# Foundation Themes for Advanced EEG/MEG Data Analysis: Theory and Demonstrations via Hands-on Examples

The three-day course followed the *Consciousness and its
Measures* conference. The course covered both the theory and practice of EEG/MEG data analysis.
It consisted of two distinct parts: (1) a one-day intensive theoretical workshop on the forward and
inverse problems for EEG/MEG and the related issue of mathematical descriptions of the brain activity in terms
of networks; (2) a two-day course (one day EEG-related, next day MEG-related) with few short lectures and long
hands-on practical sessions describing how the theoretical concepts translate in practice.

**Part 1 (theoretical workshop)** of the course included lectures by well-known experts in
their fields, followed by round-table discussions (*click a talk title to download the presentation in
PDF format*):

Lecture 1: Basic concepts and their foundations

*Andreas A. Ioannides*

Lecture 2: Basic issues for the estimation of the sources with non-invasive EEG measurements

*Fabio Babiloni*

Lecture 3: Mathematics for the forward and inverse problem of EEG

*Rolando Grave de Peralta*

Lecture 4: Mathematics for the forward and inverse problems of MEG

*Andreas A. Ioannides*

Lecture 5: Basis of the estimation of connectivity: general principles and measures of connectivity

*Laura Astolfi*

Lecture 6: Estimating of propagation measures

*Maciej Kaminski*

Lecture 7: Brain networks from MEG data

*Vahe Poghosyan / Andreas A. Ioannides*

Lecture 8: Networks at different timescales

*Fabrizio De Vico Fallani*

**Part 2 (practical sessions)** of the course included short lectures, hands-on experience with
number of software packages, and free one-on-one and group discussions with lecturers and instructors.

Some of the software used in the hands-on sessions included: *Cartool*, *Brain Information Flow Calculator* (BIFC, contact the authors: Konrad
Kwaskiewicz / Maciej Kaminski), *Brain Connectivity Toolbox* (BCT), *Pajek*, *Elekta software*.

*List of the course lecturers and intstructors:*

Laura Astolfi (University of Rome "Sapienza", Italy)

Fabio Babiloni (University of Rome "Sapienza", Italy)

Fabrizio de Vico Fallani (University of Rome "Sapienza", Italy)

Janne Hämäläinen (Elekta Oy, Finland)

Liisa Helle (Elekta Oy, Finland)

Andreas A. Ioannides (AAI Scientific Cultural Services Ltd., Cyprus)

George K. Kostopoulos (University of Patras, Greece)

Maciej Kaminski (University of Warsaw, Poland)

Konrad Kwaskiewicz (University of Warsaw, Poland)

Lichan Liu (AAI Scientific Cultural Services Ltd., Cyprus)

Rolando Grave de Peralta (Geneva University Hospital, Switzerland)

Gijs Plomp (EPFL, Switzerland)

Vahe Poghosyan (AAI Scientific Cultural Services Ltd., Cyprus)

**Research papers recommended by the lecturers for reading:**

**Andreas A. Ioannides**

Ioannides,A.A. (2006). Magnetoencephalography as a research tool in neuroscience: state of the art. Neuroscientist. 12, 524-544.

Ioannides,A.A. (2007). Dynamic functional connectivity. Curr.Opin.Neurobiol. 17, 161-170.

Ioannides,A.A., Kostopoulos,G.K., Laskaris,N.A., Liu,L.C., Shibata,T., Schellens,M., Poghosyan,V., and Khurshudyan,A. (2002). Timing and connectivity in the human somatosensory cortex from single trial mass electrical activity. Hum.Brain Mapp. 15, 231-246.

Moradi,F., Liu,L.C., Cheng,K., Waggoner,R.A., Tanaka,K., and Ioannides,A.A. (2003). Consistent and precise localization of brain activity in human primary visual cortex by MEG and fMRI. Neuroimage. 18, 595-609.

Papadelis,C., Poghosyan,V., Fenwick,P.B., and Ioannides,A.A. (2009). MEG's ability to localise accurately weak transient neural sources. Clin.Neurophysiol. 120, 1958-1970.

Poghosyan,V. and Ioannides,A.A. (2008). Attention modulates earliest responses in the primary auditory and visual cortices. Neuron 58, 802-813.

Taylor,J.G., Ioannides,A.A., and Mueller-Gaertner,H.W. (1999). Mathematical analysis of lead field
expansions. IEEE Trans.Med.Imaging 18, 151-163.

**Fabio Babiloni**

Babiloni,F., Babiloni,C., Carducci,F., Fattorini,L., Onorati,P., and Urbano,A. (1996). Spline Laplacian estimate of EEG potentials over a realistic magnetic resonance-constructed scalp surface model. Electroencephalogr.Clin.Neurophysiol. 98, 363-373.

