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Brain-Computer Interfaces for Vision Recovery in Precortical Vision Loss

Authors Yang CD ORCID logo, Guo A, Lin KY ORCID logo

Received 7 November 2025

Accepted for publication 5 March 2026

Published 12 March 2026 Volume 2026:18 561691

DOI https://doi.org/10.2147/EB.S561691

Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 4

Editor who approved publication: Dr Rustum Karanjia



Christopher D Yang,1 Alan Guo,2 Ken Y Lin1– 3

1Gavin Herbert Eye Institute, University of California Irvine, Irvine, CA, USA; 2Department of Computer Science, University of California Irvine, Irvine, CA, USA; 3Department of Biomedical Engineering, University of California Irvine, Irvine, CA, USA

Correspondence: Ken Y Lin, Gavin Herbert Eye Institute, Department of Computer Science, University of California Irvine, 850 Health Sciences Road, Irvine, CA, 92697-4375, USA, Tel +1 949-824-2020, Email [email protected]

Introduction: Precortical vision loss remains a major global health challenge. Advances in brain-computer interfaces (BCIs) offer a new pathway towards restoring functional vision by bypassing damaged structures in the visual pathway.
Methods: This narrative review aims to synthesize the current evidence on BCIs for precortical vision recovery, including non-invasive and invasive techniques. Device design, testing, and outcomes are discussed, with an emphasis on developments in technology and engineering.
Results: Non-invasive BCIs induce neuroplasticity and may restore vision in conditions of precortical vision loss such as glaucoma and optic neuropathy. Cortical visual prostheses demonstrate the ability to evoke visual precepts and recover functional vision. Integration of artificial intelligence and high-density electrode arrays has improved image encoding and device adaptability to enhance user experience and rehabilitation potential. Patient selection, safety, and long-term outcomes remain active areas of investigation.
Discussion: BCIs present a paradigm shift in treating precortical blindness that offers hope for patients with no alternative options. Yet, challenges persist, including surgical risks, durability, and variability in response. Personalization of stimulation protocols and further technical refinement are needed to optimize efficacy and accessibility.
Conclusion: BCIs are a promising experimental modality for precortical vision restoration. Continued research and interdisciplinary collaboration are essential to address current limitations.

Plain Language Summary: Vision loss has a negative impact on functional independence and well-being. Conventional treatments like medications or surgery cannot always effectively manage vision loss caused by damage to the visual pathway. Therefore, researchers are exploring new ways to treat vision loss using brain-computer interfaces (BCIs) capable of bypassing damaged visual tissue. Studies have shown that BCIs can help people with blindness regain visual function when other treatments are not effective. Moreover, complementary technologies such as artificial intelligence have made BCIs more customizable. This review aims to discuss the theoretical underpinnings, history, progress, and challenges of using BCIs to treat patients experiencing vision loss, with a focus on translational potential.

Keywords: brain computer interfaces, neuroprostheses, neuroplasticity, blindness, vision restoration

Introduction

Vision impairment and blindness are major global health problems that affect the functional independence of billions of people worldwide and impose significant societal and economic costs.1 Conventional approaches to preventing blindness, such as pharmacotherapy and invasive surgery, are limited by adverse side effects, high costs, and treatment failure. These limitations are especially pronounced in cases of advanced vision loss, where restoration of meaningful vision is often unattainable. For example, patients suffering from disorders of the retina or optic nerve have few effective treatments to choose from. Novel modalities for vision restoration such as targeted gene therapy and optogenetic approaches have been reported in the literature as theoretically effective treatments for these conditions, but face constraints in immune rejection and limited procedural and surgical access.2,3

Recent advances in brain-computer interface (BCI) technology offer a paradigm shift towards a new treatment modality for advanced vision loss that bypasses damaged peripheral visual pathways for direct stimulation of the visual cortex.2 Although reviews on BCIs have been published,3 new developments in bioengineering and artificial intelligence (AI) have rapidly accelerated the development of novel visual BCIs. The present review synthesizes the theoretical foundations, history, evidence, risks and benefits, and future directions of utilizing BCIs for vision restoration, with a focus on their translational potential for ophthalmologists managing vision loss.

Materials and Methods

Literature Search

A comprehensive search of the current body of peer-reviewed evidence on BCIs for vision loss was conducted on PubMed using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) keywords. The query utilized the following search terms: “(brain-computer interface) AND (vision) AND ((recovery) OR (restoration))”. Titles and abstracts of articles were independently evaluated by the authors. Final article selection was performed independently by the authors after reviewing full-text files. References cited in articles from our initial query were also manually reviewed. All reviewed articles were evaluated for (I) a clear vision-related indication for BCI use. All reviewed articles were subject to the following exclusion criteria: (I) studies written in a language other than English and (II) studies evaluating non-human subjects. All reviewed articles were subject to the following inclusion criteria: (I) studies written in English and (II) primary clinical studies involving BCI use for visual indications. No restrictions were placed on the included number of patients or patient demographics.

Results

BCIs for Vision Restoration: A Primer

BCIs encompass a wide array of devices used for various clinical indications that can be classified broadly into non-invasive and invasive categories. In simple terms, BCIs connect the electrical activity of the brain to an external device. Commonly known examples of non-invasive BCIs include electroencephalography (EEG) monitors used for the diagnosis of sleep disorders and epilepsy,4,5 functional magnetic resonance imaging (fMRI) used for functional brain mapping,6 and transmagnetic stimulation (TMS), a non-invasive medical treatment for mood disorders.7 Known examples of invasive BCIs include deep brain stimulator implants used in the treatment of epilepsy and Parkinson’s Disease8 and assistive speech generators for patients suffering from aphasia caused by motor neuron disease9,10 or vertebrobasilar stroke.11

The first report of the BCI was authored by Berger in 1924, when he described the identification and wake-sleep cycle correlation of discrete alpha and beta waveforms on EEG.12 Several theoretical papers tabulating the possible applications of BCIs in animal research13 and human rehabilitation14 were subsequently published in the late 1900s. The turn of the 21st century brought increased public awareness to the BCI as a clinical tool after several paralyzed patients successfully regained motor,15–18 communication,19,20 and even sensory function21,22 after BCI implantation.

