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    This study introduces a new algorithm to improve object recognition for individuals using visual prostheses. The algorithm compensates for malfunctioning electrodes, enhancing vision restoration and object identification accuracy.

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    Area of Science:

    • Biomedical Engineering
    • Neuroscience
    • Ophthalmology

    Background:

    • Visual prostheses aim to restore partial vision through electrical stimulation.
    • Electrode dropout, or malfunctioning electrodes, is a significant challenge causing dark phosphenes and hindering object identification.

    Purpose of the Study:

    • To develop and evaluate a novel dropout handling algorithm for visual prostheses.
    • To improve object recognition accuracy, speed, and confidence in simulated prosthetic vision.

    Main Methods:

    • A dropout handling algorithm was proposed, relocating phosphenes to minimize dropout impact.
    • Simulated prosthetic vision experiments were conducted with varying electrode dropout rates (10%, 20%, 30%).

    Main Results:

    • The proposed algorithm significantly increased object recognition accuracy.
    • Recognition time was reduced, and recognition confidence levels were elevated compared to no dropout handling.
    • The algorithm demonstrated effectiveness across different dropout rates.

    Conclusions:

    • Dropout handling is crucial for enhancing image perception in prosthetic vision.
    • The developed algorithm offers a practical solution to improve the efficacy of visual prostheses.
    • This approach has clinical relevance for advancing vision restoration technologies.