182
Views
0
CrossRef citations to date
0
Altmetric
Articles

Improved EEG Segmentation Using Non-linear Volterra Model in Bayesian Method

&

REFERENCES

  • D. C. Costa , G. A. M. Lopes , C. A. B. Mello , and H. O. Viana , “Speech and phoneme segmentation under noisy environment through spectrum image analysis systems,” in Man and Cybernetics (SMC) IEEE International Conference on Digital Signal Processing, Seoul, South Korea, 2012, pp. 1017–22.
  • S. Soman , Jayadeva , S. Arjunan , and D. K. Kumar , “Improved sEMG signal classification using the Twin SVM.” in IEEE International Conference on Systems, Man, and Cybernetics, Budapest, Hungary, 2016, pp. 1–6.
  • S. Soman and Jayadeva , “High performance EEG signal classification using classifiability and the Twin SVM,” Appl. Soft Comput. , Vol. 30, pp. 305–18, May 2015.
  • M. B. Westover , S. Ching , M. M. Shafi , S. S. Cash , and E. N. Brown , “Real-time segmentation and tracking of brain metabolic state in ICU EEG recordings of burst suppression,” in 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Osaka, Japan, July 3–7, 2013, 7108–11.
  • M. Basseville and N. Nikiforov , The Detection of Abrupt Changes – Theory and Applications, Information and System Sciences Series . Englewood Cliffs, NJ: Prentice-Hall, 1993.
  • B. Brodsky and B. Darkhovsky , Nonparametric Methods in Change-Point Problems . Dordrecht, The Netherlands : Kluwer Academic Publishers, 1993.
  • M. Basseville , “Detecting changes in signals and systems: A survey,” Automatica, Vol. 2, no. 3, pp. 309–26, May 1988.
  • U. Appel and A. V. Brandt , “Comparative study of three sequential time series segmentation algorithms,” Signal Process. , Vol. 6, no. 1, pp. 45–60, Jan. 1984.
  • F. Wendling , G. Carrault , and J. M. Badier , “Segmentation of Depth–EEG seizure signals: Method based on a physiological parameter and comparative study,” Ann. Biomed. Eng. , Vol. 25, no. 6, pp. 1026–39, Nov. 1997.
  • F. H. Lopes Da Silva , A. Dijk , and H. Smits , “Detection of non-stationarities in EEGs using the autoregressive model – an application to EEGS of epileptics,” in CEAN–Computerized EEG Analysis, G. Dolce and H. Konkel, Eds. Stuttgart : Fischer-Verlag, 1975, pp. 180–99.
  • U. Appel and A. V. Brandt , “Adaptive sequential segmentation of piecewise stationary time series,” Inform. Sci. , Vol. 29, no. 1, pp. 27–56, Feb. 1983.
  • V. Lawhern , S. Kerick , and K. A. Robbins , “Detecting alpha spindle events in EEG time series using adaptive autoregressive models,” BMC Neurosci. , Vol. 14, pp. 101, Sept. 2013.
  • V. Krajaca , S. Petranek , I. Patakova, and A. Varri , “Automatic identification of significant graphoelements in multichannel EEG recordings by adaptive segmentation and fuzzy clustering,” Int. J. Bio-med. Comput. , Vol. 28, no. 1–2, pp. 71–89, May-June 1991.
  • R. Biscay , M. Lavielle , A. Gonzalez , I. Clark , and P. Valdes , “Maximum a posteriori estimation of change points in the EEG,” Int. J. Bio-med. Comput. , Vol. 38, no. 2, pp. 189–96, Sept. 1995.
  • M. Lavielle and E. Lebarbier , “An application of MCMC methods to the multiple change-points problem,” Signal Process. , Vol. 81, no. 1, pp. 39–53, Apr. 2001.
  • I. Gijbels , P. Hall , and A. Kneip , “On the estimation of jump points in smooth curves,” ‎Ann. Inst. Stat. Math. , Vol. 51, no. 2, pp. 231–51, Jun. 1999.
  • J. Braun , R. Braun , and H. Muller , “Multiple change point fitting via quasi likelihood with application to DNA sequence segmentation,” Biometrika , Vol. 87, no. 2, pp. 301–14, 2000.
  • M. Lavielle , “Detection of multiple changes in a sequence of dependent variables,” Stoch. Proc. Appl. , Vol. 83, no. 1, pp. 79–102, Sept. 1999.
  • M. Lavielle and C. Ludena , “The multiple change-points problem for the spectral distribution,” Bernoulli , Vol. 6, no. 5, pp. 845–69, Apr. 2000.
  • Y. Yao , “Estimating the number of change-points via Schwarz criterion,” Stat. Probab. Lett. , Vol. 6, no. 3, pp. 181–89, Feb. 1988.
  • M. Lavielle , “Using penalized contrasts for the change-point problem,” Signal Process. , Vol. 85, no. 8, pp. 1501–10, Oct. 2005.
  • S. Ghosal , J. K. Ghosh , and A. W. van der Vaart , “Convergence rates of posterior distributions,” Ann. Stat. , Vol. 28, no. 2, pp. 500–31, Dec. 2000.
  • M. Hassani and M. R. Karami , “Noise estimation in electroencephalogram signal by using Volterra series coefficients,” J. Med. Signals Sens. , Vol. 5, no. 3, pp. 192–200, July-Sept. 2015.
  • A. Stefanou and G. Gielen , “A Volterra series nonlinear model of the sampling distortion in flash ADCs due to substrate noise coupling,” IEEE Trans. Circuits Syst. II , Vol. 58, no. 12, pp. 877–81, Dec. 2011.
  • L. A. Azpicueta-Ruiz , A. R. Figueiras-vidal , W. Kellermann , and J. Arenas-Garcia , “Enhanced adaptive Volterra filtering by automatic attenuation of memory regions and its application to acoustic echo cancellation,” IEEE Trans. Signal Process. , Vol. 61, pp. 2745–50, Mar. 2013.
  • F. Küch and W. Kellermann , “Nonlinear echo cancellation using a second order Volterra filter,” in IEEE International Conference on Acoustics, Speech & Signal Processing (ICASSP) , Orlando, FL, May 13–17, 2002.
  • G. Budura and I. Naforniţa , “Kernels measurement techniques for constructing nonlinear models,” Sci. Bull. Politech. Univ. Timiş., Rom. , Vol. 47, pp. 190–5, Jan. 2002.
  • M. Schetzen , The Volterra and Wiener Theories of Nonlinear Systems. New York : John Wiley and Sons, 1980.
  • G. Budura and C. Botoca , “La construction d'un modèle non linéaire a l'aide de series Volterra et Wiener [The construction of a nonlinear model with the help of Volterra series and wiener filter],” Rev. Acad. Roum., Buchar. , Vol. 49, no. 5, 2005.
  • G. Budura and C. Botoca , “Nonlinearities identification using the LMS Volterra filter,” in WSEAS International Conference on Dynamical Systems and Control , Venice, Italy , Nov. 2–4, 2005, pp. 148 –53.
  • B. Delyon , M. Lavielle , and E. Moulines , “Convergence of a stochastic approximation version of the EM algorithm,” Ann. Stat. , Vol. 27, no. 1, pp. 94–128, Dec. 1999.
  • U. Hoffmann , J.-M. Vesin , T. Ebrahimi , and K. Diserens , “An efficient P300-based brain-computer interface for disabled subjects,” J. Neurosci. Methods , Vol. 167, no. 1, pp. 115–25, Mar. 2008.
  • Available online: http://physionet.org/physiobank/database/chbmit (accessed on 2016 Oct. 28)

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.