AI System Recreates Image You're Thinking About By Decoding Brain Signals

To the AI algorithm, it's all just data. JL

Tim Collins reports in The Mail:

Experts used a neural network to create images based on information taken from fMRI scans, which detect changes in blood flow to analyse electrical activity. The machine was able to reconstruct owls, aircraft, stained-glass windows and red postboxes after three volunteers stared at the pictures. 'While our model was trained with natural images, our method successfully generalized the reconstruction to artificial shapes, indicating that our model 'reconstructs' images from brain activity, not simply matches to exemplars.'

Japanese scientists have create a creepy machine that can peer into your mind's eye with incredible accuracy.

The AI studies electrical signals in the brain to work out exactly what images someone is looking at, and even thinking about. 

The technology opens the door to strange future scenarios, such as those portrayed in the series 'Black Mirror', where anyone can record and playback their memories.

The findings were made by researchers from the Kamitani Lab at Kyoto University, led by Professor Yukiyasu Kamitani.

Experts used a neural network to create images based on information taken from fMRI scans, which detect changes in blood flow to analyse electrical activity. 

Using this data, the machine was able to reconstruct owls, aircraft, stained-glass windows and red postboxes after three volunteers stared at the pictures. 

It also produced pictures of objects including squares, crosses, goldfish, swans, leopards and bowling balls that the participants imagined.

Although the accuracy varied from person to person, the breakthrough opens a 'unique window into our internal world', according to the Kyoto team.

The technique could theoretically be used to create footage of daydreams, memories and other mental images. 

It could also help patients in permanent vegetative states to communicate with their loved ones. 

Writing in a paper published in the online print repository BioRxiv, its authors said: 'Here, we present a novel image reconstruction method, in which the pixel values of an image are optimized to make its Deep Neural Network features similar to those decoded from human brain activity at multiple layers. 

'We found that the generated images resembled the stimulus images (both natural images and artificial shapes) and the subjective visual content during imagery. 

'While our model was solely trained with natural images, our method successfully generalized the reconstruction to artificial shapes, indicating that our model indeed 'reconstructs' or 'generates' images from brain activity, not simply matches to exemplars.'

The breakthrough relies on neural networks, which try to simulate the way the brain works in order to learn.