Skip to main content Accessibility help
Hostname: page-component-8448b6f56d-m8qmq Total loading time: 0 Render date: 2024-04-16T15:19:08.027Z Has data issue: false hasContentIssue false

5 - Overview of analytical approaches

Published online by Cambridge University Press:  15 December 2009

Christoph M. Michel
Université de Genève
Thomas Koenig
University Hospital of Psychiatry, Berne, Switzerland
Daniel Brandeis
Department of Child and Adolescent Psychiatry, University of Zurich, Switzerland and Central Institute of Mental Health, Mannheim, Grmany
Lorena R. R. Gianotti
Universität Zürich
Jiří Wackermann
Institute for Frontier Areas of Psychology and Mental Health, Freiburg im Breisgau, Germany
Get access


The general model

The aim of this chapter is to introduce a structured overview of the different possibilities available to display and analyze brain electric scalp potentials. First, a general formal model of time-varying distributed EEG potentials is introduced. Based on this model, the most common analysis strategies used in EEG research are introduced and discussed as specific cases of this general model. Both the general model and particular methods are also expressed in mathematical terms. It is however not necessary to understand these terms to understand the chapter.

The general model that we propose here is based on the statement made in Chapter 3, stating that the electric field produced by active neurons in the brain propagates in brain tissue without delay in time. Contrary to other imaging methods that are based on hemodynamic or metabolic processes, the EEG scalp potentials are thus “real-time,” not delayed and not a-priori frequency-filtered measurements. If only a single dipolar source in the brain were active, the temporal dynamics of the activity of that source would be exactly reproduced by the temporal dynamics observed in the scalp potentials produced by that source. This is illustrated in Figure 5.1, where the expected EEG signal of a single source with spindle-like dynamics in time has been computed. The dynamics of the scalp potentials exactly reproduce the dynamics of the source. The amplitude of the measured potentials depends on the relation between the location and orientation of the active source, its strength and the electrode position.

Publisher: Cambridge University Press
Print publication year: 2009

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.)


