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

Sebastian Michelmann, Matthias S. Treder, Benjamin Griffiths, Casper Kerrén, Frédéric Roux, Maria Wimber, David Rollings, Vijay Sawlani, Ramesh Chelvarajah, Stephanie Gollwitzer, Gernot Kreiselmeyer, Hajo Hamer, Howard Bowman, Bernhard Staresina, Simon Hanslmayr

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)125-137
Number of pages13
JournalJournal of Neuroscience Methods
StatePublished - Sep 1 2018


  • Bipolar reference
  • Depth electrodes
  • Electroencephalography
  • Independent component analysis
  • Intracranial EEG
  • Neuroscience
  • Preprocessing
  • Referencing

ASJC Scopus subject areas

  • General Neuroscience


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