Abstract
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 language | English |
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Pages (from-to) | 327-335 |
Number of pages | 9 |
Journal | NeuroImage |
Volume | 183 |
Early online date | 17 Aug 2018 |
DOIs | |
Publication status | Published - 1 Dec 2018 |
Bibliographical note
Copyright © 2018 Elsevier Inc. All rights reserved.Funding
This work was supported by grants from the National Institutes of Health ( P41-EB018783 , P50-MH109429 ), US Army Research Office ( W911NF-14-1-0440 ), Fondazione Neurone , National Natural Science Foundation of China (No. 61761166006 , No. 51475292 ), and the Natural Science Foundation and Major Basic Research Program of Shanghai (No. 16JC1420102 ). We would like to thank Dr. Brendan Allison for his help editing the paper. Appendix A
Keywords
- Noise subtraction
- Referencing method
- SEEG
- Signal quality
- Stereo-electroencephalography
ASJC Scopus subject areas
- Neurology
- Cognitive Neuroscience
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Dingguo Zhang
- Department of Electronic & Electrical Engineering - Reader in Robotics Engineering
- UKRI CDT in Accountable, Responsible and Transparent AI
- Centre for Bioengineering & Biomedical Technologies (CBio)
- Bath Institute for the Augmented Human
- IAAPS
Person: Research & Teaching, Core staff, Affiliate staff