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One of the main challenges of visual prostheses is to augment the perceived information to improve the experience of its wearers. Given the limited access to implanted patients, in order to facilitate the experimentation of new techniques, this is often evaluated via Simulated Prosthetic Vision (SPV) with sighted people. In this work, we introduce a novel SPV framework and implementation that presents major advantages with respect to previous approaches. First, it is integrated into a robotics framework, which allows us to benefit from a wide range of methods and algorithms from the field (e.g. object recognition, obstacle avoidance, autonomous navigation, deep learning). Second, we go beyond traditional image processing with 3D point clouds processing using an RGB-D camera, allowing us to robustly detect the floor, obstacles and the structure of the scene. Third, it works either with a real camera or in a virtual environment, which gives us endless possibilities for immersive experimentation through a head-mounted display. Fourth, we incorporate a validated temporal phosphene model that replicates time effects into the generation of visual stimuli. Finally, we have proposed, developed and tested several applications within this framework, such as avoiding moving obstacles, providing a general understanding of the scene, staircase detection, helping the subject to navigate an unfamiliar space, and object and person detection. We provide experimental results in real and virtual environments. The code is publicly available at https://www.github.com/aperezyus/RASPV

Bionic vision uses neuroprostheses to restore useful vision to people living with incurable blindness. However, a major outstanding challenge is predicting what people "see" when they use their devices. The limited field of view of current devices necessitates head movements to scan the scene, which is difficult to simulate on a computer screen. In addition, many computational models of bionic vision lack biological realism. To address these challenges, we present VR-SPV, an open-source virtual reality toolbox for simulated prosthetic vision that uses a psychophysically validated computational model to allow sighted participants to "see through the eyes" of a bionic eye user. To demonstrate its utility, we systematically evaluated how clinically reported visual distortions affect performance in a letter recognition and an immersive obstacle avoidance task. Our results highlight the importance of using an appropriate phosphene model when predicting visual outcomes for bionic vision.

Blindness affects millions of people around the world. A promising solution to restoring a form of vision for some individuals are cortical visual prostheses, which bypass part of the impaired visual pathway by converting camera input to electrical stimulation of the visual system. The artificially induced visual percept (a pattern of localized light flashes, or ‘phosphenes’) has limited resolution, and a great portion of the field’s research is devoted to optimizing the efficacy, efficiency, and practical usefulness of the encoding of visual information. A commonly exploited method is non-invasive functional evaluation in sighted subjects or with computational models by using simulated prosthetic vision (SPV) pipelines. An important challenge in this approach is to balance enhanced perceptual realism, biologically plausibility, and real-time performance in the simulation of cortical prosthetic vision. We present a biologically plausible, PyTorch-based phosphene simulator that can run in real-time and uses differentiable operations to allow for gradient-based computational optimization of phosphene encoding models. The simulator integrates a wide range of clinical results with neurophysiological evidence in humans and non-human primates. The pipeline includes a model of the retinotopic organization and cortical magnification of the visual cortex. Moreover, the quantitative effects of stimulation parameters and temporal dynamics on phosphene characteristics are incorporated. Our results demonstrate the simulator’s suitability for both computational applications such as end-to-end deep learning-based prosthetic vision optimization as well as behavioral experiments. The modular and open-source software provides a flexible simulation framework for computational, clinical, and behavioral neuroscientists working on visual neuroprosthetics.

Argus II is the most advanced retina implants approved by the US FDA and almost 350 visually impaired people are using it. This implant uses 60 microelectrodes implanted in the retina. The goal of this implant is to improve mobility and quality of life of its users. However, users’ satisfaction is not very high due to the very low resolution of the phosphene images and features created by this device. This article proposes a system to improve the artificial vision created by visual implants. The proposed method extracts information about the people around the visually impaired person by using image processing and machine vision algorithms. This information includes the number of the people in the scene, whether they are known or unknown, their gender, estimated ages, facial emotions, and approximate distance from the visually impaired person. This information is extracted from the frames received by a camera mounted on the glasses of the user to generate signals that are fed into a visual stimulator. This information is shown to the user by a schematic vision created by some pre-trained patterns of phosphenes reflecting the information communicated to the user. The proposed system is validated with a simulated prosthetic vision comprising 150 microelectrodes that is compatible with the retina and visual cortex implants. A low-cost and energy efficient implementation of the proposed method executing on a Raspberry Pi 4 B at a frame rate of 4.5 frames/second shows the feasibility of using it in portable systems.

