Computer vision based algorithm to support correct electrode placement for home-based non-invasive electric brain stimulation

Abstract Non-invasive brain stimulation (NIBS), such as transcranial electrical (tES), has been proposed a promising treatment strategy for neuropsychiatric pathologies in addition to conventional methods. To achieve larger outreach and ensure regular, high-frequency exposure, home-based, self-applied solutions have promoted new strategy. There are currently no available assess the correct placement of tES electrodes at home or guide patients during self-application process. address these challenges, we developed tested an easy-to-use cost-effective tool monitor cap performance support home. Thirty-six healthy participants took part this study. We compared their with NIBS-expert investigator, testing participants’ accuracy instructions before (Pre) after investigator’s (Post), well using computer vision (Algorithm). For feasibility evaluation novel technique, user experience (UX) confidence levels were tested. Permutation tests showed that performed Algorithm significantly closer 3D space expert Pre. The UX questionnaire subjective measures ease use usefulness algorithm satisfactory. decreases variability performance, ensures within suggested maximum deviation, supports placement. conclude technology adds distinguishable value home-based set-ups helping electrode them real-time feedback. It also induces provides exciting opportunity further promote safe adjunct strategy, can high frequency exposure while being easily integrated patients’ daily life. Research Category Technology Methods Translational Research: 9. Transcranial Direct Current Stimulation (tDCS) Keywords: NIBS, localisation algorithm, feedback,