For better or worse, I wrote a traditional dissertation instead of a more practical “staple” dissertation composed primarily of journal articles. Hence there are some potentially useful things in it that will likely never be read beyond my committee and, perhaps, people willing to search the thing piecemeal on Google Books. Perhaps this blog can improve the situation a bit.
Anyway, for my thesis I had the fortune of working with Scott Makeig, who pioneered the use of ICA for EEG analysis (Makeig, Bell, Jung, & Sejnowski, 1996). ICA is the best general-purpose tool out there for correcting for EEG artifacts such as blinks and muscle activity. Artifact correction in general isn’t perfect (Groppe, Makeig, & Kutas, 2008), but in practice ICA artifact correction works quite well (Mognon, Jovicich, Bruzzone, & Buiatti, 2011).
One issue facing ICA newbies though is learning to recognize which independent components (ICs) represent artifacts and which reflect true neural activity. In case it helps here are few prototypical examples of artifact ICS. In each figure is the IC’s scalp topography, activity ERPimage, and power spectrum. If you load an EEGLAB dataset into EEGLAB, you can make a plot like this for each IC using Plot->Component properties in the EEGLAB GUI menu (see below).
Makeig, S., Bell, A. J., Jung, T.-P., & Sejnowski, T. J. (1996). Independent component analysis of electroencephalographic data Advances in Neural Information Processing Systems, 145-151
Groppe, D. M., Makeig, S., & Kutas, M. (2008). Independent component analysis of event-related potentials. Cognitive Science Online, 6 (1), 1-44
Mognon A, Jovicich J, Bruzzone L, & Buiatti M (2011). ADJUST: An automatic EEG artifact detector based on the joint use of spatial and temporal features. Psychophysiology PMID: 20636297