The theoretical basis of using BCIs for vision restoration relies on encoding images of the outside world into excitatory electrochemical signals that stimulate neurons comprising the different components of the visual pathway (retina, optic nerve, optic tract, thalamus, optic radiations, visual cortices, etc)., subsequently generating the sensation of basic visual precepts that patients can perceive without external visual input.23,24 These stimulated sensations, known colloquially as phosphenes, could feasibly be combined and reaggregated in various ways to construct representations of the external world.23,25 One major benefit of using BCI technology for vision restoration is it could cure patients suffering from any etiology of vision loss along the entire length of the visual pathway. For example, both optic nerve BCIs26,27 and cortical BCIs28 used intraoperatively during stereotactic neurosurgery in the conscious patient have been reported to be effective in eliciting phosphenes.

Strengths and Limitations of Brain-Computer Interfaces for Vision Restoration

BCIs present many theoretical advantages for patients with vision loss. First, they bypass damaged anatomy, allowing for recovery of visual function even when parts of the visual pathway are focally non-functional.29,30 As such, they have the potential to restore vision for patients with blindness secondary to pathologies where conventional therapies are ineffective (eg. stroke, phthisis bulbi, enucleated eye). In the example of an enucleated patient, a BCI would bypass the absent eye and convert external visual input processed through a camera into patterned electrical stimulation delivered directly to the visual cortex to generate phosphenes. Second, the possibility of a direct interface between BCIs and cortical visual centers presents the additional benefit of functional neuroplasticity; repetitive excitation of cortical areas responsible for vision necessarily strengthens the networks connecting these regions to the parietal lobe through Hebbian plasticity and cortical remapping. Third, the ability of BCIs to integrate with existing prosthetic frameworks as well as semiconductor and artificial intelligence (AI) architectures provide multiple avenues to enhance resolution, stability, and adaptability for real-time phosphene simulation, thereby reducing cognitive burden.31,32

If successfully implemented, BCIs would likely provide patients with more autonomy and functional independence than alternative proposed solutions.33–36 However, BCIs carry just as many theoretical risks. These include the immediate surgical risks of perioperative infection, hemorrhage, and immune rejection,33 as well the long-term surgical risks of device-related obsolescence and need for reoperation. Two meta-analyses evaluating the prevalence of surgical site infections for implanted deep brain stimulators estimated a pooled infection risk of 4–5%; this risk is likely similar to that of the theoretical risk of BCIs.37,38 The potential for maladaptive plasticity and abnormal or unwanted cortical activity, psychophysical and neurocognitive burdens, extensive postoperative rehabilitation, and device maintenance must also be considered, especially in patients who do not achieve satisfactory postoperative visual function. Other important considerations include a limited body of evidence on BCI durability as well as regulatory barriers borne from a scarcity of large-scale multicenter trials evaluating device standardization and long-term safety data.29,31,34,39 Perhaps most important, however, are the ethical and societal concerns related to patient privacy, autonomy, and inequitable access to a costly novel therapeutic modality. BCIs present a new class of concerns regarding patient privacy. Monitoring neural data may reveal sensitive cognitive and behavioral information vulnerable to misuse or surveillance and could jeopardize patient autonomy through proprietary algorithms that blur the boundaries between user agency and machine influence. Finally, inequitable representation in clinical trials as well as geographic disparities in governance risk incorporating structural bias into BCI development and access.31,40

Non-invasive and invasive BCI prosthetics offer distinct strengths and limitations. Non-invasive BCIs, such as sensory substitution devices (SSDs) and superficial cortical stimulators, are safe, avoid the risks of invasive neurosurgery, and are more affordable and accessible. They leverage the cortical adaptive process of “intersensory plasticity”, which allows the brain to process and integrate visual information through the non-visual senses.31 However, they carry limitations, including reduced spatiotemporal resolution and lower fidelity, that reduce the richness of the restored visual experience.41 Non-invasive BCIs also require extensive training and adaptation, meaning that patients with altered mentation may not achieve meaningful gains in functional vision. On the other hand, invasive BCIs such as cortical prostheses directly stimulate the visual cortex, enabling the generation of higher resolution phosphenes that bypass damaged visual pathways. However, invasive BCIs also carry clear weaknesses, including the aforementioned surgical risks and long-term concerns related to biocompatibility and ongoing neuropsychological assessment.3 To aid comparison, Figure 1 outlines key differences between non-invasive and invasive BCIs.

Figure 1 Visual pathway and BCI intervention points.

Challenges of Existing Approaches to Vision Restoration

Of the existing therapeutic approaches to restoring vision, there are three that have been extensively studied: behavioral rehabilitation techniques designed to harness the plasticity of the visual cortex, such as vision restoration therapy (VRT); non-invasive electromagnetic stimulation of the visual pathway; and regenerative genetic and prosthetic interventions that replace irreparably damaged visual parenchyma.

VRT is a non-invasive computer-based vision rehabilitation modality designed to treat visual field deficits secondary to pathologic insult posterior to the retina. The theoretical basis of VRT is that surviving neurons adjacent to the neurons comprising the damaged part of the cortical visual tract have preserved perceptual potential, and this potential can be activated with repetitive visual stimulation. This repetitive stimulation is thought to activate neuroplasticity, strengthen synaptic transmission, and synchronize the partially injured parenchyma as well as downstream neuronal networks, thereby enhancing visual function.42 The theoretical benefits of VRT are that (I) it is non-invasive and hence presents minimal risk to the blind patient; (II) it can be done at any time after the visual tract is damaged; (III) it is broadly applicable to all types of vision loss, including but not limited to glaucoma, amblyopia, stroke, and trauma. Its limitations include (I) a high treatment burden with sustained improvement only attainable in patients who adhere to regular treatment;42 (II) difficulty in obtaining a precise prognostic timeline given the heterogeneity of visual pathway injury; (III) a requirement for good cognitive function and mental arousal for treatment adherence, which is often suboptimal in patients with severe vision loss.43 Several Phase II studies investigating the efficacy of VRT in patients with visual field deficits stemming from different etiologies have been conducted. Sabel et al found that a three-month course of daily VRT led to significant gains in visual function on perimetry testing in a small sample of patients with severe glaucoma.42 Jung et al found that a three-to-six-month course of VRT caused a trend towards improved visual function in a sample of ten patients with anterior ischemic optic neuropathy.44 Plow et al found that a three-month course of VRT in combination with low-power transcranial direct current stimulation (tDCS) of the occipital cortex led to improvement in visual function compared to controls.45 Despite these findings, interpretation of these studies is confounded by small and heterogenous samples with inconsistent VRT regimens and incongruent statistical methods (Plow et al conducted a per-protocol analysis whereas Sabel and Jung conducted an intention-to-treat analysis). As the quality of evidence on VRT remains poor, larger trials studying more clearly defined VRT regimens on patients suffering from a wider breadth of etiologies of vision loss are warranted.

Non-invasive electromagnetic stimulation (ES) of the visual pathway has emerged as a promising minimally-invasive approach to restoring visual function for all forms of vision loss. The scientific basis of ES relies on artificial induction of visual neuronal activity with an external electromagnetic current. ES was first conceptually reported in 1755 by French academic Charles LeRoy, who generated a perceptible phosphene in a patient blinded by bilateral cataract via application of a low-power transcorneal electrical current.46 In the following centuries, academicians throughout Europe corroborated similar findings. Today, there are multiple established ES modalities, including but not limited to repetitive transorbital alternating current stimulation (rtACS),47–49 transdermal electrical stimulation (TdES),50,51 and transcorneal electrical stimulation (TcES).52–54 The parameters for each of these modalities vary, but all involve week-long courses of low-frequency electrical current applied at the milliamp scale. Randomized studies have reported good efficacy of these modalities in improving visual function in patients with optic neuropathy47,49 and stabilizing electroretinogram degradation and retinal vascular perfusion indices in patients with retinitis pigmentosa (RP),51–54 an incurable inherited retinal dystrophy characterized by progressive photoreceptor degradation. Despite the promising body of evidence surrounding ES as a non-invasive modality for treating vision loss, there remain challenges related to targeted stimulation of damaged visual neurons. For instance, the studies cited above only evaluate ES in patients with defects of the retina or optic nerve, but not of the cortical visual pathway. This is presumably due to limitations in penetrance, which makes the process of selective neuronal activation difficult.55,56 Accurate retinotopic mapping of the neurons of the cortical visual pathway is also difficult due to the small size of the lateral geniculate nucleus and optic tracts, their anatomical depth, and complex laminar structure. When applied to the cortices, the electromagnetic signal generated through ES accumulates in the superficial gyri and underlying white matter, with rapid attenuation in deeper cortical regions. This is due to the intrinsic resistive properties of cerebral tissue and known inverse relationship between electromagnetic field strength and distance from the generator coil.57 As with VRT, despite an abundance of small trials reporting positive changes in physiologic parameters reflecting visual function, the quality of evidence surrounding ES-based approaches for vision restoration remains poor, with most of these studies conducted on small cohorts with varying degrees of disease severity.

More robust evidence on regenerative interventions for vision loss is emerging, with most published studies being early Phase I or II trials investigating gene therapies for inherited retinal dystrophies. The most significant data in support of gene therapy for vision loss was reported in 2017 in a large multicenter Phase III trial assessing functional visual improvement after intravitreal administration of an adenoviral vector targeting mutations of the RPE65 gene in patients with Leber congenital amaurosis (LCA) and RP.58 In this study, clinically significant improvements in visual function were seen over a three year follow up period.59 Subsequent meta-analyses corroborated these findings in larger patient cohorts,60,61 establishing gene therapy as a viable and durable treatment option for repairing damaged retinal tissue in select inherited retinal dystrophies mediated by mutations in the RPE65 gene. On the other hand, evidence surrounding tissue-based and prosthetic interventions for vision loss is more nascent. Two Phase I/II trials assessing the feasibility of transplanting retinal pigment epithelium (RPE) cells derived from differentiated human embryonic stem cells (hESC) into the subretinal space in eyes with age-related macular degeneration (AMD) demonstrated good viability at 12 months and 4 years after transplant, respectively;62,63 a follow up study of hESCs derived from autologous marrow is currently underway in China.64 A separate Phase I study assessing transplantation of an RPE “patch” derived from hESCs in eyes with advanced AMD demonstrated good viability and improved visual function at 12 months after transplant.65

Retinal prosthetic devices, colloquially known as “bionic eyes”, offer an alternative approach to vision loss caused by photoreceptor atrophy. These commercially-available devices are, in essence, BCIs that capture images of the external world and convert them into electrical signals detectable by surviving inner retinal neurons, leading to activation of the surviving retinal neural network.66,67 (Figure 1 and Table 1) Multiple prospective trials have demonstrated that the Argus® Retinal Prosthesis System, one such device manufactured by American company Second SightTM, durably improves light perception and object recognition at two to five years after implantation.68–71 However, these devices offer only limited functional vision gains, with many patients achieving best-corrected visual acuity (BCVA) in the range of 20/1000 to 20/400.72,73 It is also important to note the adverse events associated with these prosthetic interventions, including but not limited to conjunctival erosion, retinal detachment, need for revision surgery, and immunosuppressive risks. Given that most evidence on prosthetic approaches to vision loss demonstrates marginal functional benefit, continued clinical trials focused on improving functional visual outcomes are warranted for this class of therapies.

Table 1 BCI Subtypes for Vision Restoration. Darkness of Red Background in the “Invasiveness” Column Highlights Degree of Invasiveness

Translating Cortical Stimulation into Vision Restoration

The conceptual foundation of using cortical BCIs for vision restoration emerged from decades of academic investigation. Brindley74 and Dobelle75 were the first to report that electrical stimulation of the visual cortex could evoke phosphenes in humans during the 1960s and 1970s. This seminal discovery established the basis of cortical stimulation for vision restoration and the foundation for all subsequent research on cortical stimulation in patients with vision loss. Since their discovery, there has been a clear paradigm shift towards development of BCI-based cortical approaches for patients with damage to the visual pathway.2,76,77 The United States Food and Drug Administration (FDA) has played a central role in this process through consistent regulatory engagement, yielding a robust ecosystem for expedited device development while ensuring adherence to safety standards. Today, there are several ongoing trials investigating cortical BCIs for vision restoration, including Blindsight®, manufactured by Neuralink;78 the PRIMA subretinal implant designed for central vision loss caused by AMD, manufactured by Science Corporation;79,80 the GennarisTM Bionic Vision System® which utilizes nanowire technology to generate uniquely high-resolution phosphenes;81 and the Orion®, manufactured by Second Sight.82,83 The investigators overseeing the PRIMA subretinal implant trial recently published a 2025 Quarter 4 report demonstrating durable restoration of functional vision in a cohort of 38 patients with geographic atrophy, the most advanced form of dry AMD.84 Notably, all cortical BCIs designed to treat vision loss are experimental at this time, with no comprehensive published data from large-scale randomized controlled trials. A comprehensive list of the different BCI subtypes is listed in Table 1.

Technical Advances in Brain-Computer Interfaces for Vision Restoration

Recent technical advances in the development of BCIs intended to restore vision have centered on high-density electrode arrays and the integration of bioengineering and AI to enhance performance.

Technical advances in the research and development of high-density electrode arrays have allowed for acquisition of greater anatomic and spatial resolution, enabling more precise and durable BCI-mediated cortical stimulation for phosphene evocation.2 These arrays are often invasively implanted, which allows complex and nuanced visual information to be delivered directly to the visual cortex. Commercial examples of BCIs utilizing high-density electrode arrays include the Orion® Visual Cortical Prosthesis System,2,85 which utilizes a subdural grid of 60 surface microelectrodes aggregated on a millimeter-sized semiconductor chip; (Table 1) the Blindsight®, an investigational cortical BCI designed by Neuralink with advanced robotic and AI capabilities that recently acquired “breakthrough device” designation by the United States FDA after demonstrating durable vision restoration in primate studies;7,86 and the flexible thin-film Layer 7® cortical interface manufactured by Precision NeuroscienceTM, the most clinically advanced BCI currently in the early stages of clinical development.87,88

Many of these new-age BCIs incorporate AI to supplement the user experience. For example, the CORTIVIS® was designed in parallel with Neurolight®, a powerful deep learning model that improves information transfer rates by performing object detection tasks that enhance encoding of electrical input by retinal ganglion cells.30 (Figure 1) Other examples of AI integrations include the generation of auditory descriptions of visual scenes to enhance rehabilitation by inducing intersensory plasticity31,32 and image preprocessing and segmentation to enhance phosphene fidelity and object recognition in the profoundly blind.29,32,33,89,90

Novel bioengineering techniques have also been increasingly incorporated into BCI systems to enhance translational potential. Optogenetic techniques that directly activate surviving neurons using visible or infrared light have undergone intensive study91–93 due to their ability to provide greater spatial resolution and more natural-appearing vision than traditional electrode-based systems. (Figure 1) However, technical challenges exist in delivering adequate amounts of light to deep cortical structures. Magnetic stimulation and magnetically-responsive nanoparticles have also undergone scrutiny as non-invasive alternatives to selective stimulation of BCI-connected neurons,94,95 though these approaches are still considered experimental. Multisensory paradigms have perhaps seen the most empiric study for BCIs-mediated vision restoration; visual-to-auditory96 and visual-to-tactile97,98 systems that transform external visual input into auditory or tactile sensations have been shown to successfully activate the visual cortex in blind patients, demonstrating good functional repurposing of visual cortical areas for the processing of non-visual input.99–101

Clinical and Translational Potential of BCIs

In the realm of clinical ophthalmology, BCIs expand the therapeutic landscape beyond the eye and offer a potential strategy for patients with precortical blindness who are not candidates for gene therapy, stem cell transplantation, or traditional medical or surgical approaches to vision loss. By bypassing damaged ocular structures and directly stimulating cortical visual pathways, BCIs challenge the long-held anatomic constraints of ophthalmic intervention. Future research is necessary to understand the complete spectrum of clinical use cases for this new class of technology. As larger clinical trials investigating BCIs progress and these devices become integrated in ophthalmologic care, clinicians will play key roles in both selecting and counseling patients as well as monitoring long-term functional outcomes.

Conclusion

BCIs represent a transformative approach to vision recovery that offers new hope for patients with blindness due to irreversible precortical disease. While significant progress has been made in BCI device design and clinical translation, challenges remain in optimizing safety, efficacy, and accessibility. Long-term outcomes, ethical considerations, and regulatory frameworks will require ongoing effort and attention. Continued research and clinical trials are essential to realize the full potential of BCIs for vision restoration.

Data Sharing Statement

All data reported in this study are publicly available.

Acknowledgments

Many thanks to K.Y.L. for his dedication to medical education.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Funding

The authors acknowledge departmental support from an unrestricted grant awarded by Research to Prevent Blindness.

Disclosure

The authors report no conflicts of interest in this work.

References

1. Que L, Zhu Q, Jiang C, Lu Q. An analysis of the global, regional, and national burden of blindness and vision loss between 1990 and 2021: the findings of the global burden of disease study 2021. Front Public Health. 2025;13:1560449. doi:10.3389/fpubh.2025.1560449

2. Beauchamp MS, Oswalt D, Sun P, et al. Dynamic stimulation of visual cortex produces form vision in sighted and blind humans. Cell. 2020;181(4):774–783.e5. doi:10.1016/j.cell.2020.04.033

3. Niketeghad S, Pouratian N. Brain machine interfaces for vision restoration: the current state of cortical visual prosthetics. Neurotherapeutics. 2019;16(1):134–12. doi:10.1007/s13311-018-0660-1

4. Lucey BP, Mcleland JS, Toedebusch CD, et al. Comparison of a single-channel EEG sleep study to polysomnography. J Sleep Res. 2016;25(6):625–635. doi:10.1111/jsr.12417

5. Rosenow F, Klein KM, Hamer HM. Non-invasive EEG evaluation in epilepsy diagnosis. Expert Rev Neurother. 2015;15(4):425–444. doi:10.1586/14737175.2015.1025382

6. Lynch CJ, Power JD, Scult MA, Dubin M, Gunning FM, Liston C. Rapid precision functional mapping of individuals using multi-echo fMRI. Cell Rep. 2020;33(12):108540. doi:10.1016/j.celrep.2020.108540

7. Qi Z, Liu H, Jin F, et al. A wearable repetitive transcranial magnetic stimulation device. Nat Commun. 2025;16(1):2731. doi:10.1038/s41467-025-58095-9

8. Wu YC, Liao YS, Yeh WH, Liang SF, Shaw FZ. Directions of deep brain stimulation for epilepsy and Parkinson’s disease. Front Neurosci. 2021;15:680938. doi:10.3389/fnins.2021.680938

9. Silva AB, Liu JR, Metzger SL, et al. A bilingual speech neuroprosthesis driven by cortical articulatory representations shared between languages. Nat Biomed Eng. 2024;8(8):977–991. doi:10.1038/s41551-024-01207-5

10. Vansteensel MJ, Leinders S, Branco MP, et al. Longevity of a brain-computer interface for amyotrophic lateral sclerosis. N Engl J Med. 2024;391(7):619–626. doi:10.1056/NEJMoa2314598

11. Bacher D, Jarosiewicz B, Masse NY, et al. Neural point-and-click communication by a person with incomplete locked-in syndrome. Neurorehabil Neural Repair. 2015;29(5):462–471. doi:10.1177/1545968314554624

12. Weigeldt. Archiv für Psychiatrie und Nervenkrankheiten. Deutsche Zeitschrift für Nervenheilkunde. 1923;78(6):378–379. doi:10.1007/bf01998099

13. Vidal JJ. Toward direct brain-computer communication. Annu Rev Biophys Bioeng. 1973;2:157–180. doi:10.1146/annurev.bb.02.060173.001105

14. Wolpaw JR, Birbaumer N, Heetderks WJ, et al. Brain-computer interface technology: a review of the first international meeting. IEEE Trans Rehabil Eng. 2000;8(2):164–173. doi:10.1109/tre.2000.847807

15. Brower V. When mind meets machine. EMBO Rep. 2005;6(2):108–110. doi:10.1038/sj.embor.7400344

16. Kingwell K. Neural repair and rehabilitation: neurally controlled robotic arm enables tetraplegic patient to drink coffee of her own volition. Nat Rev Neurol. 2012;8(7):353. doi:10.1038/nrneurol.2012.101

17. Collinger JL, Wodlinger B, Downey JE, et al. High-performance neuroprosthetic control by an individual with tetraplegia. Lancet. 2013;381(9866):557–564. doi:10.1016/S0140-6736(12)61816-9

18. Bouton CE, Shaikhouni A, Annetta NV, et al. Restoring cortical control of functional movement in a human with quadriplegia. Nature. 2016;533(7602):247–250. doi:10.1038/nature17435

19. Birbaumer N, Ghanayim N, Hinterberger T, et al. A spelling device for the paralysed. Nature. 1999;398(6725):297–298. doi:10.1038/18581

20. Rezeika A, Benda M, Stawicki P, Gembler F, Saboor A, Volosyak I. Brain-computer interface spellers: a review. Brain Sci. 2018;8(4):57. doi:10.3390/brainsci8040057

21. Collinger JL, Kryger MA, Barbara R, et al. Collaborative approach in the development of high-performance brain-computer interfaces for a neuroprosthetic arm: translation from animal models to human control. Clin Transl Sci. 2014;7(1):52–59. doi:10.1111/cts.12086

22. Ganzer PD, Colachis SC, Schwemmer MA, et al. Restoring the sense of touch using a sensorimotor demultiplexing neural interface. Cell. 2020;181(4):763–773.e12. doi:10.1016/j.cell.2020.03.054

23. van der Grinten M, de Ruyter van Steveninck J, Lozano A, et al. Towards biologically plausible phosphene simulation for the differentiable optimization of visual cortical prostheses. Elife. 2024;13:e85812. doi:10.7554/eLife.85812

24. Taylor PC, Walsh V, Eimer M. The neural signature of phosphene perception. Hum Brain Mapp. 2010;31(9):1408–1417. doi:10.1002/hbm.20941

25. Guo H, Qin R, Qiu Y, Zhu Y, Tong S. Configuration-based processing of phosphene pattern recognition for simulated prosthetic vision. Artif Organs. 2010;34(4):324–330. doi:10.1111/j.1525-1594.2009.00863.x

26. Veraart C, Raftopoulos C, Mortimer JT, et al. Visual sensations produced by optic nerve stimulation using an implanted self-sizing spiral cuff electrode. Brain Res. 1998;813(1):181–186. doi:10.1016/s0006-8993(98)00977-9

27. Veraart C, Wanet-Defalque MC, Gérard B, Vanlierde A, Delbeke J. Pattern recognition with the optic nerve visual prosthesis. Artif Organs. 2003;27(11):996–1004. doi:10.1046/j.1525-1594.2003.07305.x

28. Marg E, Dierssen G. Reported visual percepts from stimulation of the human brain with microelectrodes during therapeutic surgery. Confin Neurol. 1965;26(2):57–75. doi:10.1159/000104007

29. Rudroff T. Decoding thoughts, encoding ethics: a narrative review of the BCI-AI revolution [retracted in: brain Res. 2025 Oct 9:149969.]. Brain Res. 2025;1850:149423. doi:10.1016/j.brainres.2024.149423

30. Lozano A, Suárez JS, Soto-Sánchez C, et al. Neurolight: a deep learning neural interface for cortical visual prostheses. Int J Neural Syst. 2020;30(9):2050045. doi:10.1142/S0129065720500458

31. Sabel BA, Thut G, Haueisen J, et al. Vision modulation, plasticity and restoration using non-invasive brain stimulation - An IFCN-sponsored review. Clin Neurophysiol. 2020;131(4):887–911. doi:10.1016/j.clinph.2020.01.008

32. Ptito M, Bleau M, Djerourou I, Paré S, Schneider FC, Chebat DR. Brain-machine interfaces to assist the blind. Front Hum Neurosci. 2021;15:638887. doi:10.3389/fnhum.2021.638887

33. Zhang H, Jiao L, Yang S, et al. Brain-computer interfaces: the innovative key to unlocking neurological conditions. Int J Surg. 2024;110(9):5745–5762. doi:10.1097/JS9.0000000000002022

34. Zabcikova M, Koudelkova Z, Jasek R, Lorenzo Navarro JJ. Recent advances and current trends in brain-computer interface research and their applications. Int J Dev Neurosci. 2022;82(2):107–123. doi:10.1002/jdn.10166

35. He R. Perspective of signal processing-based on brain-computer interfaces using machine learning methods. Stud Health Technol Inform. 2023;308:295–302. doi:10.3233/SHTI230853

36. Patel UK, Anwar A, Saleem S, et al. Artificial intelligence as an emerging technology in the current care of neurological disorders. J Neurol. 2021;268(5):1623–1642. doi:10.1007/s00415-019-09518-3

37. Kantzanou M, Korfias S, Panourias I, Sakas DE, Karalexi MA. Deep brain stimulation-related surgical site infections: a systematic review and meta-analysis. Neuromodulation. 2021;24(2):197–211. doi:10.1111/ner.13354

38. Spindler P, Braun F, Truckenmüller P, et al. Surgical site infections associated with implanted pulse generators for deep brain stimulation: meta-analysis and systematic review. Neuromodulation. 2023;26(2):280–291. doi:10.1016/j.neurom.2022.03.014

39. Gabriele Sandrian M, Ng E, Nguyen T, Eydelman M. FDA’s role in expediting innovation of bioelectronic implants for vision restoration. J Neural Eng. 2023;20(3). doi:10.1088/1741-2552/acd8f1

40. Young MJ, Lin DJ, Hochberg LR. Brain-computer interfaces in neurorecovery and neurorehabilitation. Semin Neurol. 2021;41(2):206–216. doi:10.1055/s-0041-1725137

41. Martini ML, Oermann EK, Opie NL, Panov F, Oxley T, Yaeger K. Sensor modalities for brain-computer interface technology: a comprehensive literature review. Neurosurgery. 2020;86(2):E108–E117. doi:10.1093/neuros/nyz286

42. Sabel BA, Gudlin J. Vision restoration training for glaucoma: a randomized clinical trial. JAMA Ophthalmol. 2014;132(4):381–389. doi:10.1001/jamaophthalmol.2013.7963

43. Leitner MC, Ladek AM, Hutzler F, Reitsamer H, Hawelka S. Placebo effect after visual restitution training: no eye-tracking controlled perimetric improvement after visual border stimulation in late subacute and chronic visual field defects after stroke. Front Neurol. 2023;14:1114718. doi:10.3389/fneur.2023.1114718

44. Jung CS, Bruce B, Newman NJ, Biousse V. Visual function in anterior ischemic optic neuropathy: effect of vision restoration therapy--a pilot study. J Neurol Sci. 2008;268(1–2):145–149. doi:10.1016/j.jns.2007.12.001

45. Plow EB, Obretenova SN, Jackson ML, Merabet LB. Temporal profile of functional visual rehabilitative outcomes modulated by transcranial direct current stimulation. Neuromodulation. 2012;15(4):367–373. doi:10.1111/j.1525-1403.2012.00440.x

46. Sehic A, Guo S, Cho KS, Corraya RM, Chen DF, Utheim TP. Electrical stimulation as a means for improving vision. Am J Pathol. 2016;186(11):2783–2797. doi:10.1016/j.ajpath.2016.07.017

47. Gall C, Schmidt S, Schittkowski MP, et al. Alternating current stimulation for vision restoration after optic nerve damage: a randomized clinical trial. PLoS One. 2016;11(6):e0156134. doi:10.1371/journal.pone.0156134

48. Abbas AW, Aboeldahab H, Zeid MA, et al. Non-invasive brain stimulation for treating visual defects: a systematic review and meta-analysis. Neurol Sci. 2025;46(7):3039–3052. doi:10.1007/s10072-025-08069-y

49. Schittkowski M, Pohlner J, Mercieca K, et al. Vision Restoration through transorbital electrical stimulation in Optic Neuropathy in patients with significant optic atrophy due to primary open-angle glaucoma-a randomised, controlled, double-blind, multicentre clinical trial: the VIRON study protocol. BMJ Open. 2025;15(2):e091705. doi:10.1136/bmjopen-2024-091705

50. Miura G, Sugawara T, Kawasaki Y, et al. Clinical trial to evaluate safety and efficacy of transdermal electrical stimulation on visual functions of patients with retinitis pigmentosa. Sci Rep. 2019;9(1):11668. doi:10.1038/s41598-019-48158-5

51. Miura G, Ozawa Y, Shiko Y, et al. Evaluating the efficacy and safety of transdermal electrical stimulation on the visual functions of patients with retinitis pigmentosa: a clinical trial protocol for a prospective, multicentre, randomised, double-masked and sham-controlled design (ePICO trial). BMJ Open. 2022;12(5):e057193. doi:10.1136/bmjopen-2021-057193

52. Schatz A, Pach J, Gosheva M, et al. Transcorneal electrical stimulation for patients with retinitis pigmentosa: a prospective, randomized, sham-controlled follow-up study over 1 year. Invest Ophthalmol Vis Sci. 2017;58(1):257–269. doi:10.1167/iovs.16-19906

