Skip to main content
Log in

Total and partial coherence analysis of spontaneous and evoked EEG by means of multi-variable autoregressive processing

  • Published:
Medical and Biological Engineering and Computing Aims and scope Submit manuscript

Abstract

The joint use of total and partial coherence between pairs of EEGs simultaneously recorded in a standard set, is shown to enhance what is caused by direct correlation between cortical subsystems and what is instead related to the spread of the electromagnetic field. A multi-variable autoregressive approach is employed in the computation, giving results even for a very short time window, thus allowing coherence to be investigated at the main cortical latencies of evoked potentials. In particular, when a combined visual and somatosensory stimulation is applied, cortical interactions are captured in the frequency domain.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Akaike, H. (1970): ‘Statistical predictor identification’,Ann. Inst. Statist. Math.,22, pp. 203–217.

    Article  MathSciNet  MATH  Google Scholar 

  • Baselli, G., Cerruti, S., Civardi, S., Liberati, D., Lombardi, F., Malliani, A., andPagani, M. (1986): ‘Spectral and cross-spectral analysis of heart rate and arterial blood pressure variability signals’,Comput. Biomed. Res.,19, pp. 520–534.

    Article  Google Scholar 

  • Bendat, J. andPiersol, A. (1980): ‘Engineering applications of correlation and spectral analysis’ (Wiley).

  • Box, G. E. P., andJenkins, G. M. (1976): ‘Time series analysis, forecasting and control’ (Holden-Day, San Francisco).

    MATH  Google Scholar 

  • Cerutti, S., Liberati, D., andMascellani, P. (1985): ‘Parameter extraction in EEG processing during riskful neurosurgical operation’,Sig. Proc.,9, pp. 25–35.

    Article  Google Scholar 

  • Cerruti, S., andLiberati, D. (1985): ‘Autoregressive modeling and filtering of EEG signal generating mechanism in patients under surgical interventions’,Model. Simul. Control.,2 (4), pp. 11–23.

    Google Scholar 

  • Cerruti, S., Baselli, G., Liberati, D., andPavesi, G. (1987): ‘Single sweep analysis of visual evoked potentials through a model of parametric identification’,Biol. Cybern.,56, pp. 111–120.

    Article  Google Scholar 

  • Cerruti, S., Chiatenza, G., Liberati, D., Mascellani, andPavesi, G. (1987): ‘A parametric method of identification of the single trial event related potentials in the brain’,IEEE Trans.,BME-35, pp. 701–711.

    Google Scholar 

  • Comi, G., Locatelli, T., Fornara, C., Bianchi, A., andLiberati, D. (1990): ‘Topographic maps of single sweep long latency median nerve analysis’,EEG Clin. Neurophys.,S41, (4), pp. 28–33.

    Google Scholar 

  • Gersch, W. (1970): ‘Spectral analysis of EEGs by autorgressive decompositions of time series’,Math. Biosci.,7, pp. 205–222.

    Article  MATH  Google Scholar 

  • Gevins, A. S., andRemond, A. (1987): ‘Methods of analysis of brain electrical and magnetic signalsin ‘Handbook of electroencephalography and clinical neurophysiology, vol. 1’ (Elsevier, Amsterdam).

    Google Scholar 

  • Haykin, S., andKesler, S. (1979): ‘Prediction-error filtering and maximum-entropy spectral estimation’in Haykin, S. (Ed.), ‘Nonlinear methods of spectral analysis’ (Springer, Berlin).

    Google Scholar 

  • Hjorth, B. (1983): ‘An on-line transformation of EEG scalp potentials into ortogonal source derivation’,Electroenceph. Clin. Neurophysiol.,56, pp. 501–514.

    Article  Google Scholar 

  • Isaksson, A., Wennberg, A., andZetterberg, L. H. (1981): ‘Computer analysis of EEG signals with parametric models’,Proc. IEEE,69, pp. 451–461.

    Article  Google Scholar 

  • Liberati, D., Cerruti, S., Diponsio, E., Ventimiglia, V., andZaninelli, L. (1989): ‘Methodological aspects for the implementation of ARX modeling in single sweep visual evoked potentials analysis’,J. Biomed. Eng.,11, pp. 285–292.

    Article  Google Scholar 

  • Liberati, D. (1991): ‘Patrial and total coherence analysis of heart rate variability, blood pressure and respiration signals’,Proc. IV Int. Symp. on Biomedical Engineering, Pensicola, USA.

  • Liberati, D., Bertolini, L., andColombo, D. C. (1991a): ‘A parametric method for the detection of inter and intra-sweep variability in VEP's processing’,Med. Biol. Eng. Comput.,29, pp. 159–166.

    Article  Google Scholar 

  • Liberati, D., Bedarida, L., Brandazza, P., andCerruti, S. (1991b): ‘A model for the cortico-cortical neural interaction in multisensory evoked potentials’,IEEE Trans.,BME-39(9), pp. 879–890.

    Google Scholar 

  • Liberati, D., Dicorrado, S., andMandelli, S. (1992): ‘Topographic mapping of single sweep evoked potentials in the brain’,IEEE Trans.,BME-39, pp. 943–951.

    Google Scholar 

  • Liberati, D., Narici, L., Santoni, A., andCerutti, S. (1992b): ‘The dynamic behaviour of the evoked magneto-encephalogram detected through parametric identification’,J. Biomed. Eng.,14, pp. 57–64.

    Article  Google Scholar 

  • Marple, L. (1987): ‘Digital spectral analysis with applications’ (Prentice Hall).

  • Nunez, P. L. (1981): ‘Electric fields of the brain. The neurophysics of EEG’ (Oxford University Press, New York).

    Google Scholar 

  • Regan, D. (1972): ‘Evoked potential in psychology, sensory physiology, and clinical medicine’ (Chapman and Hall, London).

    Google Scholar 

  • Zetterberg, L. (1969): ‘Estimation of parameters for a linear differences equation with application to EEG analysis’,Mat. Biosci.,5, pp. 227–275.

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to D. Liberati.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Liberati, D., Cursi, M., Locatelli, T. et al. Total and partial coherence analysis of spontaneous and evoked EEG by means of multi-variable autoregressive processing. Med. Biol. Eng. Comput. 35, 124–130 (1997). https://doi.org/10.1007/BF02534142

Download citation

  • Received:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF02534142

Keywords

Navigation