C-Sight Visual Prosthetic Implant

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Overall the C-Sight visual prosthesis is based on optic nerve stimulation with a penetrating electrode array. The goal of the project is to produce a feasible and realiable, low-cost solution to those with visual impairment. The feasibility of this concept was demonstrated with the results of the animal experiments. The tests of image-processing strategies by computer simulation and DSP implementation in real time have been presented. A high-precision neural stimulator was studied. Research in simulated phosphene positioning and recognition of pixelized images was also performed, which may provide useful information for the design and development of visual prostheses.

In our approach, the knowledge of the topographical projection from the optic nerve to primary visual cortex is unknown prior to the surgery; therefore, an effective phosphene positioning system is very critical for the evaluation of the precise location of the induced phosphene and requires some further investigation.

An intensive preclinical evaluation for the C-Sight visual prosthesis is under way to demonstrate its clinical efficacy. Our team continues to develop the various subsystems. These include improved electrode arrays, DSP algorithms, and reduction is size and power consumption. We are also focusing on subcutaneous wireless power and data transmission. Numerous papers and patents have been published by our lab group, as we continue working towards our ultimate goal.


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  • Name:
    Shawn Casey
  • Type of entry:
    Team members:
    Shawn Casey, Shanghai Jiao Tong University
    Ren Qiushi, Peking University
    Wu Kaijie, Shanghai Jiao Tong University
    Lei Xuping, Shanghai Jiao Tong University
  • Profession:
  • Number of times previously entering contest:
  • Shawn's favorite design and analysis tools:
    LabView, Matlab, CST, Multisim
  • Shawn's hobbies and activities:
    Chess, Foreign Language
  • Shawn belongs to these online communities:
    MSN, QQ
  • Shawn is inspired by:
    A comitment to lifelong learning.
  • Software used for this entry:
    Matlab, Labview, Multisim, CST
  • Patent status: