International Conference on Brain Informatics and Health

BIH 2015: Brain Informatics and Health pp 222-231 | Cite as

Time-Varying Parametric Modeling of ECoG for Syllable Decoding

  • Vasileios G. Kanas
  • Iosif Mporas
  • Griffin W. Milsap
  • Kyriakos N. Sgarbas
  • Nathan E. Crone
  • Anastasios Bezerianos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9250)

Abstract

As a step toward developing neuroprostheses, the purpose of this study is to explore syllable decoding in a subject with implanted electrocorticographic (ECoG) recordings. For this study, we use ECoG signals recorded while a subject volunteered to perform a task in which the patient has been visually cued to speak isolated consonant-vowel syllables varying in their articulatory features. We propose a recursive estimation method to calculate the parametric model coefficients in each time instant and band power features from individual ECoG sites are extracted to decode the articulated syllables. Our findings may contribute to the development of brain machine interface (BMI) systems for syllable-level speech rehabilitation in handicapped individuals.

Keywords

Electrocorticography Time-varying autoregressive model Speech rehabilitation Brain machine interface 

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References

  1. 1.
    Gosseries, O., Vanhaudenhuyse, A., Bruno, M.-A., Demertzi, A., Schnakers, C., Boly, M.M., et al.: Disorders of consciousness: coma, vegetative and minimally conscious states. In: States of Consciousness, pp. 29–55. Springer (2011)Google Scholar
  2. 2.
    Birbaumer, N., Ghanayim, N., Hinterberger, T., Iversen, I., Kotchoubey, B., Kübler, A., et al.: A spelling device for the paralysed. Nature 398, 297–298 (1999)CrossRefGoogle Scholar
  3. 3.
    Birbaumer, N., Hinterberger, T., Kubler, A., Neumann, N.: The thought-translation device (TTD): neurobehavioral mechanisms and clinical outcome. IEEE Transactions on Neural Systems and Rehabilitation Engineering 11, 120–123 (2003)CrossRefGoogle Scholar
  4. 4.
    Birbaumer, N., Kübler, A., Ghanayim, N., Hinterberger, T., Perelmouter, J., Kaiser, J., et al.: IV. Future Work. IEEE Transactions on rehabilitation Engineering 8, 191 (2000)CrossRefGoogle Scholar
  5. 5.
    Donchin, E., Spencer, K.M., Wijesinghe, R.: The mental prosthesis: assessing the speed of a P300-based brain-computer interface. IEEE Transactions on Rehabilitation Engineering 8, 174–179 (2000)CrossRefGoogle Scholar
  6. 6.
    Krusienski, D.J., Sellers, E.W., McFarland, D.J., Vaughan, T.M., Wolpaw, J.R.: Toward enhanced P300 speller performance. Journal of neuroscience methods 167, 15–21 (2008)CrossRefGoogle Scholar
  7. 7.
    Nijboer, F., Sellers, E., Mellinger, J., Jordan, M., Matuz, T., Furdea, A., et al.: A P300-based brain–computer interface for people with amyotrophic lateral sclerosis. Clinical neurophysiology 119, 1909–1916 (2008)CrossRefGoogle Scholar
  8. 8.
    Sellers, E.W., Donchin, E.: A P300-based brain–computer interface: initial tests by ALS patients. Clinical neurophysiology 117, 538–548 (2006)CrossRefGoogle Scholar
  9. 9.
    Cheng, M., Gao, X., Gao, S., Xu, D.: Design and implementation of a brain-computer interface with high transfer rates. IEEE Transactions on Biomedical Engineering 49, 1181–1186 (2002)CrossRefGoogle Scholar
  10. 10.
    Friman, O., Luth, T., Volosyak, I., Graser, A.: Spelling with steady-state visual evoked potentials. In: 3rd International IEEE/EMBS Conference on Neural Engineering, CNE 2007, pp. 354–357 (2007)Google Scholar
  11. 11.
    Vaughan, T.M., McFarland, D.J., Schalk, G., Sarnacki, W.A., Krusienski, D.J., Sellers, E.W., et al.: The wadsworth BCI research and development program: at home with BCI. IEEE Transactions on Neural Systems and Rehabilitation Engineering 14, 229–233 (2006)CrossRefGoogle Scholar
  12. 12.
    Pei, X., Barbour, D.L., Leuthardt, E.C., Schalk, G.: Decoding vowels and consonants in spoken and imagined words using electrocorticographic signals in humans. Journal of neural engineering 8, 046028 (2011)CrossRefGoogle Scholar
  13. 13.
    Neuper, C., Müller-Putz, G.R., Scherer, R., Pfurtscheller, G.: Motor imagery and EEG-based control of spelling devices and neuroprostheses. Progress in brain research 159, 393–409 (2006)CrossRefGoogle Scholar
  14. 14.
    Scherer, R., Muller, G., Neuper, C., Graimann, B., Pfurtscheller, G.: An asynchronously controlled EEG-based virtual keyboard: improvement of the spelling rate. IEEE Transactions on Biomedical Engineering 51, 979–984 (2004)CrossRefGoogle Scholar
  15. 15.
    Guenther, F.H., Brumberg, J.S., Wright, E.J., Nieto-Castanon, A., Tourville, J.A., Panko, M., et al.: A wireless brain-machine interface for real-time speech synthesis. PloS one 4, e8218 (2009)CrossRefGoogle Scholar
  16. 16.
    DaSalla, C.S., Kambara, H., Sato, M., Koike, Y.: Single-trial classification of vowel speech imagery using common spatial patterns. Neural Networks 22, 1334–1339 (2009)CrossRefGoogle Scholar
  17. 17.
    Kellis, S., Miller, K., Thomson, K., Brown, R., House, P., Greger, B.: Decoding spoken words using local field potentials recorded from the cortical surface. Journal of neural engineering 7, 056007 (2010)CrossRefGoogle Scholar
  18. 18.
    Zhang, D., Gong, E., Wu, W., Lin, J., Zhou, W., Hong, B.: Spoken sentences decoding based on intracranial high gamma response using dynamic time warping. In: Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE, pp. 3292–3295 (2012)Google Scholar
  19. 19.
    Deng, S., Srinivasan, R., Lappas, T., D’Zmura, M.: EEG classification of imagined syllable rhythm using Hilbert spectrum methods. Journal of neural engineering 7, 046006 (2010)CrossRefGoogle Scholar
  20. 20.
    Bai, O., Nakamura, M., Ikeda, A., Shibasaki, H.: Nonlinear Markov process amplitude EEG model for nonlinear coupling interaction of spontaneous EEG. IEEE Transactions on Biomedical Engineering 47, 1141–1146 (2000)CrossRefGoogle Scholar
  21. 21.
    Ting, C.-M., Salleh, S.-H., Zainuddin, Z., Bahar, A.: Spectral estimation of nonstationary EEG using particle filtering with application to event-related desynchronization (ERD). IEEE Transactions on Biomedical Engineering 58, 321–331 (2011)CrossRefGoogle Scholar
  22. 22.
    Poulimenos, A., Fassois, S.: Parametric time-domain methods for non-stationary random vibration modelling and analysis—a critical survey and comparison. Mechanical Systems and Signal Processing 20, 763–816 (2006)CrossRefGoogle Scholar
  23. 23.
    Schlögl, A.: The electroencephalogram and the adaptive autoregressive model: theory and applications. Shaker, Germany (2000)Google Scholar
  24. 24.
    Khan, M.E., Dutt, D.N.: An expectation-maximization algorithm based Kalman smoother approach for event-related desynchronization (ERD) estimation from EEG. IEEE Transactions on Biomedical Engineering 54, 1191–1198 (2007)CrossRefGoogle Scholar
  25. 25.
    Niedzwiecki, M.: Identification of time-varying processes. Wiley, New York (2000)Google Scholar
