Hostname: page-component-745bb68f8f-f46jp Total loading time: 0 Render date: 2025-01-08T10:26:01.465Z Has data issue: false hasContentIssue false
  • English
  • Français

Optimal Measurement Conditions for Spatiotemporal Eeg/Meg Source Analysis

Published online by Cambridge University Press:  01 January 2025

Hilde M. Huizenga ,
Dirk J. Heslenfeld  and
Peter C. M. Molenaar
Hilde M. Huizenga*
Affiliation:
University of Amsterdam
Dirk J. Heslenfeld
Affiliation:
University of Amsterdam and Free University of Amsterdam
Peter C. M. Molenaar
Affiliation:
University of Amsterdam
*
Requests for reprints should be sent to Hilde Huizenga, Department of Psychology, University of Amsterdam, Roetersstraat 15, 1018WB Amsterdam, THE NETHERLANDS. E-Mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Electromagnetic source analysis yields estimates of the sources of the Electro- and/or MagnetoEncephaloGram (EEG/MEG) and thus generates a functional description of the human brain. The standard errors of the source estimates are influenced by the number and position of EEG/MEG sensors, by the number of time samples, and by the number of trials in which EEG/MEG is measured. Therefore, optimal design theory is applied to determine the required number and position of sensors, the required number of samples, and the required number of trials. To that end, the Fedorov exchange algorithm is extended to incorporate multi-response models. A simulation study and an empirical study on visual evoked potentials indicate that the proposed method is fast and reliable, and improves source precision considerably.

Keywords

spatiotemporal EEG/MEG source analysismultiresponse nonlinear regressionoptimal designFedorov exchange
Type
Articles
Information
Psychometrika , Volume 67 , Issue 2 , June 2002 , pp. 299 - 313
Copyright
Copyright © 2002 The Psychometric Society

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

We would like to thank Juha Virtanen for his help in collecting the empirical data. The research of H.M.H. has been made possible by a fellowship of the Royal Netherlands Academy of Arts and Sciences. D.J.H. was supported by grant number 575-65-058 from the Dutch Organization for Scientific Research (NWO).

References

Ahonen, A.I., Hämäläinen, M.S., Ilmoniemi, R.J., Kajola, M.J., Knuutila, J.E.T., Simola, J.T., & Vilkman, V.A. (1993). Sampling theory for neuromagnetic detector arrays. IEEE Transactions on Biomedical Engineering, 40(9), 859–869.CrossRefGoogle ScholarPubMed
Atkinson, A.C., & Donev, A.N. (1992). Optimum experimental designs. Oxford, U.K.: Clarendon Press.CrossRefGoogle Scholar
Browne, M.W., & du Toit, S.H.C. (1992). Automated fitting of nonstandard models. Multivariate Behavioral Research, 27, 269–300.CrossRefGoogle ScholarPubMed
Cook, R.D., & Nachtsheim, C.J. (1980). A comparison of algorithms for constructing exact D-optimal designs. Technometrics, 22(3), 315–324.CrossRefGoogle Scholar
Cuffin, B.N. (1985). A comparison of moving dipole inverse solutions using EEG's and MEG's. IEEE Transactions on Biomedical Engineering, 32(11), 905–910.CrossRefGoogle ScholarPubMed
Cuffin, B.N., & Cohen, D. (1979). Comparison of the magnetoencephalogram and electroencephalogram. Electroencephalography and Clinical Neurophysiology, 47, 132–146.CrossRefGoogle ScholarPubMed
Gaumond, R.P., Lin, J.-H., & Geselowitz, D.B. (1983). Accuracy of dipole localization with a spherical homogeneous model. IEEE Transactions on Biomedical Engineering, 30(1), 29–34.CrossRefGoogle ScholarPubMed
Gill, P.E., Murray, W., & Wright, M.H. (1981). Practical Optimization. London, U.K.: Academic Press.Google Scholar
Gunji, A., Kagigi, R., & Hoshiyama, M. (2000). Spatiotemporal source analysis of vocalization-associated magnetic fields. Cognitive Brain Research, 9, 157–163.CrossRefGoogle ScholarPubMed
Hämäläinen, M., Hari, R., Ilmoniemi, R.J., Knuutila, J., & Lounasmaa, O.V. (1993). Magnetoencephalography—Theory, instrumentation, and applications to noninvasive studies of the working human brain. Reviews of Modern Physics, 65, 413–497.CrossRefGoogle Scholar
Hari, R., Joutsiniemi, S.-L, & Sarvas, J. (1988). Spatial resolution of neuromagnetic records: Theoretical calculations in a spherical model. Electroencephalography and Clinical Neurophysiology, 71, 64–72.CrossRefGoogle Scholar
Hochwald, B., Nehorai, A. (1997). Magnetoencephalography with diversely oriented and multicomponent sensors. IEEE Transactions on Biomedical Engineering, 44, 40–50.CrossRefGoogle ScholarPubMed
Huizenga, H.M. (1995). The statistical approach to electromagnetic source localization in the brain. Amsterdam: University of Amsterdam.Google Scholar
Huizenga, H.M., De Munck, J.C., Waldorp, L.J., & Grasman, R.P.P.P. (in press). Spatiotemporal EEG/MEG source analysis based on a parametric noise covariance model. IEEE Transactions on Biomedical Engineering.Google Scholar
Huizenga, H.M., & Molenaar, P.C.M. (1994). Estimating and testing the sources of evoked potentials in the brain. Multivariate Behavioral Research, 29, 237–262.CrossRefGoogle ScholarPubMed
Huizenga, H.M., & Molenaar, P.C.M. (1995). Equivalent source estimation of scalp potential fields contaminated by heteroscedastic and correlated noise. Brain Topography, 8, 13–33.CrossRefGoogle ScholarPubMed
Huizenga, H.M., & Molenaar, P.C.M. (1996). Ordinary least squares dipole localization is influenced by the reference. Electroencephalography and Clinical Neurophysiology, 99, 562–567.CrossRefGoogle ScholarPubMed
Huizenga, H.M., van Zuijen, T.L., Heslenfeld, D.J., & Molenaar, P.C.M. (2001). Simultaneous MEG and EEG source analysis. Physics in Medicine and Biology, 46(7), 1737–1751.CrossRefGoogle ScholarPubMed
Kenemans, J.L., Baas, J.M.P., Mangun, G.R., Lijffijt, M., & Verbaten, M.N. (2000). On the processing of spatial frequencies as revealed by evoked-potential source modeling. Clinical Neurophysiology, 111, 1113–1123.CrossRefGoogle ScholarPubMed
Kuc, R. (1996). Magnetometer spacing criterion for biomagnetic source current imaging. IEEE Transactions on Biomedical Engineering, 43(11), 1125–1127.CrossRefGoogle ScholarPubMed
Lange, N. & Zeger, S.L. (1997). Non-linear Fourier time series analysis for human brain mapping by functional magnetic resonance imaging. Applied Statistics, 46, 1–29.Google Scholar
Mosher, J.C., Spencer, M.E., Leahy, R.M., & Lewis, P.S. (1993). Error bounds for EEG and MEG dipole source localization. Electroencephalography and Clinical Neurophysiology, 86, 303–321.CrossRefGoogle ScholarPubMed
Nunez, P.L. (1988). Spatial filtering and experimental strategies in EEG. In Samson-Dollfus, D. (Eds.), Statistics and topography in quantitative EEG. Paris, France: Elsevier.Google Scholar
Ogura, Y., & Sekihara, K. (1993). Relationship between dipole parameter estimation errors and measurement conditions in magnetoencephalography. IEEE Transactions on Biomedical Engineering, 40(9), 919–924.CrossRefGoogle ScholarPubMed
Pukelsheim, F. (1993). Optimal design of experiments. New York, NY: Wiley.Google Scholar
Schott, J.R. (1997). Matrix analysis for statistics. New York, NY: Wiley.Google Scholar
Seber, G.A.F., & Wild, C.J. (1989). Nonlinear regression. New York, NY: Wiley.CrossRefGoogle Scholar
Silvey, S.D. (1970). Statistical inference. Harmondsworth, U.K.: Penguin.Google Scholar
Spitzer, A.R., Cohen, L.G., Fabrikant, J., & Hallet, M. (1989). A method for determining optimal interelectrode spacing for cerebral topographic mapping. Electroencephalography and Clinical Neurophysiology, 72, 355–361.CrossRefGoogle ScholarPubMed
St. John, R.C., & Draper, N.R. (1975). D-optimality for regression designs: A review. Technometrics, 17, 15–23.CrossRefGoogle Scholar
Tiitinen, H., Sivonen, P., Alku, P., Virtanen, J., & Näätänen, R. (1999). Electromagnetic recordings reveal latency differences in speech and tine processing in humans. Cognitive Brain Research, 8, 355–363.CrossRefGoogle Scholar
Vaidyanathan, C., & Buckley, K.M. (1997). A sampling theorem for EEG electrode configuration. IEEE Transactions on Biomedical Engineering, 44(1), 94–97.CrossRefGoogle ScholarPubMed
Vardi, Y., Shepp, L.A., & Kaufman, L. (1985). A statistical model for positron emission tomography. Journal of the American Statistical Association, 80, 8–20.CrossRefGoogle Scholar
Waldorp, L.J., Huizenga, H.M., Dolan, C.V., & Molenaar, P.C.M. (2001). Estimated generalized least squares electromagnetic source analysis based on a parametric noise covariance model. IEEE Transactions on Biomedical Engineering, 48(6), 737–741.CrossRefGoogle ScholarPubMed