53. Bittner AK, Seger K, Salveson R, et al. Randomized controlled trial of electro-stimulation therapies to modulate retinal blood flow and visual function in retinitis pigmentosa. Acta Ophthalmol. 2018;96(3):e366–e376. doi:10.1111/aos.13581

54. Dizdar Yigit D, Sevik MO, Şahin Ö. Transcorneal electrical stimulation therapy may have a stabilization effect on multifocal electroretinography for patients with retinitis pigmentosa. Retina. 2022;42(5):923–933. doi:10.1097/IAE.0000000000003386

55. Gaillet V, Cutrone A, Artoni F, et al. Spatially selective activation of the visual cortex via intraneural stimulation of the optic nerve. Nat Biomed Eng. 2020;4(2):181–194. doi:10.1038/s41551-019-0446-8

56. Borda E, Gaillet V, Airaghi Leccardi MJI, Zollinger EG, Moreira RC, Ghezzi D. Three-dimensional multilayer concentric bipolar electrodes restrict spatial activation in optic nerve stimulation. J Neural Eng. 2022;19(3). doi:10.1088/1741-2552/ac6d7e

57. Siebner HR, Funke K, Aberra AS, et al. Transcranial magnetic stimulation of the brain: what is stimulated? - A consensus and critical position paper. Clin Neurophysiol. 2022;140:59–97. doi:10.1016/j.clinph.2022.04.022

58. Russell S, Bennett J, Wellman JA, et al. Efficacy and safety of voretigene neparvovec (AAV2-hRPE65v2) in patients with RPE65-mediated inherited retinal dystrophy: a randomised, controlled, open-label, Phase 3 trial. Lancet. 2017;390(10097):849–860. doi:10.1016/S0140-6736(17)31868-8

59. Testa F, Maguire AM, Rossi S, et al. Three-year follow-up after unilateral subretinal delivery of adeno-associated virus in patients with Leber congenital Amaurosis type 2. Ophthalmology. 2013;120(6):1283–1291. doi:10.1016/j.ophtha.2012.11.048

60. Wang X, Yu C, Tzekov RT, Zhu Y, Li W. The effect of human gene therapy for RPE65-associated Leber’s congenital amaurosis on visual function: a systematic review and meta-analysis. Orphanet J Rare Dis. 2020;15(1):49. doi:10.1186/s13023-020-1304-1

61. Tuohy GP, Megaw R. A systematic review and meta-analyses of interventional clinical trial studies for gene therapies for the inherited retinal degenerations (IRDs). Biomolecules. 2021;11(5):760. doi:10.3390/biom11050760

62. Mehat MS, Sundaram V, Ripamonti C, et al. Transplantation of human embryonic stem cell-derived retinal pigment epithelial cells in macular degeneration. Ophthalmology. 2018;125(11):1765–1775. doi:10.1016/j.ophtha.2018.04.037

63. Schwartz SD, Tan G, Hosseini H, Nagiel A. Subretinal transplantation of embryonic stem cell-derived retinal pigment epithelium for the treatment of macular degeneration: an assessment at 4 years. Invest Ophthalmol Vis Sci. 2016;57(5):ORSFc1–ORSFc9. doi:10.1167/iovs.15-18681

64. Safety and efficacy of autologous transplantation of iPSC-RPE in the treatment of macular degeneration. ClinicalTrials.gov. 2022. Available from: https://clinicaltrials.gov/study/NCT05445063. Accessed October 25, 2025.

65. da Cruz L, Fynes K, Georgiadis O, et al. Phase 1 clinical study of an embryonic stem cell-derived retinal pigment epithelium patch in age-related macular degeneration. Nat Biotechnol. 2018;36(4):328–337. doi:10.1038/nbt.4114

66. Palanker D. Electronic retinal prostheses. Cold Spring Harb Perspect Med. 2023;13(8):a041525. doi:10.1101/cshperspect.a041525

67. Wu KY, Mina M, Sahyoun JY, Kaleva A, Tran SD. Retinal prostheses: engineering and clinical perspectives for vision restoration. Sensors. 2023;23(13):5782. doi:10.3390/s23135782

68. da Cruz L, Dorn JD, Humayun MS, et al. Five-year safety and performance results from the Argus II retinal prosthesis system clinical trial. Ophthalmology. 2016;123(10):2248–2254. doi:10.1016/j.ophtha.2016.06.049

69. Edwards TL, Cottriall CL, Xue K, et al. Assessment of the electronic retinal implant Alpha AMS in restoring vision to blind patients with end-stage retinitis pigmentosa. Ophthalmology. 2018;125(3):432–443. doi:10.1016/j.ophtha.2017.09.019

70. Ho AC, Humayun MS, Dorn JD, et al. Long-term results from an epiretinal prosthesis to restore sight to the blind. Ophthalmology. 2015;122(8):1547–1554. doi:10.1016/j.ophtha.2015.04.032

71. Finn AP, Grewal DS, Vajzovic L. Argus II retinal prosthesis system: a review of patient selection criteria, surgical considerations, and post-operative outcomes. Clin Ophthalmol. 2018;12:1089–1097. doi:10.2147/OPTH.S137525

72. Ayton LN, Barnes N, Dagnelie G, et al. An update on retinal prostheses. Clin Neurophysiol. 2020;131(6):1383–1398. doi:10.1016/j.clinph.2019.11.029

73. Mills JO, Jalil A, Stanga PE. Electronic retinal implants and artificial vision: journey and present. Eye. 2017;31(10):1383–1398. doi:10.1038/eye.2017.65

74. Brindley GS, Lewin WS. The sensations produced by electrical stimulation of the visual cortex. J Physiol. 1968;196(2):479–493. doi:10.1113/jphysiol.1968.sp008519

75. Dobelle WH, Mladejovsky MG, Evans JR, Roberts TS, Girvin JP. “Braille” reading by a blind volunteer by visual cortex stimulation. Nature. 1976;259(5539):111–112. doi:10.1038/259111a0

76. Beauchamp MS, Bosking WH, Oswalt D, Yoshor D. Raising the stakes for cortical visual prostheses. J Clin Invest. 2021;131(23):e154983. doi:10.1172/JCI154983

