**6. Conclusions**

DBS programming provides options beyond conventional parameter selection. Access to these parameters is particularly important for addressing ET that does not respond to conventional DBS parameters [12] or develops habituation [13,24]. Deep-learning modalities might be able to further refine this approach to avoid supra-therapeutic stimulation, minimize battery consumption, and enable the titration of more complex devices [46,47]. The role of the individual DBS parameters in tremor control remains elusive. Our proof of concept underscores the interdependence of voltage, pulse width, and frequency, warranting further research.

**Supplementary Materials:** The following are available online at http://www.mdpi.com/2077-0383/9/6/1855/s1, Figure S1: Overall tremor outcomes; Figure S2. Experimental Set-up, Table S1. Characteristics of studies included in the meta-analysis.

**Author Contributions:** M.B., I.D.B., J.M.C.v.D. and T.v.L. designed the study. M.B. and I.D.B. collected the data. M.B., I.D.B., G.D., T.v.L. and D.L.M.O. analyzed and interpreted the data. M.B. and I.D.B. conducted the statistical analysis. I.D.B. drafted the article. M.B., J.M.C.v.D., G.D., T.v.L. and D.L.M.O. critically revised the article and reviewed the submitted version of the manuscript. M.B. supervised the study. The final version of the manuscript was approved by all authors. All authors have read and agreed to the published version of the manuscript.

**Funding:** Universitair Medisch Centrum Groningen: 2017-1.

**Acknowledgments:** This work was supported by the Healthy Aging Pilot funding (2017-1) from the University Medical Center Groningen.

**Conflicts of Interest:** None of the authors has any conflict of interest to disclose.
