Download PDF
Methods Inf Med 2001; 40(04): 338-345
DOI: 10.1055/s-0038-1634430
Original Article
Schattauer GmbH

Evoked Potential Enhancement Using a Neurophysiologically-based Model

B. H. Jansen
1   Department of Electrical and Computer Engineering, and Bioengineering Research Center, University of Houston, Houston, USA
,
A. Balaji Kavaipatti
1   Department of Electrical and Computer Engineering, and Bioengineering Research Center, University of Houston, Houston, USA
,
O. Markusson
1   Department of Electrical and Computer Engineering, and Bioengineering Research Center, University of Houston, Houston, USA
› Author Affiliations
Further Information

Publication History

Received 30 May 2000

Accepted 09 April 2001

Publication Date:
08 February 2018 (online)

    Permissions and Reprints

Summary

Objective: Single trial evoked potentials (EP) are generally obscured by the much larger spontaneous or background electroencephalogram (EEG). A novel method was developed to enhance single trial EPs. The potential of this approach was explored using actual flash evoked visual EPs.

Method: The basic procedure is a variant of the adaptive filtering approach. At the core of our method is a mathematical, but neurophysiologically-realistic, nonlinear model of the cortical structures involved in generating EEG and EP activity. The model parameters are adjusted by a genetic algorithm in such a way that the model output resembles the actually observed pre-stimulus EEG activity. When post-stimulus EEG is passed through the inverse model, enhancement of the single trial EP should, theoretically, occur.

Results: Evidence was found that, in case of visual evoked potentials obtained by flashing light through closed eyelids, alpha activity continues to around 150 ms post-stimulus, at which point a low frequency potential arises, cresting 100 ms later and disappearing after another 100 ms or so. Also, it was found that an individual’s response varies considerably from trial to trial.

Conclusion: The inverse modeling approach presented here is effective at enhancing single trial EP activity. One potential application is to distinguish trials that contain a response from those that do not, which could result in improved ensemble averages.

Keywords

Evoked Potentials - Visual - Signal Processing - Computer Assisted - Nonlinear - Models - Neural Networks - Algorithms
 

