Computational imaging with the human brain

TL;DR

This paper introduces a novel computational imaging approach using brain-computer interfaces to perform ghost imaging by leveraging real-time neural feedback, enhancing resolution and efficiency in visual scene reconstruction.

Contribution

It demonstrates the first use of human brain signals to adaptively control a computational imaging process, blending neuroscience with advanced imaging techniques.

Findings

Successful ghost imaging of hidden scenes using brain feedback

Real-time adaptive pattern projection improves imaging resolution

Brain signals influence image reconstruction quality

Abstract

Brain-computer interfaces (BCIs) are enabling a range of new possibilities and routes for augmenting human capability. Here, we propose BCIs as a route towards forms of computation, i.e. computational imaging, that blend the brain with external silicon processing. We demonstrate ghost imaging of a hidden scene using the human visual system that is combined with an adaptive computational imaging scheme. This is achieved through a projection pattern `carving' technique that relies on real-time feedback from the brain to modify patterns at the light projector, thus enabling more efficient and higher resolution imaging. This brain-computer connectivity demonstrates a form of augmented human computation that could in the future extend the sensing range of human vision and provide new approaches to the study of the neurophysics of human perception. As an example, we illustrate a simple experiment whereby image reconstruction quality is affected by simultaneous conscious processing and readout of the perceived light intensities.