P094 Method for EEG guided transcranial Electrical Stimulation without models. Issue 3 (March 2017)

• Record Type: Journal Article
• Authors: Cancelli, A.; Cottone, C.; Tecchio, F.; Truong, D.; Dmochowski, J.; Adair, D.; Bikson, M.
• Is Part Of: Clinical neurophysiology. Volume 128:Issue 3(2017:Mar.)
• Page Start: e54
• Page End: e56
• Publication Date: 2017-03
• Subjects: Neurophysiology -- Periodicals; Electroencephalography -- Periodicals; Electromyography -- Periodicals; Neurology -- Periodicals; 612.8
• DOI: 10.1016/j.clinph.2016.10.219
• Languages: English
• ISSNs: 1388-2457

Abstract

Abstract : Objective: There is a long interest in using EEG measurements to inform transcranial Electrical Stimulation (tES) but adoption is lacking. The conventional approach is to use anatomical head-models for both source localization (the EEG inverse problem) and current flow modeling (the tES forward model), but this approach is computationally demanding, requires an anatomical MRI, and strict assumptions about the target brain regions. We evaluate techniques whereby tES dose is derived from EEG without the need for an anatomical head model or assumptions. Approach: The approaches are verified using a Finite Element Method (FEM) simulation of the EEG generated by a dipole, oriented either tangential or radial to the surface, and then simulating brain current flow produced by various model-free techniques including: (1) Voltage-to-voltage, (2) Voltage-to-Current; (3) Laplacian; and two Ad-Hoc techniques (4) Dipole sink-to-sink; and (5) Sink to Concentric Ring. These model-free approaches are compared to a numerically optimized dose that assumes perfect understanding of the dipole location and head anatomy. We vary the number of electrodes from a few to over three hundred, with focality or intensity as optimization criterion. Main results: Our results demonstrate how simple Ad-Hoc approaches can achieve reasonable targeting for the case of a cortical dipole with 2–8 electrodes and no need for a model of the head. Significance: For its simplicity and linearity, model-free EEG guided lends itself to broad adoption and can be applied to a static (tDCS), time-variant (e.g. tACS, tRNS, tPCS), or closed-loop tES.