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

2026-07-26

AI Summary

Visual prosthesis users must rely on active visual search to interpret sparse, distorted percepts, yet effective training protocols remain unclear. This study proposes the first neuroadaptive training platform integrating immersive virtual reality with dry-electrode EEG-based closed-loop feedback to simulate phosphene-mediated object localization. A sham-stimulation control condition was incorporated to isolate the specific effects of neural feedback. The system combines head-mounted EEG, low-resolution visual simulation, and a beta/(alpha+theta) engagement index, supplemented by post-trial visual feedback. Both experimental groups demonstrated improved task performance over time; however, EEG-based feedback did not confer a significant group-level advantage. Notably, substantial inter-individual variability emerged, and the EEG engagement metric showed no feedback-specific correlation with behavioral improvement.

Abstract

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, $β/(α+θ)$, 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.

Problem

• prosthetic vision
• EEG feedback
• immersive VR
• closed-loop
• visual prosthesis

Innovation

• EEG-guided neuroadaptive training
• simulated prosthetic vision
• immersive virtual reality
• closed-loop EEG feedback
• dry-electrode headset