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

Michelmann S, Treder MS, Griffiths B, Kerren C, Roux F, Wimber M, Rollings D, Sawlani V, Chelvarajah R, Gollwitzer S, Kreiselmeyer G, Hamer H, Bowman H, Staresina B, Hanslmayr S (2018)

Publication Type: Journal article

Publication year: 2018

Journal

Journal of Neuroscience Methods Elsevier

Book Volume: 307

Pages Range: 125-137

DOI: 10.1016/j.jneumeth.2018.06.021

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(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.

Authors with CRIS profile

Gernot Kreiselmeyer Department of Neurology Hajo Hamer Professur für Epileptologie

Involved external institutions

University of Birmingham GB United Kingdom (GB) Queen Elizabeth Hospital Birmingham GB United Kingdom (GB)

How to cite

APA:

Michelmann, S., Treder, M.S., Griffiths, B., Kerren, C., Roux, F., Wimber, M.,... Hanslmayr, S. (2018). Data-driven re-referencing of intracranial EEG based on independent component analysis (ICA). Journal of Neuroscience Methods, 307, 125-137. https://dx.doi.org/10.1016/j.jneumeth.2018.06.021

MLA:

Michelmann, Sebastian, et al. "Data-driven re-referencing of intracranial EEG based on independent component analysis (ICA)." Journal of Neuroscience Methods 307 (2018): 125-137.

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