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.
Pre-processing
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.
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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.
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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
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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.
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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.
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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
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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.
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