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

Michelmann, Sebastian, Treder, Matthias S. ORCID:, Griffiths, Benjamin, Kerrén, Casper, Roux, Frédéric, Wimber, Maria, Rollings, David, Sawlani, Vijay, Chelvarajah, Ramesh, Gollwitzer, Stephanie, Kreiselmeyer, Gernot, Hamer, Hajo, Bowman, Howard, Staresina, Bernhard and Hanslmayr, Simon 2018. Data-driven re-referencing of intracranial EEG based on independent component analysis (ICA). Journal of Neuroscience Methods 307 , pp. 125-137. 10.1016/j.jneumeth.2018.06.021

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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(s) 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.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Publisher: Elsevier
ISSN: 0165-0270
Date of First Compliant Deposit: 12 October 2018
Date of Acceptance: 25 June 2018
Last Modified: 15 Nov 2023 19:58

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