  • References

  • 1 Basar E. EEG-Brain Dynamics: Relation between EEG and brain evoked potentials. Elsevier Biomedical Press; The Netherlands: 1980
    Google Scholar
  • 2 Brandt ME, Jansen BH, Carbonari JP. Pre-stimulus spectral EEG patterns and the visual evoked response. Electroencephal Clin Neurophysiol 1991; 80: 16-20.
    CrossrefPubMedGoogle Scholar
  • 3 Gevins AS, Morgan NH. Classifier-directed signal processing in brain research. IEEE TBME 1986; 33: 1054-68.
    PubMedGoogle Scholar
  • 4 Zouridakis G, Jansen BH, Boutros NN. A fuzzy clustering approach to EP estimation. IEEE T-BME 1997; 44: 673-80.
    PubMedGoogle Scholar
  • 5 McGillem CD, Aunon JI. Measurements of signal components in single visually evoked brain potentials. IEEE T-BME 1977; 24: 232-41.
    PubMedGoogle Scholar
  • 6 McGillem CD, Aunon JI, Pomalaza CA. Improved waveform estimation procedures for event related potentials. IEEE T-BME 1985; 32: 371-9.
    PubMedGoogle Scholar
  • 7 Woody CD. Characterization of an adaptive filter for the analysis of variable latency neuro-electric signals. Med Biol Engng 1967; 5: 539-53.
    CrossrefPubMedGoogle Scholar
  • 8 Gupta L, Molfese DL, Tammana R, Simos PG. Nonlinear alignment and averaging for estimating the evoked potential. IEEE T-BME 1996; 43: 348-56.
    PubMedGoogle Scholar
  • 9 Jansen BH, Yeh Y. Single trial evoked potential analysis by means of crosscorrelation and dynamic time-warping. Sign Proces 1986; 11: 179-86.
    PubMedGoogle Scholar
  • 10 Cerutti S, Bersani V, Carrara A, Liberati D. Analysis of visual evoked potentials through Wiener filtering applied to a small number of sweeps. J Biomed Engng 1987; 9: 3-12.
    CrossrefPubMedGoogle Scholar
  • 11 Doyle DJ. Some comments on the use of Wiener filtering for the estimation of evoked potentials. Electroencephal Clin Neurophysiol 1975; 38: 533-4.
    CrossrefPubMedGoogle Scholar
  • 12 Weerd JPC de. Facts and fancies about a posteriori “Wiener’’ filtering. IEEE T-BME 1981; 28: 252-7.
    PubMedGoogle Scholar
  • 13 Weerd JPC de. A posteriori time-varying filtering of averaged evoked potentials, I. Introduction and conceptual basis. Biol Cybern 1981; 41: 211-22.
    CrossrefPubMedGoogle Scholar
  • 14 Jansen BH. Analysis of biomedical signals by means of linear modeling. CRC Crit Rev Bioengng 1985; 12: 343-92.
    PubMedGoogle Scholar
  • 15 Cerutti S, Baselli G, Liberati D, Pavesi G. Single sweep analysis of visual evoked potentials through a model of parametric identification. Biol Cybernet 1987; 56: 111-20.
    CrossrefPubMedGoogle Scholar
  • 16 Liberati D, Bedarida L, Brandazza P, Cerutti S. A model for the cortico-cortical neural interaction in multisensory-evoked potentials. IEEE T-BME 1991; 38: 879-89.
    PubMedGoogle Scholar
  • 17 Solomon G, Barford J. A new concept of vertex ERA and EEG analysis applying inverse filtering. Acta Otolar 1977; 83: 200-10.
    CrossrefPubMedGoogle Scholar
  • 18 Kawashima T, Miyake A, Yamazaki T, Watanobe S, Shikita Y. An analysis of the non-linearity of the visual evoked responses of the cats. J Physiol Soc Jap 1983; 45: 453.
    PubMedGoogle Scholar
  • 19 Shibagaki M, Kiyono S, Kawashima T, Watanabe S. Non-linearity of VEPs in cerveau isolé and midpontina pretrigeminal cats. Electroencephal Clin Neurophysiol 1985; 62: 65-73.
    CrossrefPubMedGoogle Scholar
  • 20 Watanabe S, Shikita S. Stability of alpha rhythm. In: Yamaguchi N, Fujisawa K. eds. Recent Advances in EEG and EMG Data Processing. Amsterdam: Elsevier/North Holland Biomedical Press; 1981: 87-94.
    Google Scholar
  • 21 Jansen BH, Zouridakis G, Brandt ME. A neurophysiologically based mathematical model of flash visual evoked potentials. Biol Cybern 1993; 68: 275-83.
    CrossrefPubMedGoogle Scholar
  • 22 Jansen BH, Rit VG. EEG and visual evoked potential generation in a mathematical model of coupled cortical columns. Biol. Cybern 1995; 73: 357-66.
    CrossrefPubMedGoogle Scholar
  • 23 Lopes da Silva FH, Hoek A, Smits H, Zetterberg LH. Model of brain rhythmic activity. The alpha rhythm of the thalamus. Kybernetik 1974; 15: 27-37.
    CrossrefPubMedGoogle Scholar
  • 24 Lopes da Silva FH, Rotterdam A van, Barts P, Heusden E, Burr W. Model of neuronal populations. The basic mechanism of rhythmicity. Prog Brain Res 1976; 45: 281-308.
    PubMedGoogle Scholar
  • 25 Rotterdam A van, Lopes da Silva FH, Ende J van der, Viergever MA, Hermans AJ. A model of the spatial-temporal characteristics of the alpha rhythm. Bull Math Biol 1982; 44: 283-305.
    CrossrefPubMedGoogle Scholar
  • 26 Goldberg DE. Genetic Algorithms in Search, Optimization and Machine Learning. New Jersey: Addison-Wesley; 1989
    Google Scholar
  • 27 Yao L, Sethares WA. Nonlinear parameter estimation via the genetic algorithm. IEEE TSP 1994; 42: 927-35.
    PubMedGoogle Scholar
  • 28 Filho JR, Alippi C, Treleaven P. Genetic algorithm programming environments. Computer 1994; 27: 28-43.
    PubMedGoogle Scholar
  • 29 Agarwal AK, Ahalpara DP, Kaw PK, Prabhakara HR, and Sen A. Model equations from a chaotic time series. Pramãna – J Physics 1990; 35: 287-301.
    PubMedGoogle Scholar
  • 30 GB Gilmore R. Topological analysis and synthesis of chaotic time series. Physica D 1992; 58: 229-42.
    CrossrefPubMedGoogle Scholar
  • 31 Simm CW, Sawley ML, Skiff F, Pochelon A. On the analysis of experimental signals for evidence of deterministic chaos. Hel Phys Acta 1987; 60: 510-51.
    PubMedGoogle Scholar
  • 32 Jansen BH, Nyberg HN. Measuring the similarity between trajectories using clustering techniques. Chaos 1993; 43: 143-51.
    PubMedGoogle Scholar
  • 33 Takens F. Detecting strange attractors in turbulence. Warwick Lect Notes Math 1980; 898: 366-81.
    PubMedGoogle Scholar
  • 34 Jansen BH, Bourne JR, Ward JW. Autoregressive estimation of short segment spectra for computerized EEG analysis. IEEE T-BME 1981; 28: 630-38.
    PubMedGoogle Scholar