Abstract Blindness is a main threat that affects the daily life activities of any human. Visual prostheses have been introduced to provide artificial vision to the blind with the aim of allowing them to restore confidence and independence. In this article, we propose an approach that involves four image enhancement techniques to facilitate object recognition and localization for visual prostheses users. These techniques are clip art representation of the objects, edge sharpening, corner enhancement and electrode dropout handling. The proposed techniques are tested in a real-time mixed reality simulation environment that mimics vision perceived by visual prostheses users. Twelve experiments were conducted to measure the performance of the participants in object recognition and localization. The experiments involved single objects, multiple objects and navigation. To evaluate the performance of the participants in objects recognition, we measure their recognition time, recognition accuracy and confidence level. For object localization, two metrics were used to measure the performance of the participants which are the grasping attempt time and the grasping accuracy. The results demonstrate that using all enhancement techniques simultaneously gives higher accuracy, higher confidence level and less time for recognizing and grasping objects in comparison to not applying the enhancement techniques or applying pair-wise combinations of them. Visual prostheses could benefit from the proposed approach to provide users with an enhanced perception.

Blindness is a main threat that affects the daily life activities of any human. Visual prostheses have been introduced to provide artificial vision to the blind with the aim of allowing them to restore confidence and independence. In this article, we propose an approach that involves four image enhancement techniques to facilitate object recognition and localization for visual prostheses users. These techniques are clip art representation of the objects, edge sharpening, corner enhancement and electrode dropout handling. The proposed techniques are tested in a real-time mixed reality simulation environment that mimics vision perceived by visual prostheses users. Twelve experiments were conducted to measure the performance of the participants in object recognition and localization. The experiments involved single objects, multiple objects and navigation. To evaluate the performance of the participants in objects recognition, we measure their recognition time, recognition accuracy and confidence level. For object localization, two metrics were used to measure the performance of the participants which are the grasping attempt time and the grasping accuracy. The results demonstrate that using all enhancement techniques simultaneously gives higher accuracy, higher confidence level and less time for recognizing and grasping objects in comparison to not applying the enhancement techniques or applying pair-wise combinations of them. Visual prostheses could benefit from the proposed approach to provide users with an enhanced perception. [ABSTRACT FROM AUTHOR]

Background: Most existing retinal prostheses contain a built‐in visible‐light camera module that captures images of the surrounding environment. Thus, in case of insufficient or lack of visible light, the camera fails to work, and the retinal prostheses enter a dormant or "OFF" state. A simple and effective solution is replacing the visible‐light camera with a dual‐mode camera. The present research aimed to achieve two main purposes: (1) to explore whether the dual‐mode camera in prosthesis recipients works under no visible‐light conditions and (2) to assess its performance. Methods: To accomplish these aims, we enrolled subjects in a psychophysical experiment under simulated prosthetic vision conditions. We found that the subjects could complete some simple visual tasks, but the recognition performance under the infrared mode was significantly inferior to that under the visible‐light mode. These results inspired us to develop and propose a feasible infrared image‐enhancement processing algorithm. Another psychophysical experiment was performed to verify the feasibility of the algorithm. Results: The obtained results showed that the average efficiency of the subjects completing visual tasks using our enhancement algorithm (0.014 ± 0.001) was significantly higher (p < 0.001) than that of subjects using direct pixelization (0.007 ± 0.001). Conclusions: We concluded that a dual‐mode camera could be a feasible solution to improving the performance of retinal prostheses as the camera adapted better to the specific existing ambient light conditions. Dual‐mode cameras combined with this infrared image‐enhancement algorithm could provide a promising direction for the design of future retinal prostheses. [ABSTRACT FROM AUTHOR]

