Windel, FabienneGardier, Remy Marc M.Fourchard, GaspardVinals, RoserBavelier, DaphnePadberg, Frank JohannesRancans, ElmarsBonne, OmerNahum, MorThiran, Jean-PhilippeMorishita, TakuyaHummel, Friedhelm Christoph2023-08-282023-08-282023-08-282023-07-1410.1016/j.clinph.2023.06.009https://infoscience.epfl.ch/handle/20.500.14299/200294WOS:001043377300001Objective: Home-based non-invasive brain stimulation (NIBS) has been suggested as an adjunct treatment strategy for neuro-psychiatric disorders. There are currently no available solutions to direct and monitor correct placement of the stimulation electrodes. To address this issue, we propose an easy-touse digital tool to support patients for self-application. Methods: We recruited 36 healthy participants and compared their cap placement performance with the one of a NIBS-expert investigator. We tested participants' placement accuracy with instructions before (Pre) and after the investigator's placement (Post), as well as participants using the support tool (CURRENT). User experience (UX) and confidence were further evaluated. Results: Permutation tests demonstrated a smaller deviation within the CURRENT compared with Pre cap placement (p = 0.02). Subjective evaluation of ease of use and usefulness of the tool were vastly positive (8.04 out of 10). CURRENT decreased the variability of performance, ensured placement within the suggested maximum of deviation (10 mm) and supported confidence of correct placement. Conclusions: This study supports the usability of this novel technology for correct electrode placement during self-application in home-based settings. Significance: CURRENT provides an exciting opportunity to promote home-based, self-applied NIBS as a safe, high-frequency treatment strategy that can be well integrated in patients' daily lives. & COPY; 2023 Published by Elsevier B.V. on behalf of International Federation of Clinical Neurophysiology.Clinical NeurologyNeurosciencesNeurosciences & Neurologyhome -based non-invasive brain stimulationteselectrode localization algorithmreal-time feedbackmonitoringcomputer visionstatetext::journal::journal article::research article