  26. 26.
    Duncan, J.S., Papademetris, X., Yang, J., Jackowski, M., Zeng, X., Staib, L.H.: Geometric strategies for neuroanatomic analysis from MRI. Neuroimage 23, S34–S45 (2004)CrossRefGoogle Scholar
  27. 27.
    Goldman, D.: The clinical use of the “average” reference electrode in monopolar recording. Electroencephalography and clinical neurophysiology 2, 209–212 (1950)CrossRefGoogle Scholar
  28. 28.
    Pistohl, T., Schulze-Bonhage, A., Aertsen, A., Mehring, C., Ball, T.: Decoding natural grasp types from human ECoG. Neuroimage 59, 248–260 (2012)CrossRefGoogle Scholar
  29. 29.
    Boersma, P., Weenink, D.: Praat, a system for doing phonetics by computer (2001)Google Scholar
  30. 30.
    Ljung, L.: System identification: theory for the user. PTR Prentice Hall Information and System Sciences Series 198 (1987)Google Scholar
  31. 31.
    Kanas, V.G., Mporas, I., Benz, H.L., Sgarbas, K.N., Bezerianos, A., Crone, N.E.: Joint spatial-spectral feature space clustering for speech activity detection from ECoG signals. IEEE Transactions on Biomedical Engineering 61, 1241–1250 (2014)CrossRefGoogle Scholar
  32. 32.
    Canolty, R.T., Soltani, M., Dalal, S.S., Edwards, E., Dronkers, N.F., Nagarajan, S.S., et al.: Spatiotemporal dynamics of word processing in the human brain. Frontiers in neuroscience 1, 185 (2007)CrossRefGoogle Scholar
  33. 33.
    Aha, D.W., Kibler, D., Albert, M.K.: Instance-based learning algorithms. Machine learning 6, 37–66 (1991)Google Scholar
  34. 34.
    Platt, J.: Fast training of support vector machines using sequential minimal optimization. Advances in kernel methods—support vector learning 3 (1999)Google Scholar
  35. 35.
    Kononenko, I.: Estimating attributes: analysis and extensions of RELIEF. In: Bergadano, F., De Raedt, L. (eds.) ECML 1994. LNCS, vol. 784, pp. 171–182. Springer, Heidelberg (1994)CrossRefGoogle Scholar
  36. 36.
    Vigneau, M., Beaucousin, V., Herve, P.-Y., Duffau, H., Crivello, F., Houde, O., et al.: Meta-analyzing left hemisphere language areas: phonology, semantics, and sentence processing. Neuroimage 30, 1414–1432 (2006)CrossRefGoogle Scholar
  37. 37.
    Indefrey, P.: The spatial and temporal signatures of word production components: a critical update. Frontiers in psychology 2 (2011)Google Scholar
  38. 38.
    McGuire, P., Silbersweig, D., Frith, C.: Functional neuroanatomy of verbal self-monitoring. Brain 119, 907–917 (1996)CrossRefGoogle Scholar
  39. 39.
    Shergill, S.S., Brammer, M.J., Fukuda, R., Bullmore, E., Amaro, E., Murray, R.M., et al.: Modulation of activity in temporal cortex during generation of inner speech. Human brain mapping 16, 219–227 (2002)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Vasileios G. Kanas
    • 1
  • Iosif Mporas
    • 1
    • 2
  • Griffin W. Milsap
    • 3
  • Kyriakos N. Sgarbas
    • 1
  • Nathan E. Crone
    • 4
  • Anastasios Bezerianos
    • 5
    • 6
  1. 1.Department of Electrical and Computer EngineeringUniversity of PatrasPatrasGreece
  2. 2.Computer Informatics Engineering DepartmentTEI of Western GreecePatrasGreece
  3. 3.Department of Biomedical EngineeringJohns Hopkins UniversityBaltimoreUSA
  4. 4.Department of NeurologyJohns Hopkins UniversityBaltimoreUSA
  5. 5.Department of Medical Physics, School of MedicineUniversity of PatrasPatrasGreece
  6. 6.Singapore Institute for NeurotechnologyNational University of SingaporeSingaporeSingapore

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