Fuchs, M, Wagner, M, Kastner, J. Boundary element method volume conductor models for EEG source reconstruction. Clinical Neurophysiology 2001;112:1400–1407.CrossRefGoogle ScholarPubMed
Mosher, JC, Leahy, RM, Lewis, PS. EEG and MEG: forward solutions for inverse methods. IEEE Transactions on Biomedical Engineering 1999;46:245–259.CrossRefGoogle ScholarPubMed
Koenig, T, Hubl, D, Mueller, TJ. Decomposing the EEG in time, space and frequency: a formal model, existing methods, and new proposals. In Hirata, K, ed. International Congress Series 318 1232. Amsterdam: Elsevier; 2002, pp. 317–321.Google Scholar
John, ER, Easton, P, Prichep, LS, Friedman, J. Standardized varimax descriptors of event related potentials: basic considerations. Brain Topography 1993;6:143–162.CrossRefGoogle ScholarPubMed
John, ER, Prichep, LS, Easton, P. Standardized varimax descriptors of event related potentials: evaluation of psychiatric patients. Psychiatry Research 1994;55:13–40.CrossRefGoogle ScholarPubMed
Makeig, S, Debener, S, Onton, J, Delorme, A. Mining event-related brain dynamics. Trends in Cognitive Science 2004;8:204–210.CrossRefGoogle ScholarPubMed
Makeig, S, Jung, TP, Bell, AJ, Ghahremani, D, Sejnowski, TJ. Blind separation of auditory event-related brain responses into independent components. Proceedings of the National Academy of Sciences USA 1997;94:10979–10984.CrossRefGoogle ScholarPubMed
Ilmoniemi, RJ. Models of source currents in the brain. Brain Topography 1993;5: 331–336.CrossRefGoogle Scholar
Pascual-Marqui, RD, Michel, CM, Lehmann, D. Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain. International Journal of Psychophysiology 1994;18:49–65.CrossRefGoogle Scholar
Kayser, J, Tenke, CE. Trusting in or breaking with convention: towards a renaissance of principal components analysis in electrophysiology. Clinical Neurophysiology 2005;116:1747–1753.CrossRefGoogle ScholarPubMed
Wackermann, J. Beyond mapping: estimating complexity of multichannel EEG recordings. Acta Neurobiologiae Experimentalis (Warszawa). 1996;56: 197–208.Google ScholarPubMed
Jung, TP, Makeig, S, Humphries, Cet al. Removing electroencephalographic artifacts by blind source separation. Psychophysiology 2000;37:163–178.CrossRefGoogle ScholarPubMed
Kobayashi, K, James, CJ, Nakahori, T, Akiyama, T, Gotman, J. Isolation of epileptiform discharges from unaveraged EEG by independent component analysis. Clinical Neurophysiology 1999;110: 1755–1763.CrossRefGoogle ScholarPubMed
Urrestarazu, E, Iriarte, J, Artieda, Jet al. Independent component analysis separates spikes of different origin in the EEG. Journal of Clinical Neurophysiology 2006;23:72–78.CrossRefGoogle ScholarPubMed
Delorme, A, Makeig, S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods 2004;134:9–21.CrossRefGoogle ScholarPubMed
Lobaugh, NJ, West, R, McIntosh, AR. Spatiotemporal analysis of experimental differences in event-related potential data with partial least squares. Psychophysiology 2001;38:517–530.CrossRefGoogle ScholarPubMed
Koenig, T, Studer, D, Hubl, D, Melie, L, Strik, WK. Brain connectivity at different time-scales measured with EEG. Philosophical Transactions of the Royal Society London Series B Biological Sciences 2005;360:1015–1023.CrossRefGoogle ScholarPubMed
Pascual-Marqui, RD, Michel, CM, Lehmann, D. Segmentation of brain electrical activity into microstates: model estimation and validation. IEEE Transactions on Biomedical Engineering 1995;42:658–665.CrossRefGoogle Scholar
Brandeis, D, Naylor, H, Halliday, R, Callaway, E, Yano, L. Scopolamine effects on visual information processing, attention, and event-related potential map latencies. Psychophysiology 1992;29:315–336.CrossRefGoogle ScholarPubMed
Scherg, M, Ille, N, Bornfleth, H, Berg, P. Advanced tools for digital EEG review: virtual source montages, whole-head mapping, correlation, and phase analysis. Journal of Clinical Neurophysiology 2002;19: 91–112.CrossRefGoogle ScholarPubMed
John, ER, Ahn, H, Prichep, Let al. Developmental equations for the electroencephalogram. Science 1980;210:1255–1258.CrossRefGoogle ScholarPubMed
John, ER, Prichep, LS, Fridman, J, Easton, P. Neurometrics: computer-assisted differential diagnosis of brain dysfunctions. Science 1988;239:162–169.CrossRefGoogle ScholarPubMed
Hughes, JR, John, ER. Conventional and quantitative electroencephalography in psychiatry. Journal of Neuropsychiatry and Clinical Neuroscience 1999;11:190–208.CrossRefGoogle Scholar
Borbely, AA, Achermann, P. Sleep homeostasis and models of sleep regulation. Journal of Biological Rhythms 1999;14:557–568.Google ScholarPubMed
Herrmann, WM. Development and critical evaluation of an objective for the electroencephalographic classification of psychotropic drugs. In Herrmann, WM, ed. Electroencephalography in Drug Research. Stuttgart: Gustav Fisher; 1982, pp. 249–351.Google Scholar
Saletu, B, Kufferle, B, Grunberger, Jet al. Clinical, EEG mapping and psychometric studies in negative schizophrenia: comparative trials with amisulpride and fluphenazine. Neuropsychobiology 1994;29: 125–135.CrossRefGoogle ScholarPubMed
Finelli, , Achermann, P, Borbely, AA. Individual ‘fingerprints’ in human sleep EEG topography. Neuropsychopharmacology 2001;25:S57–S62.CrossRefGoogle ScholarPubMed
Koenig, T, Marti-Lopez, F, Valdes-Sosa, P. Topographic time-frequency decomposition of the EEG. Neuroimage 2001;14:383–390.CrossRefGoogle ScholarPubMed
Studer, D, Hoffmann, U, Koenig, T. From EEG dependency multichannel matching pursuit to sparse topographic EEG decomposition. Journal of Neuroscience Methods 2006;153:261–275.CrossRefGoogle ScholarPubMed

Save book to Kindle

To save this book to your Kindle, first ensure is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the or variations. ‘’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats