@article{makeig_mining_2004,
	title = {Mining event-related brain dynamics},
	volume = {8},
	issn = {13646613},
	url = {https://pubmed.ncbi.nlm.nih.gov/15120678/},
	doi = {10.1016/j.tics.2004.03.008},
	abstract = {This article provides a new, more comprehensive view of event-related brain dynamics founded on an information-based approach to modeling electroencephalographic (EEG) dynamics. Most EEG research focuses either on peaks 'evoked' in average event-related potentials (ERPs) or on changes 'induced' in the EEG power spectrum by experimental events. Although these measures are nearly complementary, they do not fully model the event-related dynamics in the data, and cannot isolate the signals of the contributing cortical areas. We propose that many ERPs and other EEG features are better viewed as time/frequency perturbations of underlying field potential processes. The new approach combines independent component analysis (ICA), time/frequency analysis, and trial-by-trial visualization that measures EEG source dynamics without requiring an explicit head model.},
	number = {5},
	urldate = {2021-05-10},
	journal = {Trends in Cognitive Sciences},
	author = {Makeig, Scott and Debener, Stefan and Onton, Julie and Delorme, Arnaud},
	year = {2004},
	pmid = {15120678},
	note = {Publisher: Elsevier Ltd},
	keywords = {Action Potentials / physiology*, Arnaud Delorme, Brain / physiology*, Brain Mapping / instrumentation*, Computer-Assisted, Electroencephalography / methods*, Evoked Potentials / physiology*, Humans, Image Processing, MEDLINE, Magnetoencephalography / methods, Models, NCBI, NIH, NLM, National Center for Biotechnology Information, National Institutes of Health, National Library of Medicine, Neurological, Non-U.S. Gov't, PubMed Abstract, Reproducibility of Results, Research Support, Review, Scott Makeig, Stefan Debener, doi:10.1016/j.tics.2004.03.008, pmid:15120678},
	pages = {204--210},
}

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We propose that many ERPs and other EEG features are better viewed as time/frequency perturbations of underlying field potential processes. The new approach combines independent component analysis (ICA), time/frequency analysis, and trial-by-trial visualization that measures EEG source dynamics without requiring an explicit head model.","number":"5","urldate":"2021-05-10","journal":"Trends in Cognitive Sciences","author":[{"propositions":[],"lastnames":["Makeig"],"firstnames":["Scott"],"suffixes":[]},{"propositions":[],"lastnames":["Debener"],"firstnames":["Stefan"],"suffixes":[]},{"propositions":[],"lastnames":["Onton"],"firstnames":["Julie"],"suffixes":[]},{"propositions":[],"lastnames":["Delorme"],"firstnames":["Arnaud"],"suffixes":[]}],"year":"2004","pmid":"15120678","note":"Publisher: Elsevier Ltd","keywords":"Action Potentials / physiology*, Arnaud Delorme, Brain / physiology*, Brain Mapping / instrumentation*, Computer-Assisted, Electroencephalography / methods*, Evoked Potentials / physiology*, Humans, Image Processing, MEDLINE, Magnetoencephalography / methods, Models, NCBI, NIH, NLM, National Center for Biotechnology Information, National Institutes of Health, National Library of Medicine, Neurological, Non-U.S. Gov't, PubMed Abstract, Reproducibility of Results, Research Support, Review, Scott Makeig, Stefan Debener, doi:10.1016/j.tics.2004.03.008, pmid:15120678","pages":"204–210","bibtex":"@article{makeig_mining_2004,\n\ttitle = {Mining event-related brain dynamics},\n\tvolume = {8},\n\tissn = {13646613},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/15120678/},\n\tdoi = {10.1016/j.tics.2004.03.008},\n\tabstract = {This article provides a new, more comprehensive view of event-related brain dynamics founded on an information-based approach to modeling electroencephalographic (EEG) dynamics. 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