Minimizing the number of phosphenes required for object recognition under prosthetic vision
Scialom, E.; Ernst, U. A.; Rotermund, D.; Herzog, M. H.
Show abstract
Cortical prostheses offer the potential for partial vision restoration in individuals with blindness by stimulating V1 neurons to produce phosphenes. However, the low number of phosphenes that can be elicited in practice makes encoding of whole objects difficult, and the round shape of phosphenes lack the contour cues necessary for perceptual grouping. We propose a minimalistic encoding approach that focuses on essential visual information. We fragmented objects contours into either phosphenes or curved segments, providing either low or high local visual information. 46 participants identified these fragmented objects in a free-naming task. The number of fragments gradually increased to quantify the minimum number of phosphenes and segments necessary to recognize objects. Most objects could be recognized with only 65 phosphenes, which is in the range of implantable electrodes in human patients. Participants required 27% fewer segments than phosphenes to recognize objects. Including individual objects as a random effect in a linear mixed model substantially increased the explained variance, suggesting that the minimal number of fragments required for object recognition in prosthetic vision strongly depends on the particular object. Our results demonstrate that a minimalistic approach can substantially reduce the number of phosphenes required for recognition, emphasizing the importance of identifying critical object features to minimize brain stimulation in visual prostheses.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Generating accurate 3D gaze vectors using synchronized eye tracking and motion capture 93%
- Towards a standardization of non-symbolic numerical experiments: GeNEsIS, a flexible and user-friendly tool to generate controlled stimuli 93%
- Methods in Cognitive Pupillometry: Design, Preprocessing, and Statistical Analysis 93%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.