Optimal referencing for stereo-electroencephalographic (SEEG) recordings

Guangye Li, Shize Jiang, Sivylla E. Paraskevopoulou, Meng Wang, Yang Xu, Zehan Wu, Liang Chen, Dingguo Zhang, Gerwin Schalk

Research output: Contribution to journalArticlepeer-review

67 Citations (SciVal)


Stereo-electroencephalography (SEEG) is an intracranial recording technique in which depth electrodes are inserted in the brain as part of presurgical assessments for invasive brain surgery. SEEG recordings can tap into neural signals across the entire brain and thereby sample both cortical and subcortical sites. However, even though signal referencing is important for proper assessment of SEEG signals, no previous study has comprehensively evaluated the optimal referencing method for SEEG. In our study, we recorded SEEG data from 15 human subjects during a motor task, referencing them against the average of two white matter contacts (monopolar reference). We then subjected these signals to 5 different re-referencing approaches: common average reference (CAR), gray-white matter reference (GWR), electrode shaft reference (ESR), bipolar reference, and Laplacian reference. The results from three different signal quality metrics suggest the use of the Laplacian re-reference for study of local population-level activity and low-frequency oscillatory activity.

Original languageEnglish
Pages (from-to)327-335
Number of pages9
Early online date17 Aug 2018
Publication statusPublished - 1 Dec 2018


  • Noise subtraction
  • Referencing method
  • SEEG
  • Signal quality
  • Stereo-electroencephalography

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience


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