bacon W F, Egeth HE (1994) Overriding stimulus-driven attentional capture. Percept. Psychophys. 55:485–496Google Scholar
  6. 6.
    Fabiani M, Gratton G, Coles M G (2007) Event-Related brain potentials, CambridgeGoogle Scholar
  7. 7.
    Eimer M (2011) The Face-Sensitive N170 Component of the Event-Related Brain Potential, LondonGoogle Scholar
  8. 8.
    Molinaro N, Markus C, Barber, Horacio A and Carreiras M (2009) On the functional nature of the N400: Contrasting effects related to visual word recognition and contextual semantic integration Cogn Neurosci. 1(1):1-7 DOI 10.1080/17588920903373952Google Scholar
  9. 9.
    Mackworth N H (1948) The breakdown of vigilance during prolonged visual search in the Quarterly Journal of Experimental Psychology 1:6-21Google Scholar
  10. 10.
    Herrmann C S, Knight R T (2001) Mechanisms of human attention: event-related potentials and oscillations Neuroscience and Biobehavioral Reviews 465-76Google Scholar
  11. 11.
    Hillyard S A, Anllo-Vento L (1999) Event-related brain potentials in the study of visual selective attention, California PsychophysiologyGoogle Scholar
  12. 12.
    Wykowska A, Schubö A (2010) On the temporal relation of top-down and bottom-up mechanisms during guidance of attention, Cogn Neurosci. 22(4):640-54 DOI 10.1162/jocn.2009.21222Google Scholar
  13. 13.
    Clark V P, Hillyard S A (1996) Spatial selective attention affects early extrastriate but not striate components of the visual evoked potential, Cogn Neurosci. 8:387–402Google Scholar
  14. 14.
    Corbetta M, Kincade J M, Ollinger J M, McAvoy M P, Shulman G L (2000) Voluntary orienting is dissociated from target detection in human posterior parietal cortex. Nat. Neurosci. 3:292–297Google Scholar
  15. 15.
    Demiralp T, Ademoglu A, Comerchero M, Polich J (2001) Wavelet Analysis of P3a and P3b. Brain Topogr. 13:251–267Google Scholar
  16. 16.
    Polich J (2007) Updating P300: An integrative theory of P3a and P3b. Clin. Neurophysiol. 118 2128-2124Google Scholar
  17. 17.
    Castellanos N P, Makarov V A (2006) Recovering EEG Brain Signals: Artifact Suppression with Wavelet Enhanced Independent Component Analysis, Neuroscience Methods vol.158, no. 2, pp.300-312Google Scholar
  18. 18.
    Jain S, Wadhwani A K (2012) Analysis of EEG Signals for Epilepsy and Seizure by decomposition with Wavelet Transform International Journal of Engineering and Advanced Technology (IJEAT) 1(4): 2249 – 8958Google Scholar
  19. 19.
    Johankhani P, Kodogiannis V, Revett K (2006) EEG Signal Classification Using Wavelet Feature Extraction and Neural Networks IEEE JVA06, 2006, pp 120–124Google Scholar
  20. 20.
    Hmeidi I, Hawashin B, Eyas El-Qawasmeh (2008) Performance of KNN and SVM classifiers on full word Arabic articles Journal Advanced Engineering Informatics archive 22(1):106-111Google Scholar
  21. 21.
    Prochazka A, Kukal J, Vysata O (2008) Wavelet Transform Use for Feature Extraction and EEG Signal Segments Classification IEEE ISCCSP Proc., Prague, 2008, pp 719-722Google Scholar
  22. 22.
    Bentin, S, McCarthy G, Perez E, Puce A, Allison T (1996) Electrophysiological studies of face perception in humans Cogn Neurosci. 8:551-565Google Scholar
  23. 23.
    Samar V J, Bopardikar A, Rao R, Swartz K (1999) Wavelet analysis of neuroelectric waveforms. Brain and Lang. 66(1):7-60Google Scholar
  24. 24.
