  Objective: Transcranial direct current stimulation (tDCS) is a non-invasive
brain stimulation technique used to generate conduction currents in the head
and disrupt brain functions. To rapidly evaluate the tDCS-induced current
density in near real-time, this paper proposes a deep learning-based emulator,
named DeeptDCS. Methods: The emulator leverages Attention U-net taking the
volume conductor models (VCMs) of head tissues as inputs and outputting the
three-dimensional current density distribution across the entire head. The
electrode configurations are also incorporated into VCMs without increasing the
number of input channels; this enables the straightforward incorporation of the
non-parametric features of electrodes (e.g., thickness, shape, size, and
position) 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) in
terms of accuracy. The generalization ability of DeeptDCS to non-trained
electrode configurations can be greatly enhanced through fine-tuning the model.
The computational time required by one emulation via DeeptDCS is a fraction of
a second. Conclusion: DeeptDCS is at least two orders of magnitudes faster than
a physics-based open-source simulator, while providing satisfactorily accurate
results. Significance: The high computational efficiency permits the use of
DeeptDCS in applications requiring its repetitive execution, such as
uncertainty quantification and optimization studies of tDCS.

Quantitative Methods;Machine Learning;Signal Processing