Skip to main content

Chaos Analysis of Speech Imagery of IPA Vowels

  • Conference paper
  • First Online:
Intelligent Human Computer Interaction (IHCI 2018)

Abstract

In Brain Computer Interfacing (BCI), speech imagery is still at nascent stage of development. There are few studies reported considering mostly vowels or monosyllabic words. However, language specific vowels or words made it harder to standardise the whole analysis of electroencephalography (EEG) while distinguishing between them. Through this study, we have explored significance of chaos parameters for different imagined vowels chosen from International Phonetic Alphabets (IPA). The vowels were categorised into two categories, namely, soft vowels and diphthongs. Chaos analysis at EEG subband levels were evaluated. We have also reported significant contrasts between spatiotemporal distributions with chaos analysis for activation of different brain regions in imagining vowels.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Adeli, H., Ghosh-Dastidar, S., Dadmehr, N.: A wavelet-chaos methodology for analysis of EEGs and EEG subbands to detect seizure and epilepsy. IEEE Trans. Biomed. Eng. 54(2), 205–211 (2007)

    Article  Google Scholar 

  2. Cao, L.: Pratical method for determining the minimum embedding dimension of a scalar time series. Phys. D Nonlinear Phenom. 110(1–2), 43–50 (1997)

    Article  Google Scholar 

  3. DaSalla, C.S., Kambara, H., Sato, M., Koike, Y.: Single-trial classification of vowel speech imagery using common spatial patterns. Neural Netw. 22(9), 1334–1339 (2009)

    Article  Google Scholar 

  4. D’Zmura, M., Deng, S., Lappas, T., Thorpe, S., Srinivasan, R.: Toward EEG sensing of imagined speech. In: Jacko, J.A. (ed.) HCI 2009. LNCS, vol. 5610, pp. 40–48. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-02574-7_5

    Chapter  Google Scholar 

  5. Iasemidis, L.D., Olson, L.D., Savit, R.S., Sackellares, J.C.: Time dependencies in the occurrences of epileptic seizures. Epilepsy Res. 17(1), 81–94 (1994)

    Article  Google Scholar 

  6. Iasemidis, L.D., Sackellares, J.C., Zaveri, H.P., Williams, W.J.: Phase space topography and the Lyapunov exponent of electrocorticograms in partial seizures. Brain Topogr. 2(3), 187–201 (1990)

    Article  Google Scholar 

  7. Pincus, S.M.: Approximate entropy as a measure of system complexity. Proc. Natl. Acad. Sci. 88(6), 2297–2301 (1991)

    Article  MathSciNet  Google Scholar 

  8. Riaz, A., Akhtar, S., Iftikhar, S., Khan, A.A., Salman, A.: Inter comparison of classification techniques for vowel speech imagery using EEG sensors. In: 2nd International Conference on Systems and Informatics (ICSAI) 2014, pp. 712–717 (2014)

    Google Scholar 

  9. Rojas, D.A., Ramos, O.L., Saby, J.E.: Recognition of spanish vowels through imagined speech by using spectral analysis and SVM. J. Inf. Hiding Multimed. Sig. Process 7(4), 889–897 (2016)

    Google Scholar 

  10. Roy, R., Sikdar, D., Mahadevappa, M., Kumar, C.: A fingertip force prediction model for grasp patterns characterised from the chaotic behaviour of EEG. Med. Biol. Eng. Comput. 56, 2095–2107 (2018)

    Article  Google Scholar 

  11. Roy, R., Sikdar, D., Mahadevappa, M., Kumar, C.: EEG based motor imagery study of time domain features for classification of power and precision hand grasps. In: 8th International IEEE/EMBS Conference on Neural Engineering (NER) 2017, pp. 440–443 (2017)

    Google Scholar 

  12. Takens, F.: Dynamical systems and turbulence. Lect. Notes Math. 898(9), 366 (1981)

    Article  MathSciNet  Google Scholar 

  13. Williams, G.P.: Chaos Theory Tamed. Joseph Henry Press, Washington (1997)

    MATH  Google Scholar 

  14. Wolf, A., Swift, J.B., Swinney, H.L., Vastano, J.A.: Determining Lyapunov exponents from a time series. Phys. D Nonlinear Phenom. 16(3), 285–317 (1985)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Debdeep Sikdar .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sikdar, D., Roy, R., Mahadevappa, M. (2018). Chaos Analysis of Speech Imagery of IPA Vowels. In: Tiwary, U. (eds) Intelligent Human Computer Interaction. IHCI 2018. Lecture Notes in Computer Science(), vol 11278. Springer, Cham. https://doi.org/10.1007/978-3-030-04021-5_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-04021-5_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04020-8

  • Online ISBN: 978-3-030-04021-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics