Expanding Access to Neuromodulation Through Home-Based Platforms: Balancing Scalability, Safety, and Personalized Stimulation
DOI: Pending DOI
Synopsis
Home-based neuromodulation platforms are increasingly being explored as telehealth infrastructure expands and demand grows for scalable neuropsychiatric interventions. However, implementation remains limited by tensions between scalability and safety, standardization and personalization, and accessibility and regulatory oversight. This narrative review evaluated current evidence on the feasibility, safety optimization, and personalization of home-based transcranial electrical stimulation (tES). Findings identified major challenges in stimulation variability, adherence monitoring, and a lack of standardized regulatory frameworks. Addressing these barriers is essential for the safe, evidence-based expansion of home-based neuromodulation technologies.
Background
Transcranial electrical stimulation (tES), including direct current (tDCS), alternating current (tACS), and random noise stimulation (tRNS), has traditionally required clinic-based administration by trained specialists [1,2]. As a result, access to neuromodulation remains limited by geographic disparities, high treatment costs, and limited specialist availability [2,3]. Growing evidence supporting neuromodulation in depression, post-stroke recovery, and neurodegenerative disorders has increased interest in home-based treatment platforms [4,5]. Advances in portable stimulation devices, wearable biosensors, smartphone-guided interfaces, and cloud-based monitoring systems have enabled investigation of remotely supervised neuromodulation protocols [6]. Home-based tES has demonstrated feasibility across multiple neuropsychiatric and neurologic conditions; however, expansion has outpaced development of standardized frameworks for personalization, safety monitoring, regulatory oversight, and treatment reproducibility [7,8]. Interindividual variability in skull thickness, electrode positioning, and cortical anatomy may substantially alter delivered electric field magnitude despite identical stimulation parameters [9]. Use outside of clinical oversight raises concerns regarding unsupervised self-administration, regulation, and long-term safety monitoring [10]. This narrative review evaluates current home-based neuromodulation technologies, implementation barriers, and strategies to support safe, individualized, and scalable treatment delivery.
Methods
A thorough narrative review of the current literature on home-based neuromodulation technologies was conducted. This review included published studies, review articles, clinical trials, and regulatory discussions regarding remote delivery of neuromodulation. We primarily focused on literature published between 2010 and 2025 while also including earlier foundational studies when relevant, as this period corresponds with the emergence and proliferation of home-use devices. Areas of focus included, but were not limited to, portable transcranial electrical stimulation devices, wearable neuromodulation innovations, frameworks for remote monitoring, computational targeting strategies, the integration of digital biomarkers, and regulatory aspects pertinent to decentralized neuromodulation care. The evidence was categorized thematically into three main areas: (1) safety and profiles of adverse events in settings without direct supervision or with remote oversight; (2) personalized dosing along with models of electric fields; and (3) regulatory and ethical standards for home-use monitoring. Due to variability in outcome measures and methodologies across the studies, a quantitative meta-analysis was not conducted.
Results
Current home-based neuromodulation options have significant potential to expand access to neuropsychiatric and neurologic treatment by reducing geographical, financial, and logistical barriers associated with clinic-based care [11]. Portable tES systems currently represent the most extensively investigated home-based modality due to their relatively low cost, portability, and favorable safety profile. Emerging wearable and digitally integrated stimulation systems further support the feasibility of remotely supervised neuromodulation paradigms.
A review of current literature indicated that home-based tES was well tolerated, with mild, transient adverse effects [11]. Remotely supervised tDCS via videoconference-guided electrode placement has shown adherence exceeding 90% across neurologic populations, including multiple sclerosis cohorts, with studies demonstrating cognitive outcomes comparable to conventional cognitive training and greater improvement in complex attention and intra-individual variability [12]. These findings suggest that remotely supervised neuromodulation platforms may facilitate broader patient inclusion and improved accessibility. However, unsupervised consumer device use has been associated with increased rates of skin irritation, electrode misplacement, and stimulation of unintended cortical regions [8,13]. Contraindication screening for implanted neurostimulation devices, seizure history, and dermatologic conditions has also been inconsistent outside of clinic settings.
Additional implementation barriers include variability in skull thickness, cortical anatomy, electrode positioning, and stimulation dosing, contributing to inconsistent treatment outcomes and limited personalization. Current tES protocols often use fixed parameters without accounting for these variabilities, leading to significant differences in cortical electric field magnitude for identical surface doses [9,14]. Barriers also exist regarding adherence monitoring, limited integration of physiologic biomarkers, and uncertainty regarding long-term safety surveillance.
Several emerging strategies may improve scalability while preserving treatment precision and safety. Finite element modeling of individualized head anatomy derived from structural MRI allows optimization of electrode montage and current intensity to achieve target electric field thresholds at intended cortical regions [14]. Although MRI-based personalization remains impractical in most home settings, development of atlas-based and machine learning-derived head models may provide scalable alternatives for individualized dosing [15]. Integration of wearable physiologic sensing, adaptive closed-loop stimulation systems, and remote clinician-supervised platforms incorporating digital adherence tracking and symptom monitoring may further improve treatment responsiveness and adverse-event detection, while providing a scalable framework for decentralized neuromodulation care.
Regulatory oversight of home-based tES remains fragmented. In the United States, general wellness devices fall outside FDA enforcement priorities under the General Wellness Policy, while clinical-indication devices require 510(k) clearance or De Novo authorization [16]. A Breakthrough Device Designation granted to a prescription tDCS platform targeting major depressive disorder establishes precedent for expedited clinical review of home-use neuromodulation devices [17].