Babiloni,F., Babiloni,C., Carducci,F., Romani,G.L., Rossini,P.M., Angelone,L.M., and Cincotti,F. (2003). Multimodal integration of high-resolution EEG and functional magnetic resonance imaging data: a simulation study. Neuroimage. 19, 1-15.

Babiloni,F., Babiloni,C., Carducci,F., Romani,G.L., Rossini,P.M., Angelone,L.M., and Cincotti,F. (2004). Multimodal integration of EEG and MEG data: a simulation study with variable signal-to-noise ratio and number of sensors. Hum.Brain Mapp. 22, 52-62.

Babiloni,F., Carducci,F., Babiloni,C., and Urbano,A. (1998). Improved realistic Laplacian estimate of highly-sampled EEG potentials by regularization techniques. Electroencephalogr.Clin.Neurophysiol. 106, 336-343.

**Rolando Grave de Peralta**

Grave de Peralta,R., Hauk,O., and Gonzalez,S.L. (2009). The neuroelectromagnetic inverse problem and the zero dipole localization error. Comput.Intell.Neurosci. 659-247.

Grave de Peralta-Menendez,R. and Gonzalez-Andino,S.L. (1998). A critical analysis of linear inverse solutions to the neuroelectromagnetic inverse problem. IEEE Trans.Biomed.Eng 45, 440-448.

Grave de Peralta-Menendez,R., Murray,M.M., Michel,C.M., Martuzzi,R., and Gonzalez Andino,S.L. (2004).
Electrical neuroimaging based on biophysical constraints. Neuroimage. 21, 527-539.

**Laura Astolfi and Fabrizio De Vico Fallani**

Astolfi,L., Cincotti,F., Mattia,D., De Vico Fallani,F., Tocci,A., Colosimo,A., Salinari,S., Marciani,M.G., Hesse,W., Witte,H., Ursino,M., Zavaglia,M., and Babiloni,F. (2008). Tracking the time-varying cortical connectivity patterns by adaptive multivariate estimators. IEEE Trans.Biomed.Eng 55, 902-913.

Astolfi,L., Cincotti,F., Mattia,D., Marciani,M.G., Baccala,L.A., De Vico Fallani,F., Salinari,S., Ursino,M., Zavaglia,M., and Babiloni,F. (2006). Assessing cortical functional connectivity by partial directed coherence: simulations and application to real data. IEEE Trans.Biomed.Eng 53, 1802-1812.

Astolfi,L., Cincotti,F., Mattia,D., Marciani,M.G., Baccala,L.A., De Vico Fallani,F., Salinari,S., Ursino,M., Zavaglia,M., Ding,L., Edgar,J.C., Miller,G.A., He,B., and Babiloni,F. (2007). Comparison of different cortical connectivity estimators for high-resolution EEG recordings. Hum.Brain Mapp. 28, 143-157.

Baccala,L.A. and Sameshima,K. (2001). Partial directed coherence: a new concept in neural structure determination. Biol.Cybern. 84, 463-474.

De Vico Fallani,F., Astolfi,L., Cincotti,F., Mattia,D., Marciani,M.G., Gao,S., Salinari,S., Soranzo,R., Colosimo,A., and Babiloni,F. (2008a). Structure of the cortical networks during successful memory encoding in TV commercials. Clin.Neurophysiol. 119, 2231-2237.

De Vico Fallani,F., Astolfi,L., Cincotti,F., Mattia,D., Marciani,M.G., Salinari,S., Kurths,J., Gao,S., Cichocki,A., Colosimo,A., and Babiloni,F. (2007). Cortical functional connectivity networks in normal and spinal cord injured patients: Evaluation by graph analysis. Hum.Brain Mapp. 28, 1334-1346.

De Vico Fallani,F., Astolfi,L., Cincotti,F., Mattia,D., Marciani,M.G., Tocci,A., Salinari,S., Witte,H., Hesse,W., Gao,S., Colosimo,A., and Babiloni,F. (2008b). Cortical network dynamics during foot movements. Neuroinformatics. 6, 23-34.

Kaminski,M., Ding,M., Truccolo,W.A., and Bressler,S.L. (2001). Evaluating causal relations in neural
systems: granger causality, directed transfer function and statistical assessment of significance. Biol.Cybern.
85, 145-157.

**Maciej Kaminski**

Blinowska,K.J. and Kaminski,M. (2006). Multivariate Signal Analysis by Parametric Models. In Handbook of Time Series Analysis, B. Schelter, M. Winterhalder, and J. Timmer, eds. (Weinheim: Wiley-VCH Verlag), pp. 387-420.

Kaminski,M. and Liang,H. (2005). Causal influence: advances in neurosignal analysis. Crit Rev.Biomed.Eng 33,
347-430.