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Neural Computing and Applications

, Volume 31, Issue 3, pp 945–953 | Cite as

Fractal dimension methods to determine optimum EEG electrode placement for concentration estimation

  • Hossein Siamaknejad
  • Wei Shiung LiewEmail author
  • Chu Kiong Loo
Original Article
  • 129 Downloads

Abstract

In this study, fractal dimension approaches were used for analyzing EEG to determine the optimum electrode placement to distinguish between concentration and relaxation states. The EEG of participants was recorded from multiple electrode placements while under relaxed state and while in a state of heightened mental activity. Higuchi and Katz algorithms were applied to extract the fractal dimension FD indices of windowed segments of the EEG. These were then plotted in a graph, and a simple threshold was applied to best divide the indices of relaxation and concentration. The Higuchi algorithm was found to be better than Katz at distinguishing between relaxation and concentration, and P3 was found to be the best position to measure concentration, with P8 being a close contender.

Keywords

Fractal dimensions Electroencephalogram Medical signals Attention estimation 

Notes

Acknowledgements

This study was funded by University of Malaya High Impact Research (HIR) Grant UM.C/625/1/HIR/MOHE/FCSIT/10.

Compliance with ethical standards

Conflict of interest statement

We declare that we have no potential conflicts of interest.

References

  1. 1.
    Palmer ED, Finger S (2001) An early description of ADHD (inattentive subtype): Dr Alexander Crichton and mental restlessness (1798). Child Psychol Psychiatry Rev 6(02):66–73CrossRefGoogle Scholar
  2. 2.
    Lubar JF, Swartwood MO, Swartwood JN, O’Donnell PH (1995) Evaluation of the effectiveness of EEG neurofeedback training for ADHD in a clinical setting as measured by changes in TOVA scores, behavioral ratings, and WISC-R performance. Biofeedback Self Regul 20(1):83–99CrossRefGoogle Scholar
  3. 3.
    Cusenza M (2012) Fractal analysis of the EEG and clinical applications. Available at: https://www.openstarts.units.it/handle/10077/7394. Accessed Jul 2014 
  4. 4.
    Bojić T, Vuckovic A, Kalauzi A (2010) Modeling eeg fractal dimension changes in wake and drowsy states in humans preliminary study. J Theor Biol 262(2):214–222CrossRefzbMATHGoogle Scholar
  5. 5.
    Lutzenberger W, Elbert T, Birbaumer N, Ray WJ, Schupp H (1992) The scalp distribution of the fractal dimension of the EEG and its variation with mental tasks. Brain Topogr 5(1):27–34CrossRefGoogle Scholar
  6. 6.
    Wang Q, Sourina O, Nguyen MK (2011) Fractal dimension based neurofeedback in serious games. Vis Comput 27(4):299–309CrossRefGoogle Scholar
  7. 7.
    Jasper H (1958) Report of the committee on methods of clinical examination in electroencephalography. Electroencephalogr Clin Neurophysiol 10:370–375CrossRefGoogle Scholar
  8. 8.
    Sourina O, Wang Q, Liu Y, Nguyen MK (2013) Fractal-based brain state recognition from EEG in human computer interaction. In: Biomedical engineering systems and technologies. Springer, pp 258–272Google Scholar
  9. 9.
    Fuchs T, Birbaumer N, Lutzenberger W, Gruzelier JH, Kaiser J (2003) Neurofeedback treatment for attention-deficit/hyperactivity disorder in children: a comparison with methylphenidate. Appl Psychophysiol Biofeedback 28(1):1–12CrossRefGoogle Scholar
  10. 10.
    Gevensleben H, Holl B, Albrecht B, Schlamp D, Kratz O, Studer P, Wangler S, Rothenberger A, Moll GH, Heinrich H (2009) Distinct EEG effects related to neurofeedback training in children with ADHD: a randomized controlled trial. Int J Psychophysiol 74(2):149–157CrossRefGoogle Scholar
  11. 11.
    Thompson L, Thompson M, Reid A (2010) Neurofeedback outcomes in clients with Asperger's syndrome. Appl Psychophysiol Biofeedback 35(1):63–81CrossRefGoogle Scholar
  12. 12.
    Kouijzer MEJ, van Schie HT, de Moor JMH, Gerrits BJL, Buitelaar JK (2010) Neurofeedback treatment in autism. Preliminary findings in behavioral, cognitive, and neurophysiological functioning. Res Autism Spectr Disord 4(3):386–399CrossRefGoogle Scholar
  13. 13.
    Saxby E, Peniston EG (1995) Alpha-theta brainwave neurofeedback training: an effective treatment for male and female alcoholics with depressive symptoms. J Clin Psychol 51(5):685–693CrossRefGoogle Scholar
  14. 14.
    Vernon D, Egner T, Cooper N, Compton T, Neilands C, Sheri A, Gruzelier J (2003) The effect of training distinct neurofeedback protocols on aspects of cognitive performance. Int J Psychophysiol 47(1):75–85CrossRefGoogle Scholar
  15. 15.
    Hanslmayr S, Sauseng P, Doppelmayr M, Schabus M, Klimesch W (2005) Increasing individual upper alpha power by neurofeedback improves cognitive performance in human subjects. Appl Psychophysiol Biofeedback 30(1):1–10CrossRefGoogle Scholar
  16. 16.
    Heinrich H, Gevensleben H, Strehl U (2007) Annotation: neurofeedback-train your brain to train behaviour. J Child Psychol Psychiatry 48(1):3–16CrossRefGoogle Scholar
  17. 17.
    Davidson PR, Jones RD, Peiris MTR (2007) EEG-based lapse detection with high temporal resolution. IEEE Trans Biomed Eng 54(5):832–839CrossRefGoogle Scholar
  18. 18.
    Pop-Jordanov J, Pop-Jordanova N (2009) Neurophysical substrates of arousal and attention. Cognit Process 10(1):71–79CrossRefGoogle Scholar
  19. 19.
    Block A, Von Bloh W, Schellnhuber HJ (1990) Efficient box-counting determination of generalized fractal dimensions. Phys Rev A 42(4):1869MathSciNetCrossRefGoogle Scholar
  20. 20.
    Higuchi T (1988) Approach to an irregular time series on the basis of the fractal theory. Phys D 31(2):277–283MathSciNetCrossRefzbMATHGoogle Scholar
  21. 21.
    Sanei S, Chambers JA (2013) EEG signal processing. Wiley, HobokenGoogle Scholar
  22. 22.
    Neidermeyer E (1999) The normal EEG of the waking adult. In: Electroencephalography: basic principles, clinical applications and related fields, 4th edn. Williams and Wilkins, Baltimore, pp 149–173Google Scholar
  23. 23.
    Ashwal S, Rust R (2003) Child neurology in the 20th century. Pediatr Res 53(2):345–361CrossRefGoogle Scholar
  24. 24.
    Pfurtscheller G, Flotzinger D, Neuper C (1994) Differentiation between finger, toe and tongue movement in man based on 40 Hz EEG. Electroencephalogr Clin Neurophysiol 90(6):456–460CrossRefGoogle Scholar
  25. 25.
    Mandelbrot BB (1967) How long is the coast of Britain. Science 156(3775):636–638CrossRefGoogle Scholar
  26. 26.
    Nakamura YOSHIO, Yamamoto YOSHIHARU, Muraoka I (1993) Autonomic control of heart rate during physical exercise and fractal dimension of heart rate variability. J Appl Physiol 74(2):875–881CrossRefGoogle Scholar
  27. 27.
    Reza Boostani and Mohammad Hassan Moradi (2004) A new approach in the BCI research based on fractal dimension as feature and Adaboost as classifier. J Neural Eng 1(4):212CrossRefGoogle Scholar
  28. 28.
    Gómez C, Mediavilla Á, Hornero R, Abásolo D, Fernández A (2009) Use of the Higuchi’s fractal dimension for the analysis of MEG recordings from Alzheimer’s disease patients. Med Eng Phys 31(3):306–313CrossRefGoogle Scholar
  29. 29.
    Katz MJ (1988) Fractals and the analysis of waveforms. Comput Biol Med 18(3):145–156CrossRefGoogle Scholar
  30. 30.
    Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27(8):861–874MathSciNetCrossRefGoogle Scholar
  31. 31.
    Lutsyuk NV, Éismont EV, Pavlenko VB (2006) Modulation of attention in healthy children using a course of EEG-feedback sessions. Neurophysiology 38(5–6):389–395CrossRefGoogle Scholar
  32. 32.
    Esteller R, Vachtsevanos G, Echauz J, Litt B (2001) A comparison of waveform fractal dimension algorithms. IEEE Trans Circuits Syst I Fundam Theory Appl 48(2):177–183CrossRefGoogle Scholar
  33. 33.
    Rueckert L, Lange N, Partiot A, Appollonio I, Litvan I, Le Bihan D, Grafman J (1996) Visualizing cortical activation during mental calculation with functional MRI. Neuroimage 3(2):97–103CrossRefGoogle Scholar

Copyright information

© The Natural Computing Applications Forum 2017

Authors and Affiliations

  1. 1.Faculty of Computer Science and Information TechnologyUniversity MalayaKuala LumpurMalaysia

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