Prediction of visual perceptions with artificial neural networks in a visual prosthesis for the blind.
Within the framework of the OPTIVIP project, an optic nerve based visual prosthesis is developed in order to restore partial vision to the blind. One of the main challenges is to understand, decode and model the physiological process linking the stimulating parameters to the visual sensations produced in the visual field of a blind volunteer. We propose to use adaptive neural techniques. Two prediction models are investigated. The first one is a grey-box model exploiting the neurophysiological knowledge available up to now. It combines a neurophysiological model with artificial neural networks, such as multi-layer perceptrons and radial basis function networks, in order to predict the features of the visual perceptions. The second model is entirely of the black-box type. We show that both models provide satisfactory prediction tools and achieve similar prediction accuracies. Moreover, we demonstrate that significant improvement (25%) was gained with respect to linear statistical methods, suggesting that the biological process is strongly non-linear.
Archambeau
Cédric
C
Microelectronics Laboratory, Université catholique de Louvain, Place du Levant 3, B-1348 Louvain-la-Neuve, Belgium. archambeau@dice.ucl.ac.be
Delbeke
Jean
J
Veraart
Claude
C
Verleysen
Michel
M
D001766:Blindness / Q000534:rehabilitation*; D005544:Forecasting; D006801:Humans; D008960:Models, Psychological; D016571:Neural Networks, Computer; D009900:Optic Nerve / Q000473:pathology / Q000502:physiology; D019736:Prostheses and Implants; D014796:Visual Perception