    Kalayci T, Ozdamar O, Erdol N (1994) The use of wavelet transform as a preprocessor for the neural network detection of EEG spikes. IEEE Southeastcon ’94 Proc. pp. 1–3Google Scholar
  25. 25.
    Schiff S J, Heller J, Weinstein S L, Milton J (1994) Wavelet transforms and surrogate data for electroencephalographic spike and seizure detection. Electroencephalography and Clinical Neurophysiology 91:442–455Google Scholar
  26. 26.
    Tang Z W, Ishii N (1993) The recognition system with two channels at different resolution for detecting spike in Human’s EEG. IEICE Transactions on Information and Systems, E76-D(3):377–387Google Scholar
  27. 27.
    Raz J, Dickerson L, Turetsky B (1999) A wavelet packet model of evoked potentials. Brain and Lang. 66(1):61-88Google Scholar
  28. 28.
    Coles M G H, Rugg M D (1996) Event-related brain potentials: an introduction. Electrophysiology of Mind: Event-related Brain Potentials and Cognition, Oxford University PressGoogle Scholar
  29. 29.
    George N, Evans J, Fiori N, Davidoff J, Renault B (1996) Brain events related to normal and moderately scrambled faces. Cogn Brain Res 4(2):65-76Google Scholar
  30. 30.
    Sutton S, Braren M, Zubin J, John E (1965) Evoked potential correlates of stimulus uncertainty. Science 150(3700):1187-8Google Scholar
  31. 31.
    Polich J (2007) Updating P300: An integrative theory of P3a and P3b. Clin Neurophysiol 118(10): 2128-2148Google Scholar
  32. 32.
    Kutas M, Hillyard S A (1980) Event-related brain potentials to semantically inappropriate and surprisingly large words. Biol. Psychol. 11(2):99–116Google Scholar
  33. 33.
    Chavan A, Dr. Kolte M, Optimal Mother Wavelet for EEG signal processing, IJAREEIE 2(12):5959-5963Google Scholar
  34. 34.
    Sahibsingh A, Dudani (1976) The Distance-Weighted k-Nearest-Neighbor Rule IEEE Transactions on Systems, Man, and Cybernetics SMC-6(4):325-327 DOI 10.1109/TSMC.1976.5408784Google Scholar
  35. 35.
    M.A. Hearst M A, Dumais S T, Osman E, Platt J, Scholkopf B (1998) Support vector machines IEEE Intelligent Systems and their Applications 13(4):18-28 DOI: 10.1109/5254.708428Google Scholar
  36. 36.
    Calder A J et al. (2001) The Oxford Handbook of Face Perception. Oxford University PressGoogle Scholar
  37. 37.
    Hadjileontiadis L, Kompatsiaris I, Kosmidou V, Tsolaki A (2014) Brain source localization of MMN, P300 and N400: Aging and gender differences. Brain Res. 1603:32-49Google Scholar
  38. 38.
    Li L, Gratton C, Yao D, Knight R (2010) Role of Frontal and Parietal Cortices in the Control of Bottom-up and Top-down Attention in Humans, Brain Res. 1344:173-84. DOI 10.1016/j.brainres.2010.05.016Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Anastasia Karatzia
    • 1
  • Despoina Petsani
    • 1
  • Chrysoula Kaza
    • 1
  • Christos-Rafail Argyriou
    • 1
  • Anastasios Galanopoulos
    • 1
  • Angeliki-Ιlektra Karaiskou
    • 1
  • Pavlos Triantaris
    • 1
  • Ioannis Xygonakis
    • 1
  • Chrysa Papadaniil
    • 1
  • Leontios J. Hadjileontiadis
    • 1
    Email author
  1. 1.Department of Electrical and Computer EngineeringAristotle University of ThessalonikiThessalonikiGreece

Personalised recommendations