The following sections are intended provide a brief overview of some different types of analyses for EEG data. This is not, however, an extensive list and new methods and publications are coming out all the time. The references are intended to be a starting list for interested individuals and are by no means comprehensive.
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Pre-processing is an important start to any EEG analysis. It involves organising and ‘cleaning up’ the raw data. Many EEG analysis software tools have useful online tutorials that cover software-specific pre-processing steps - see the Analysis section below for links to these tutorials.
Useful references
Delorme, A., & Makeig, S. (2004). EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of neuroscience methods, 134(1), 9–21.
Ille N, Berg P, Scherg M. (2002). Artifact correction of the ongoing EEG using spatial filters based on artifact and brain signal topographies. Journal of Clinical Neurophysiology 19:113-24.
Picton TW, van Roon P, Armilio ML, Berg P, Ille N, Scherg M. (2000). The correction of ocular artifacts: a topographic perspective. Clinical Neurophysiology 111:53-65.
Berg P, Scherg M. (1994). A multiple source approach to the correction of eye artifacts. Electroencephalography & Clinical Neurophysiology 90:229-41.
Litvak V, Komssi S, Scherg M, Hoechstetter K, Classen J, Zaaroor M, Pratt H, Kahkonen S. (2007). Artifact correction and source analysis of early electroencephalographic responses evoked by transcranial magnetic stimulation over primary motor cortex. NeuroImage 37:56–70.
Lins OG, Picton TW, Berg P, Scherg M. (1993). Ocular artifacts in recording EEGs and event-related potentials. II: Source dipoles and source components. Brain Topography 6:65-78.
Event-Related Potentials (ERPs)
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The analysis of ERPs is possible in most EEG software and analysis packages.
Useful references
Maris, E. (2012). Statistical testing in electrophysiological studies. Psychophysiology., 49(4):549-65.
Non-parametric permutation analyses
Non-parametric methods can refer to a variety of different techniques and can be utilised in many different ways. Brain activity data tends to break the ‘rules’ for parametric statistical tests. Cluster-based permutation analyses can be used to overcome the multiple comparisons problem for large datasets.
Video introduction
http://www.cogsci.ucsd.edu/~dgroppe/EEGLAB12_statistics.html
Useful references
Galán, L., Biscay, R., Rodríguez, J. L., Pérez-Abalo, M. C., & Rodríguez, R. (1997). Testing topographic differences between event related brain potentials by using non-parametric combinations of permutation tests. Electroencephalography and clinical neurophysiology, 102(3), 240–7. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/9129579
Karniski, W., Blair, R. C., & Snider, a D. (1994). An exact statistical method for comparing topographic maps, with any number of subjects and electrodes. Brain topography, 6(3), 203–10. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/8204407
Nichols, T. E., & Holmes, A. P. (2002). Nonparametric permutation tests for functional neuroimaging: a primer with examples. Human brain mapping, 15(1), 1–25. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/11747097
Singh, K. D., Barnes, G. R., & Hillebrand, A. (2003). Group imaging of task-related changes in cortical synchronisation using nonparametric permutation testing. NeuroImage, 19(4), 1589–1601.
Xu, Y., Sudre, G. P., Wang, W., Weber, D. J., & Kass, R. E. (2011). Characterizing global statistical significance of spatiotemporal hot spots in magnetoencephalography/ electroencephalography source space via excursion algorithms. Statistics in medicine, 30(23), 2854–66.
Maris E., Oostenveld R. (2007). Nonparametric statistical testing of EEG- and MEG-data. J Neurosci Methods.
Topographic Analyses
Topographic analysis refers to an analysis of how the amplitude of recorded activity is distributed across scalp electrode locations. Topographic maps typically show a view of the scalp and are shaded according to the amplitude of the activity measured at each electrode. There are several ways in which these maps can be used and/or analysed statistically.
Useful references
Murray, M. M., Brunet, D., & Michel, C. M. (2008). Topographic ERP analyses: a step-by-step tutorial review. Brain topography, 20(4), 249–64.
Skrandies, W. (1990). Global field power and topographic similarity. Brain topography, 3(1), 137–41. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/2094301
Brunet, D., Murray, M. M., & Michel, C. M. (2011). Spatiotemporal analysis of multichannel EEG: CARTOOL. Computational intelligence and neuroscience, 2011, 813870.
Frequency Analyses
In addition to providing data about where on the scalp and when in time differences in brain activity occur, EEG data can also be separated into different frequency bands for analysis. Different frequency bands have, in the past, been related to cognitive processes and provide a third dimension for EEG analysis. Frequency information can be analysed in combination with electrode and/or timing information. It is possible to analyse the frequencies of induced and evoked activity.