A visual prosthesis is an auxiliary device for patients with blinding diseases that cannot be treated with conventional surgery or drugs. It converts captured images into corresponding electrical stimulation patterns, according to which phosphenes are generated through the action of internal electrodes on the visual pathway to form visual perception. However, due to some restrictions such as the few implantable electrodes that the biological tissue can accommodate, the induced perception is far from ideal. Therefore, an important issue in visual prosthesis research is how to detect and present useful information in low‐resolution prosthetic vision to improve the visual function of the wearer. In recent years, with the development and broad application of computer vision methods, researchers have investigated the possibility of their utilization in visual prostheses by simulating prosthetic visual percepts. Through the optimization of visual perception by image processing, the efficiency of visual prosthesis devices can be further improved to better meet the needs of prosthesis wearers. In this article, recent works on prosthetic vision centering on implementing computer vision methods are reviewed. Differences, strengths, and weaknesses of the mentioned methods are discussed. The development directions of optimizing prosthetic vision and improving methods of visual perception are analyzed. [ABSTRACT FROM AUTHOR]

Blindness affects millions of people around the world. A promising solution to restoring a form of vision for some individuals are cortical visual prostheses, which bypass part of the impaired visual pathway by converting camera input to electrical stimulation of the visual system. The artificially induced visual percept (a pattern of localized light flashes, or 'phosphenes') has limited resolution, and a great portion of the field's research is devoted to optimizing the efficacy, efficiency, and practical usefulness of the encoding of visual information. A commonly exploited method is non-invasive functional evaluation in sighted subjects or with computational models by using simulated prosthetic vision (SPV) pipelines. An important challenge in this approach is to balance enhanced perceptual realism, biologically plausibility, and real-time performance in the simulation of cortical prosthetic vision. We present a biologically plausible, PyTorch-based phosphene simulator that can run in real-time and uses differentiable operations to allow for gradient-based computational optimization of phosphene encoding models. The simulator integrates a wide range of clinical results with neurophysiological evidence in humans and non-human primates. The pipeline includes a model of the retinotopic organization and cortical magnification of the visual cortex. Moreover, the quantitative effects of stimulation parameters and temporal dynamics on phosphene characteristics are incorporated. Our results demonstrate the simulator's suitability for both computational applications such as end-to-end deep learning-based prosthetic vision optimization as well as behavioral experiments. The modular and open-source software provides a flexible simulation framework for computational, clinical, and behavioral neuroscientists working on visual neuroprosthetics., Competing Interests: Mv, Jd, AL, LP, BR, PR, Mv, Rv, UG, YG No competing interests declared, (© 2024, van der Grinten, de Ruyter van Steveninck, Lozano et al.)

Background and Objective: Current retinal prostheses can only generate low-resolution visual percepts constituted of limited phosphenes which are elicited by an electrode array and with uncontrollable color and restricted grayscale. Under this visual perception, prosthetic recipients can just complete some simple visual tasks, but more complex tasks like face identification/object recognition are extremely difficult. Therefore, it is necessary to investigate and apply image processing strategies for optimizing the visual perception of the recipients. This study focuses on recognition of the object of interest employing simulated prosthetic vision.Method: We used a saliency segmentation method based on a biologically plausible graph-based visual saliency model and a grabCut-based self-adaptive-iterative optimization framework to automatically extract foreground objects. Based on this, two image processing strategies, Addition of Separate Pixelization and Background Pixel Shrink, were further utilized to enhance the extracted foreground objects.Results: i) The results showed by verification of psychophysical experiments that under simulated prosthetic vision, both strategies had marked advantages over Direct Pixelization in terms of recognition accuracy and efficiency. ii) We also found that recognition performance under two strategies was tied to the segmentation results and was affected positively by the paired-interrelated objects in the scene.Conclusion: The use of the saliency segmentation method and image processing strategies can automatically extract and enhance foreground objects, and significantly improve object recognition performance towards recipients implanted a high-density implant. [ABSTRACT FROM AUTHOR]

