arXiv subjects

Adyah Rastogi

Publications and source records attributed to Adyah Rastogi.

3 recordsLinked to original sources

arXiv2026

Cross-Modal Guidance for Out-of-View Object Search in Simulated Prosthetic Vision

Out-of-view guidance is well established in virtual and augmented reality, but its effectiveness may depend on the visual bandwidth available to the user. We test this under simulated prosthetic vision (SPV), where visual guidance must share the same sparse representation used to inspect the scene. Nineteen participants performed object search under two SPV conditions differing in electrode density and phosphene spread (10x10 and 20x20) and four guidance conditions (no guidance, visual, haptic, audio) all driven by the same horizontal target-offset variable. All three modalities reduced search time and head movement. The tested auditory and haptic cues produced approximately 25% faster overall search and 11-13% faster target acquisition than the visual cue, despite similarly direct orienting trajectories. The tested haptic and auditory cues also shortened post-acquisition search. Final head-target angular offset was reduced substantially more in the 10x10 SPV condition; there, all three cues also reduced vertical localization error by approximately 45-58% despite providing no elevation information. Under severe visual constraints, guidance performance depended on cue implementation and search stage.

cs.HC↗

arXiv2026

Evaluating Closed-Loop EEG Feedback for Simulated Prosthetic Vision in Immersive VR: A Sham-Controlled Feasibility Study

Visual prostheses require users to interpret sparse and distorted artificial percepts through active visual search. We developed an EEG-guided neuroadaptive training platform for simulated prosthetic vision in immersive virtual reality and evaluated its feasibility in a sham-controlled object-localization task. Twenty-two sighted participants searched a virtual desk scene rendered through a low-resolution phosphene simulation while EEG was recorded using a dry-electrode headset integrated with a head-mounted display. During training, participants received post-trial visual feedback based either on a commonly used EEG engagement index, $\beta/(\alpha+\theta)$, or on visually matched non-contingent sham values. Both groups showed comparable within-session improvements in localization performance, consistent with practice, increasing familiarity with the simulated percepts, or refinement of search strategies. EEG-contingent feedback did not produce reliable group-level benefits in localization accuracy, completion time, workload, or modulation of the targeted index. Exploratory analyses showed substantial individual variability, but did not establish a feedback-specific relationship between the engagement index and behavioral performance. These findings demonstrate the feasibility of integrating EEG-contingent feedback with immersive simulated prosthetic vision, while identifying important limitations of the EEG measure, single-session training protocol, and post-trial feedback design.

cs.HC↗

arXiv2025

Evaluating Deep Human-in-the-Loop Optimization for Retinal Implants Using Sighted Participants

Human-in-the-loop optimization (HILO) is a promising approach for personalizing visual prostheses by iteratively refining stimulus parameters based on user feedback. Previous work demonstrated HILO's efficacy in simulation, but its performance with human participants remains untested. Here we evaluate HILO using sighted participants viewing simulated prosthetic vision to assess its ability to optimize stimulation strategies under realistic conditions. Participants selected between phosphenes generated by competing encoders to iteratively refine a deep stimulus encoder (DSE). We tested HILO in three conditions: standard optimization, threshold misspecifications, and out-of-distribution parameter sampling. Participants consistently preferred HILO-generated stimuli over both a naive encoder and the DSE alone, with log odds favoring HILO across all conditions. We also observed key differences between human and simulated decision-making, highlighting the importance of validating optimization strategies with human participants. These findings support HILO as a viable approach for adapting visual prostheses to individuals. Clinical relevance: Validating HILO with sighted participants viewing simulated prosthetic vision is an important step toward personalized calibration of future visual prostheses.

cs.LG↗