Collectively, current evidence suggests that successful scalability of home-based neuromodulation will depend on balancing accessibility with robust personalization, safety monitoring, and regulatory standardization, rather than simply expanding device availability.
Discussion
Home-based tES is at a unique intersection of clinical need, advancing technology, and shifting regulations. To ensure a responsible approach, three main challenges highlighted in this review must be addressed. First, while scalability calls for standardization, increasing evidence favors personalized, anatomy-informed dosing, indicating the need for computational tools that facilitate such personalization outside clinical environments. Second, safety in unsupervised settings can be improved by leveraging the device's built-in intelligence, such as impedance monitoring, session tracking, and automated checks for contraindications, rather than relying solely on clinician oversight. Lastly, regulatory systems should adopt a more nuanced model for home use with appropriate levels of oversight, moving beyond the simplistic clinical-versus-consumer binary that may no longer adequately address modern home-use neuromodulation. Key areas for further research include: validating atlas-based head modeling for personalized home use, developing wearable devices for real-time brain-state monitoring to enable adaptive delivery, and creating longitudinal safety monitoring systems for consumer tES user groups. A collaborative approach involving device manufacturers, healthcare professionals, ethicists, and regulators is crucial to promote the evidence-based and equitable expansion of home-based neuromodulation.
Disclosures
Nothing to disclose by any authors.
References
1. Yang C, Jung B, Lee S-H. Transcranial electrical stimulation: Clinical implication and practice for treatment of psychiatric illness. Clinical Psychopharmacology and Neuroscience. 2023;22(3):391-404. doi:10.9758/cpn.23.1118
2. Charvet LE, Shaw MT, Bikson M, et al. Supervised transcranial direct current stimulation (tDCS) at home: A guide for clinical research and practice. Brain Stimulation. 2020;13(3):686-693. doi:10.1016/j.brs.2020.02.011
3. Venkatraman V, Futch BG, Bartlett A, et al. Disparities in access to deep brain stimulation and responsive neurostimulation approaches to drug-resistant epilepsy. Neuromodulation: Technology at the Neural Interface. 2024;27(4):792-799. doi:10.1016/j.neurom.2023.11.007
4. Brunoni AR, Moffa AH, Fregni F, et al. Transcranial direct current stimulation for acute major depressive episodes: Meta-analysis of individual patient data. British Journal of Psychiatry. 2016;208(6):522-531. doi:10.1192/bjp.bp.115.164715
5. Lefaucheur J-P, Antal A, Ayache SS, et al. Evidence-based guidelines on the therapeutic use of transcranial direct current stimulation (tDCS). Clinical Neurophysiology. 2017;128(1):56-92. doi:10.1016/j.clinph.2016.10.087
6. Gupta N, Kasula V, Sanmugananthan P, et al. SmartWear body sensors for neurological and neurosurgical patients: A review of current and future technologies. World Neurosurgery: X. 2024;21:100247. doi:10.1016/j.wnsx.2023.100247
7. Cappon D, den Boer T, Jordan C, et al. Safety and feasibility of tele-supervised home-based transcranial direct current stimulation for major depressive disorder. Frontiers in Aging Neuroscience. 2022;13. doi:10.3389/fnagi.2021.765370
8. Sandran N, Hillier S, Hordacre B. Strategies to implement and monitor in-home transcranial electrical stimulation in neurological and psychiatric patient populations: A systematic review. Journal of NeuroEngineering and Rehabilitation. 2019;16(1). doi:10.1186/s12984-019-0529-5
9. Datta A, Bansal V, Diaz J, et al. Gyri-precise head model of transcranial direct current stimulation: Improved spatial focality using a ring electrode versus conventional rectangular pad. Brain Stimulation. 2009;2(4). doi:10.1016/j.brs.2009.03.005
10. Antal A, Bjekić J, Ganho-Ávila A, et al. Low intensity transcranial electric stimulation: Safety, ethical, legal regulatory and application guidelines (2017–2025: An update). Clinical Neurophysiology. 2026;184:2111436. doi:10.1016/j.clinph.2025.2111436
11. Pathak H, Suhas S, Sreeraj VS, et al. Home-based transcranial electrical stimulation (TES) for the management of psychiatric disorders: A scoping review. Indian Journal of Psychological Medicine. 2025;48(3):232-250. doi:10.1177/02537176251387028
12. Charvet L, Shaw M, Dobbs B, et al. Remotely supervised transcranial direct current stimulation increases the benefit of at-home cognitive training in multiple sclerosis. Neuromodulation: Technology at the Neural Interface. 2018;21(4):383-389. doi:10.1111/ner.12583
13. Woodham RD, Selvaraj S, Lajmi N, et al. Home-based transcranial direct current stimulation treatment for major depressive disorder: A fully remote phase 2 randomized sham-controlled trial. Nature Medicine. 2024;31(1):87-95. doi:10.1038/s41591-024-03305-y
14. Opitz A, Paulus W, Will S, et al. Determinants of the electric field during transcranial direct current stimulation. NeuroImage. 2015;109:140-150. doi:10.1016/j.neuroimage.2015.01.033
15. Huang Y, Parra LC. Fully automated whole-head segmentation with improved smoothness and continuity, with theory reviewed. PLOS ONE. 2015;10(5). doi:10.1371/journal.pone.0125477
16. U.S. Food and Drug Administration. Medical device regulatory pathways: Wellness, 510(k), De Novo. FDA.gov. Accessed May 26, 2026. [combined pages]
17. U.S. Food and Drug Administration. Premarket approval document: P230024B. FDA.gov. Accessed May 26, 2026.