Objective. We developed a realistic simulation paradigm for cortical prosthetic vision and investigated whether we can improve visual performance using a novel clustering algorithm. Approach. Cortical visual prostheses have been developed to restore sight by stimulating the visual cortex. To investigate the visual experience, previous studies have used uniform phosphene maps, which may not accurately capture generated phosphene map distributions of implant recipients. The current simulation paradigm was based on the Human Connectome Project retinotopy dataset and the placement of implants on the cortices from magnetic resonance imaging scans. Five unique retinotopic maps were derived using this method. To improve performance on these retinotopic maps, we enabled head scanning and a density-based clustering algorithm was then used to relocate centroids of visual stimuli. The impact of these improvements on visual detection performance was tested. Using spatially evenly distributed maps as a control, we recruited ten subjects and evaluated their performance across five sessions on the Berkeley Rudimentary Visual Acuity test and the object recognition task. Main results. Performance on control maps is significantly better than on retinotopic maps in both tasks. Both head scanning and the clustering algorithm showed the potential of improving visual ability across multiple sessions in the object recognition task. Significance. The current paradigm is the first that simulates the experience of cortical prosthetic vision based on brain scans and implant placement, which captures the spatial distribution of phosphenes more realistically. Utilisation of evenly distributed maps may overestimate the performance that visual prosthetics can restore. This simulation paradigm could be used in clinical practice when making plans for where best to implant cortical visual prostheses., (Creative Commons Attribution license.)

Retinal prostheses have the potential to restore partial vision. Object recognition in scenes of daily life is one of the essential tasks for implant wearers. Still limited by the low-resolution visual percepts provided by retinal prostheses, it is important to investigate and apply image processing methods to convey more useful visual information to the wearers. We proposed two image processing strategies based on Itti's visual saliency map, region of interest ( ROI) extraction, and image segmentation. Itti's saliency model generated a saliency map from the original image, in which salient regions were grouped into ROI by the fuzzy c-means clustering. Then Grabcut generated a proto-object from the ROI labeled image which was recombined with background and enhanced in two ways-8-4 separated pixelization (8-4 SP) and background edge extraction ( BEE). Results showed that both 8-4 SP and BEE had significantly higher recognition accuracy in comparison with direct pixelization ( DP). Each saliency-based image processing strategy was subject to the performance of image segmentation. Under good and perfect segmentation conditions, BEE and 8-4 SP obtained noticeably higher recognition accuracy than DP, and under bad segmentation condition, only BEE boosted the performance. The application of saliency-based image processing strategies was verified to be beneficial to object recognition in daily scenes under simulated prosthetic vision. They are hoped to help the development of the image processing module for future retinal prostheses, and thus provide more benefit for the patients. [ABSTRACT FROM AUTHOR]

Retinal prostheses for the restoration of functional vision are under development and visual prostheses targeting proximal stages of the visual pathway are also being explored. To investigate the experience with visual prostheses, psychophysical experiments using simulated prosthetic vision in normally sighted individuals are necessary. In this study, a helmet display with real-time images from a camera attached to the helmet provided the simulated vision, and experiments of recognition and discriminating multiple objects were used to evaluate visual performance under different parameters (gray scale, distortion, and dropout). The process of fitting and training with visual prostheses was simulated and estimated by adaptation to the parameters with time. The results showed that the increase in the number of gray scale and the decrease in phosphene distortion and dropout rate improved recognition performance significantly, and the recognition accuracy was 61.8 ± 7.6% under the optimum condition (gray scale: 8, distortion: k = 0, dropout: 0%). The adaption experiments indicated that the recognition performance was improved with time and the effect of adaptation to distortion was greater than dropout, which implies the difference of adaptation mechanism to the two parameters. [ABSTRACT FROM AUTHOR]

