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.