About: Data-driven re-referencing of intracranial EEG based on independent component analysis (ICA)

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type	http://eprints.org/ontology/ArticleEPrint http://eprints.org/ontology/EPrint Article Academic Article
seeAlso	HTML Summary of #67587 Data-driven re-referencing of intracranial EEG based on independent component analysis (ICA)
sameAs	Data-driven re-referencing of intracranial EEG based on independent component analysis (ICA)
http://eprints.org/ontology/hasAccepted	Data-driven re-referencing of intracranial EEG based on independent component analysis (ICA) (PDF)
http://eprints.org/ontology/hasDocument	Data-driven re-referencing of intracranial EEG based on independent component analysis (ICA) (PDF) Data-driven re-referencing of intracranial EEG based on independent component analysis (ICA) (Other) Data-driven re-referencing of intracranial EEG based on independent component analysis (ICA) (Other) Data-driven re-referencing of intracranial EEG based on independent component analysis (ICA) (Other) Data-driven re-referencing of intracranial EEG based on independent component analysis (ICA) (Other) Data-driven re-referencing of intracranial EEG based on independent component analysis (ICA) (Other)
dc:hasVersion	Data-driven re-referencing of intracranial EEG based on independent component analysis (ICA) (PDF)
Title	Data-driven re-referencing of intracranial EEG based on independent component analysis (ICA)
described by	https://demo.openlinksw.com/about/id/entity/https/www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3
Date	2018-09-01
Creator	Simon Hanslmayr Benjamin Griffiths David Rollings Matthias S. Treder Ramesh Chelvarajah Sebastian Michelmann Bernhard Staresina Gernot Kreiselmeyer Frédéric Roux Casper Kerrén Hajo Hamer Vijay Sawlani Stephanie Gollwitzer Maria Wimber Howard Bowman
status	peer reviewed published
Publisher	Elsevier
abstract	Background: Intracranial recordings from patients implanted with depth electrodes are a valuable source of information in neuroscience. They allow for the unique opportunity to record brain activity with high spatial and temporal resolution. A common pre-processing choice in stereotactic EEG (S-EEG) is to re-reference the data with a bipolar montage. In this, each channel is subtracted from its neighbor, to reduce commonalities between channels and isolate activity that is spatially confined. New Method: We challenge the assumption that bipolar reference effectively performs this task. To extract local activity, the distribution of the signal source of interest, interfering distant signals, and noise need to be considered. Referencing schemes with fixed coefficients can decrease the signal to noise ratio (SNR) of the data, they can lead to mislocalization of activity and consequently to misinterpretation of results. We propose to use Independent Component Analysis (ICA), to derive filter coefficients that reflect the statistical dependencies of the data at hand. Results: We describe and demonstrate this on human S-EEG recordings. In a simulation with real data, we quantitatively show that ICA outperforms the bipolar referencing operation in sensitivity and importantly in specificity when revealing local time series from the superposition of neighboring channels. Comparison with Existing Method: We argue that ICA already performs the same task that bipolar referencing pursues, namely undoing the linear superposition of activity and will identify activity that is local. Conclusions: When investigating local sources in human S-EEG, ICA should be preferred over re-referencing the data with a bipolar montage.
Is Part Of	Journal of Neuroscience Methods https://kar.kent.ac.uk/id/repository
Subject	QA 75 Electronic computers. Computer science R Medicine
list of authors	https://kar.kent.ac.uk/id/eprint/67587#authors
volume	307
is topic of	https://www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3
is primary topic of	HTML Summary of #67587 Data-driven re-referencing of intracranial EEG based on independent component analysis (ICA)

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