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Objective: Transcranial direct current stimulation (tDCS) is a non-invasivebrain stimulation technique used to generate conduction currents in the headand disrupt brain functions. To rapidly evaluate the tDCS-induced currentdensity in near real-time, this paper proposes a deep learning-based emulator,named DeeptDCS. Methods: The emulator leverages Attention U-net taking thevolume conductor models (VCMs) of head tissues as inputs and outputting thethree-dimensional current density distribution across the entire head. Theelectrode configurations are also incorporated into VCMs without increasing thenumber of input channels; this enables the straightforward incorporation of thenon-parametric features of electrodes (e.g., thickness, shape, size, andposition) in the training and testing of the proposed emulator. Results:Attention U-net outperforms standard U-net and its other three variants(Residual U-net, Attention Residual U-net, and Multi-scale Residual U-net) interms of accuracy. The generalization ability of DeeptDCS to non-trainedelectrode configurations can be greatly enhanced through fine-tuning the model.The computational time required by one emulation via DeeptDCS is a fraction ofa second. Conclusion: DeeptDCS is at least two orders of magnitudes faster thana physics-based open-source simulator, while providing satisfactorily accurateresults. Significance: The high computational efficiency permits the use ofDeeptDCS in applications requiring its repetitive execution, such asuncertainty quantification and optimization studies of tDCS.

DeeptDCS: Deep Learning-Based Estimation of Currents Induced During Transcranial Direct Current Stimulation