High-Density Adaptive Ten Ten: Proposal for Electrode Nomenclature for High-Density EEG

Document Type

Article

Abstract

PURPOSE: High-density EEG (HD-EEG) systems and electrical source imaging techniques have revolutionized our ability to assess the potential sources of epileptiform activity and other EEG features. Nonetheless, clinical use of HD-EEG is hampered by the lack of a standardized electrode nomenclature system and the inherent difficulties encountered in visually reviewing recordings. Inefficient visual review of HD-EEG remains a major barrier to incorporating these techniques into routine clinical care. METHODS: Extension of the 10-10 is first defined by the addition of 2 reference curves: the -10% and -20% axial reference curves. Electrode positions over the face are named based on facial bony structures (N = nasion, Z = zygomatic prominence, M = mandible) and over the back of the head on posterior landmarks (I = inion, S = subinion, B = Base). Then, following the 10% incremental distance rule, we define additional electrode positions. Electrodes with nonstandard positions are clustered around the closest 10-10 electrode, deemed their cardinal point. RESULTS: The 256-electrode Geodesic Sensor Net mapped to 96 of the 120 extended 10-10 cardinal electrodes. CONCLUSIONS: Electrode position nomenclature that builds upon the international standard 10-10 system allows electroencephalographers to identify spatial areas of interest in HD-EEG relative to positions in routine use. A standard viewing montage for HD-EEG and its application with electrical source imaging boost efficiency when reviewing data and improve accuracy in recognizing epileptiform discharges. Additionally, our proposed system is not limited to a specific HD-EEG system, electrode count, or electrode layout.

Publication Date

5-1-2020

Publication Title

Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society

E-ISSN

15371603

Volume

37

Issue

3

First Page

263

Last Page

270

PubMed ID

31490287

Digital Object Identifier (DOI)

10.1097/WNP.0000000000000632

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