77. Sharf T, Kalakuntla T, J Lee D, Gokoffski KK. Electrical devices for visual restoration. Surv Ophthalmol. 2022;67(3):793–800. doi:10.1016/j.survophthal.2021.08.008

78. Fine I, Boynton GM. A virtual patient simulation modeling the neural and perceptual effects of human visual cortical stimulation, from pulse trains to percepts. Sci Rep. 2024;14(1):17400. doi:10.1038/s41598-024-65337-1

79. Palanker D, Le Mer Y, Mohand-Said S, Muqit M, Sahel JA. Photovoltaic restoration of central vision in atrophic age-related macular degeneration. Ophthalmology. 2020;127(8):1097–1104. doi:10.1016/j.ophtha.2020.02.024

80. Muqit MMK, Hubschman JP, Picaud S, et al. PRIMA subretinal wireless photovoltaic microchip implantation in non-human primate and feline models. PLoS One. 2020;15(4):e0230713. doi:10.1371/journal.pone.0230713

81. Rosenfeld JV, Wong YT, Yan E, et al. Tissue response to a chronically implantable wireless intracortical visual prosthesis (Gennaris array). J Neural Eng. 2020;17(4):046001. doi:10.1088/1741-2552/ab9e1c

82. Niketeghad S, Muralidharan A, Patel U, et al. Phosphene perceptions and safety of chronic visual cortex stimulation in a blind subject. J Neurosurg. 2019;132(6):2000–2007. doi:10.3171/2019.3.JNS182774

83. Najarpour Foroushani A, Pack CC, Sawan M. Cortical visual prostheses: from microstimulation to functional percept. J Neural Eng. 2018;15(2):021005. doi:10.1088/1741-2552/aaa904

84. Holz FG, Le Mer Y, Muqit MMK, et al. Subretinal photovoltaic implant to restore vision in geographic atrophy due to AMD. N Engl J Med. 2025;394(3):232–242. doi:10.1056/NEJMoa2501396

85. Orion visual cortical prosthesis system. Available from: https://www.cortigent.com/orion. Accessed October 25, 2025.

86. Yazdan-Shahmorad A, Silversmith DB, Kharazia V, Sabes PN. Targeted cortical reorganization using optogenetics in non-human primates. Elife. 2018;7:e31034. doi:10.7554/eLife.31034

87. Hettick M, Ho E, Poole AJ, et al. Minimally invasive implantation of scalable high-density cortical microelectrode arrays for multimodal neural decoding and stimulation. Nat Biomed Eng. 2025. doi:10.1038/s41551-025-01501-w

88. Fernández E, Normann RA. CORTIVIS approach for an intracortical visual prostheses. Artificial Vision. 2016;191–201. doi:10.1007/978-3-319-41876-6_15

89. Ma J, Rui Z, Zou Y, et al. Neurosurgical and BCI approaches to visual rehabilitation in occipital lobe tumor patients. Heliyon. 2024;10(23):e39072. doi:10.1016/j.heliyon.2024.e39072

90. van Velthoven EAM, van Stuijvenberg OC, Haselager DRE, et al. Ethical implications of visual neuroprostheses-a systematic review. J Neural Eng. 2022;19(2). doi:10.1088/1741-2552/ac65b2

91. Lindner M, Gilhooley MJ, Hughes S, Hankins MW. Optogenetics for visual restoration: from proof of principle to translational challenges. Prog Retin Eye Res. 2022;91:101089. doi:10.1016/j.preteyeres.2022.101089

92. Busskamp V, Roska B, Sahel JA. Optogenetic vision restoration. Cold Spring Harb Perspect Med. 2024;14(8):a041660. doi:10.1101/cshperspect.a041660

93. Stefanov A, Flannery JG. A systematic review of optogenetic vision restoration: history, challenges, and new inventions from bench to bedside. Cold Spring Harb Perspect Med. 2023;13(6):a041304. doi:10.1101/cshperspect.a041304

94. Lu X, Li G, Jiao W, et al. Magnetic nanomaterials-mediated neuromodulation. Wiley Interdiscip Rev Nanomed Nanobiotechnol. 2023;15(4):e1890. doi:10.1002/wnan.1890

95. Smith IT, Zhang E, Yildirim YA, et al. Nanomedicine and nanobiotechnology applications of magnetoelectric nanoparticles. Wiley Interdiscip Rev Nanomed Nanobiotechnol. 2023;15(2):e1849. doi:10.1002/wnan.1849

96. Levy-Tzedek S, Hanassy S, Abboud S, Maidenbaum S, Amedi A. Fast, accurate reaching movements with a visual-to-auditory sensory substitution device. Restor Neurol Neurosci. 2012;30(4):313–323. doi:10.3233/RNN-2012-110219

97. Stronks HC, Mitchell EB, Nau AC, Barnes N. Visual task performance in the blind with the BrainPort V100 vision aid. Expert Rev Med Devices. 2016;13(10):919–931. doi:10.1080/17434440.2016.1237287

98. Nau A, Bach M, Fisher C. Clinical tests of ultra-low vision used to evaluate rudimentary visual perceptions enabled by the BrainPort vision device. Transl Vis Sci Technol. 2013;2(3):1. doi:10.1167/tvst.2.3.1

99. Murphy MC, Nau AC, Fisher C, Kim SG, Schuman JS, Chan KC. Top-down influence on the visual cortex of the blind during sensory substitution. Neuroimage. 2016;125:932–940. doi:10.1016/j.neuroimage.2015.11.021

100. Ortiz T, Poch J, Santos JM, et al. Recruitment of occipital cortex during sensory substitution training linked to subjective experience of seeing in people with blindness. PLoS One. 2011;6(8):e23264. doi:10.1371/journal.pone.0023264

101. Chebat DR, Schneider FC, Ptito M. Spatial competence and brain plasticity in congenital blindness via sensory substitution devices. Front Neurosci. 2020;14:815. doi:10.3389/fnins.2020.00815

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