A Computational Ghost Imaging Architecture Connecting the Human Brain and Computers

Recently, Wang Gao, a doctoral student in physics at the University of Glasgow, UK, proposed an architecture to connect the human brain and the computer to realize computational ghost imaging (CGI). His related paper "Computational Ghost Imaging with the Human Brain" has been published in Intelligent Computing.

The so-called computational ghost imaging (CGI) refers to the use of correlation calculations to obtain object information. This research achievement can image objects beyond the human eye's line of sight, thereby enhancing human visual perception and providing new ideas for the development of augmented reality, human-computer interaction, and other fields.

Brain–computer interface (BCI) is a technology that connects the human brain with an external computer or machine, controls the behavior of the computer or machine, and reads or monitors the activity of the brain by identifying and analyzing the electrical signals sent by the brain. Its research results provide a Technology roadmap for exploring brain neuroscience, and provide new clues for understanding the Neurophysics mechanism of human visual system, which can be used to study the differences between conscious and unconscious EEG states.

In the process of developing a computing ghost imaging architecture, Wang Gao used the human visual system as a detector; The commonly used visual evoked signals in Brain–computer interface were investigated, and Steady state visually evoked potential (SSVEP) was selected as a part of imaging detection. On this basis, he conducted extensive experiments to study the intensity relationship between optical stimulus input and SSVEP output, and drew corresponding thermal maps, providing a foundation for subsequent successful imaging. At the same time, Wang Gao proposed an adaptive computing loop that provides real-time feedback control of SSVEP signals on optical speckle, thereby accelerating imaging speed, reducing detection times, and shortening the time required for imaging. As a result, he constructed a deep neural network for computing ghost imaging, which achieved imaging mapping from intensity detection sequences to object images for SSVEP, reducing the impact of noise and improving the quality of imaging.

The research results of Wang Gao's ghost imaging architecture have great potential in specific applications:

First, in terms of human enhancement, the use of Brain–computer interface to achieve computational imaging can expand human perception, for example, to achieve the imaging of scenes outside the human line of sight.

Secondly, in terms of human-computer interaction, imaging the area being watched through the human eye is expected to enhance the interaction ability between disabled people and the outside world.

Third, in terms of Neurophysics, this achievement provides a Technology roadmap to explore brain neuroscience, such as the representation of EEG differences in conscious and unconscious states.

Fourthly, in medicine, this technology can provide a potential framework and method for examining the function of visual pathways in the human brain.

Fifthly, in terms of brain computer integration, this technology can potentially utilize the powerful data processing capabilities of the human brain to process visual stimuli in external environments, and complete complex tasks by reading EEG signals recorded by BCI. For example, using the human brain to recognize or classify images.