Retinal prostheses have the potential to restore some level of visual function to the patients suffering from retinal degeneration. In this paper, an epiretinal approach with active stimulation devices is presented. The MEMS-based processing system consists of an external micro-camera, an information processor, an implanted electrical stimulator and a microelectrode array. The image processing strategy combining image clustering and enhancement techniques was proposed and evaluated by psychophysical experiments. The results indicated that the image processing strategy improved the visual performance compared with direct merging pixels to low resolution. The image processing methods assist epiretinal prosthesis for vision restoration. [ABSTRACT FROM AUTHOR]

Clinical trials with blind patients implanted with a visual neuroprosthesis showed that even the simplest tasks were difficult to perform with the limited vision restored with current implants. Simulated prosthetic vision (SPV) is a powerful tool to investigate the putative functions of the upcoming generations of visual neuroprostheses. Recent studies based on SPV showed that several generations of implants will be required before usable vision is restored. However, none of these studies relied on advanced image processing. High-level image processing could significantly reduce the amount of information required to perform visual tasks and help restore visuomotor behaviors, even with current low-resolution implants. In this study, we simulated a prosthetic vision device based on object localization in the scene. We evaluated the usability of this device for object recognition, localization, and reaching. We showed that a very low number of electrodes (e.g., nine) are sufficient to restore visually guided reaching movements with fair timing (10 s) and high accuracy. In addition, performance, both in terms of accuracy and speed, was comparable with 9 and 100 electrodes. Extraction of high level information (object recognition and localization) from video images could drastically enhance the usability of current visual neuroprosthesis. We suggest that this method--that is, localization of targets of interest in the scene--may restore various visuomotor behaviors. This method could prove functional on current low-resolution implants. The main limitation resides in the reliability of the vision algorithms, which are improving rapidly. [ABSTRACT FROM AUTHOR]

Due to the limitations of existing techniques, even the most advanced visual prostheses, using several hundred electrodes to transmit signals to the visual pathway, restrict sensory function and visual information. To identify the bottlenecks and guide prosthesis designing, psychophysics simulations of a visual prosthesis in normally sighted individuals are desirable. In this study, psychophysical experiments of discriminating objects with similar profiles were used to test the effects of phosphene array parameters (spatial resolution, gray scale, distortion, and dropout rate) on visual information using simulated prosthetic vision. The results showed that the increase in spatial resolution and number of gray levels and the decrease in phosphene distortion and dropout rate improved recognition performance, and the accuracy is 78.5% under the optimum condition (resolution: 32 × 32, gray level: 8, distortion: k = 0, dropout: 0%). In combined parameter tests, significant facial recognition accuracy was achieved for all the images with k = 0.1 distortion and 10% dropout. Compared with other experiments, we find that different objects do not show specific sensitivity to the changes of parameters and visual information is not nearly enough even under the optimum condition. The results suggests that higher spatial resolution and more gray levels are required for visual prosthetic devices and further research on image processing strategies to improve prosthetic vision is necessary, especially when the wearers have to accomplish more than simple visual tasks. [ABSTRACT FROM AUTHOR]

Visual prostheses offer a possibility of restoring vision to the blind. It is necessary to determine minimum requirements for daily visual tasks. To investigate the recognition of common objects in daily life based on the simulated irregular phosphene maps, the effect of four parameters (resolution, distortion, dropout percentage, and gray scale) on object recognition was investigated. The results showed that object recognition accuracy significantly increased with an increase of resolution. Distortion and dropout percentage had significant impact on the object recognition; with the increase of distortion level and dropout percentage, the recognition decreased considerably. The accuracy decreased significantly only at gray level 2, whereas the other three gray levels showed no obvious difference. The two image processing methods (merging pixels to lower the resolution and edge extraction before lowering resolution) showed significant difference on the object recognition when there was a high degree of distortion level or dot dropout. [ABSTRACT FROM AUTHOR]