Frequency analyses can be carried out in most of the software described in the ‘Analysis Software’ section below. However, the types of frequency analysis that are possible can vary between software.
Useful references
Mitra PP, Pesaran B. (1999) Analysis of dynamic brain imaging data. Biophys J., 76(2):691-708.
Jarvis MR, Mitra PP. (2001). Sampling properties of the spectrum and coherency of sequences of action potentials. Neural Comput., 13(4):717-49.
Tallon-Baudry C, Bertrand O, Delpuech C, Permier J. (1997). Oscillatory gamma-band (30-70 Hz) activity induced by a visual search task in humans. J Neurosci., 17(2):722-34.
Source Localisation
High-density EEG (typically 64+ channels) can be used to infer the sources of the brain that are active during different experimental conditions. This involves using an inverse model to estimate the activity in the brain that is likely to have produced certain patterns of activity on the scalp. Note that the spatial resolution of this type of analysis is limited in EEG compared to MEG or fMRI, but is nevertheless desirable for some experiments.
Useful references
Michel, C. M., Murray, M. M., Lantz, G., Gonzalez, S., Spinelli, L., & Grave de Peralta, R. (2004). EEG source imaging. Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology, 115(10), 2195–222. doi:10.1016/j.clinph.2004.06.001
Dale AM, Liu AK, Fischl B, Buckner RL, Belliveau JW, Lewine JD, Halgren E (2000): Dynamic statistical parametric mapping: combining fMRI and MEG to produce high-resolution spatiotemporal maps of cortical activity. Neuron 26:55-67.
Arthur K. Liu, Anders M. Dale, and John W. Belliveau (2002): Monte Carlo Simulation Studies of EEG and MEG Localization Accuracy. Human Brain Mapping 16:47-62.
Fa-Hsuan Lin, Thomas Witzel, Matti S. Hamalainen, Anders M. Dale, John W. Belliveau, and Steven M. Stufflebeam (2004): Spectral spatiotemporal imaging of cortical oscillations and interactions in the human brain. NeuroImage 23:582-595.
Mosher JC, Lewis PS, Leahy RM. (1992). Multiple dipole modeling and localization from spatio-temporal MEG data. IEEE Trans Biomed Eng., 39(6):541-57.
Mosher JC, Baillet S, Leahy RM. (1999) EEG source localization and imaging using multiple signal classification approaches. J Clin Neurophysiol., 16(3):225-38.
Van Veen BD, van Drongelen W, Yuchtman M, Suzuki A. (1997). Localization of brain electrical activity via linearly constrained minimum variance spatial filtering. IEEE Trans Biomed Eng. 44(9):867-80.
Gross J, Kujala J, Hamalainen M, Timmermann L, Schnitzler A, Salmelin R. (2001). Dynamic imaging of coherent sources: Studying neural interactions in the human brain. Proc Natl Acad Sci USA., 16;98(2):694-9.
Fuchs M, Kastner J, Wagner M, Hawes S, Ebersole J.S. (2002). A standardized boundary element method volume conductor model. Clin Neurophysiol., 113(5):702-12.
Oostendorp T, van Oosterom A. (1991). The potential distribution generated by surface electrodes in inhomogeneous volume conductors of arbitrary shape. IEEE Trans Biomed Eng, 38(5):409-17.
R. Kavanagh, T. M. Darccey, D. Lehmann, and D. H. Fender. (1978). Evaluation of methods for three-dimensional localization of electric sources in the human brain. IEEE Trans Biomed Eng, 25:421-429.
Dipole Fitting
A different method of inferring activity in the brain is to place ‘dipoles’ in certain regions of the brain and to estimate the ‘activity’ in these dipoles from the activity on the scalp.
The BESA Website provides some information about dipole fitting (although note that the BESA software itself is not freely-available). Nevertheless, the BESA Website provides a free dipole simulator tool (http://www.besa.de/updates/tools/).
Dynamic Causal Modelling (DCM)
For an introduction to DCM, check the Scholarpedia page: http://www.scholarpedia.org/article/Dynamic_causal_modeling
Useful references
Kiebel, S. J., Garrido, M. I., Moran, R. J., & Friston, K. J. (2008). Dynamic causal modelling for EEG and MEG. Cognitive neurodynamics, 2(2), 121–36.
Pinotsis, D. a, Schwarzkopf, D. S., Litvak, V., Rees, G., Barnes, G., & Friston, K. J. (2012). Dynamic causal modelling of lateral interactions in the visual cortex. NeuroImage, 66C, 563–576.