The influence of complexity and minimum resolution necessary for recognition of pixelized Chinese characters (CCs) was investigated by using simulated prosthetic vision. An image-processing method was used to evaluate the complexity of CCs, which is defined as the frequency of black pixels and analyzed by black pixel statistic complexity algorithm. A total of 631 most commonly used CCs that can deliver 80% of the information in Chinese daily reading were chosen as the testing database in order to avoid the negative effect due to illegibility and incognizance. CCs in Hei font style were captured as images and pixelized as 6 × 6, 8 × 8, 10 × 10, and 12 × 12 pixel arrays with square dots. Recognition accuracy of CCs with different complexity and different numbers of pixel arrays was tested by using simulated prosthetic vision. The results indicate that both pixel array number and complexity have significant impact on pixelized reading of CCs. Recognition accuracy of pixelized CCs drops with the increase of complexity and the decrease of pixel number. More than 80% of CCs with any complexity can be recognized correctly; 10 × 10 pixel array can sufficiently provide pixelized reading of CCs for visual prosthesis. Pixelized reading of CCs with low resolution is possible only for characters with low complexity (complexity less than 0.16 for a 6 × 6 pixel array and less than 0.24 for an 8 × 8 pixel array). [ABSTRACT FROM AUTHOR]

Abstract: Investigators of microelectronic visual prosthesis devices have found that some aspects of vision can be restored in the form of spots of light in the visual field, so-called “phosphenes”, from which more rich and complex scenes may be composed. However, questions still surround the capabilities of how such a form of vision can allow its recipients to “see” and to carry out everyday activities. Through simulations of prosthetic vision, researchers can experience first-hand many performance and behavioral aspects of prosthetic vision, and studies conducted on a larger population can inform the performance and behavioral preferences in general and in individual cases. This review examines the findings from the various investigations of the functional capacity of prosthetic vision conducted through simulations, especially on the topics of letter acuity, reading, navigation, learning and visual scanning adaptation. Central to the review, letter acuity is posited as a reference measurement so that results and performance trends across the various simulation models and functional assessment tasks can be more readily compared and generalized. Future directions for simulation based research are discussed with respect to designing a functional visual prosthesis, improving functional vision in near-term low-phosphene-count devices, and pursuing image processing strategies to impart the most comprehensible prosthetic vision. [Copyright &y& Elsevier]

Abstract: With increasing research advances and clinical trials of visual prostheses, there is significant demand to better understand the perceptual and psychophysical aspects of prosthetic vision. In prosthetic vision a visual scene is composed of relatively large, isolated, spots of light so-called “phosphenes”, very much like a magnified pictorial print. The utility of prosthetic vision has been studied by investigators in the form of virtual–reality visual models (simulations) of prosthetic vision administered to normally sighted subjects. In this review, the simulations from these investigations are examined with respect to how they visually render the phosphenes and the virtual–reality apparatus involved. A comparison is made between these simulations and the actual descriptions of phosphenes reported from human trials of visual prosthesis devices. For the results from these simulation studies to be relevant to the experience of visual prosthesis recipients, it is important that, the simulated phosphenes must be consistent with the descriptions from human trials. A standardized simulation and reporting framework is proposed so that future simulations may be configured to be more realistic to the experience of implant recipients, and the simulation parameters from different investigators may be more readily extracted, and study results more fittingly compared. [Copyright &y& Elsevier]

The rehabilitation of the reading ability of the blind with a limited number of stimulating electrodes is regarded as one of the major functions of the envisioned visual prosthesis. This article systematically studied how many pixels of individual Chinese characters should be needed for correct and economic recognition by blind Chinese subjects. In this study, 40 normal-sighted subjects were tested on a self-developed platform HanziConvertor (Institute for Laser Medicine & Bio-photonics, Shanghai Jiaotong University, China) with digital imaging processing capacities to convert images of printed text into various pixelized patterns made up of discrete dots, and present them orderly on a computer screen. It was found that various complicated factors such as pixel number, character typeface, stroke number, etc., can obviously affect the recognition accuracy. It was also found that optimal recognition accuracy occurs at a specific size of binary pixel array, due to a trade-off between a strictly limited number of stimulation electrodes and character sampling resolution. The results showed that (i) recognition accuracy of pixelized characters is optimal with at least 12 × 12 binary pixels, and therefore it is recommended to apply a minimum of 150 discrete and functioning electrodes for restoring the reading ability of blind Chinese individuals in the visual prosthesis; (ii) fonts of Song Ti and Hei Ti are clearer and more effective than other typefaces; and (iii) characters with fewer strokes lead to better accuracy. [ABSTRACT FROM AUTHOR]

Loss of photoreceptor cells is one of the major causes of blindness. Several groups are exploring the functional replacement of photoreceptors by a retinal prosthesis. The goal of this study was to simulate the vision levels that recipients of retinal prostheses with 4 × 4, 6 × 10, and 16 × 16 electrode arrays may experience, and to test the functionality of this vision. A PC video camera captured images that were converted in real time into dots (“pixels”). The PC monitor and a head-mounted display worn by test subjects displayed the pixelized images. To assess performance of normally sighted individuals with each array, we designed a set of tasks including: four-choice orientation discrimination of a Sloan letter E, object recognition and discrimination, a cutting task, a pouring task, symbol recognition, and two reading tasks. In the letter E task, subjects were found to have visual acuities of 20/1,810, 20/1,330, and 20/420 with the 4 × 4, 6 × 10, and 16 × 16 arrays, respectively. Most subjects were able to read fonts as small as 36 point with the 16 × 16 array, corresponding with a visual acuity of 20/600 in our system. The test subjects partially overcame the visual limitation of the system by scanning the video camera over the letters allowing spatial and temporal integration of visual information. In all categories, subjects performed best with the 16 × 16 array and least well with the 4 × 4 array. Even with the lowest resolution array, however, some subjects could recognize simple objects and symbols. [ABSTRACT FROM AUTHOR]

Objective . Temporal resolution is a key challenge in artificial vision. Several prosthetic approaches are limited by the perceptual fading of evoked phosphenes upon repeated stimulation from the same electrode. Therefore, implanted patients are forced to perform active scanning, via head movements, to refresh the visual field viewed by the camera. However, active scanning is a draining task, and it is crucial to find compensatory strategies to reduce it. Approach . To address this question, we implemented perceptual fading in simulated prosthetic vision using virtual reality. Then, we quantified the effect of fading on two indicators: the time to complete a reading task and the head rotation during the task. We also tested if stimulation strategies previously proposed to increase the persistence of responses in retinal ganglion cells to electrical stimulation could improve these indicators. Main results . This study shows that stimulation strategies based on interrupted pulse trains and randomisation of the pulse duration allows significant reduction of both the time to complete the task and the head rotation during the task. Significance . The stimulation strategy used in retinal implants is crucial to counteract perceptual fading and to reduce active head scanning during prosthetic vision. In turn, less active scanning might improve the patient's comfort in artificial vision., (Creative Commons Attribution license.)

Bionic vision uses neuroprostheses to restore useful vision to people living with incurable blindness. However, a major outstanding challenge is predicting what people "see" when they use their devices. The limited field of view of current devices necessitates head movements to scan the scene, which is difficult to simulate on a computer screen. In addition, many computational models of bionic vision lack biological realism. To address these challenges, we present VR-SPV, an open-source virtual reality toolbox for simulated prosthetic vision that uses a psychophysically validated computational model to allow sighted participants to "see through the eyes" of a bionic eye user. To demonstrate its utility, we systematically evaluated how clinically reported visual distortions affect performance in a letter recognition and an immersive obstacle avoidance task. Our results highlight the importance of using an appropriate phosphene model when predicting visual outcomes for bionic vision.