You wake up. You were dreaming, but in the haze of morning, you cannot quite remember what ran through your head. Childhood acquaintances were there. You were in Australia. One guy was a pirate. There was something about a cow. Perhaps. We have all had similarly murky memories of an earlier night’s dream. But what if you could actually record your dreaming brain? Could you reconstruct the stories that play out in your head?
It appears to be plausible. Science fiction is full of machines that can peer inside our heads and decipher our thoughts, and science, it seems, is catching up. The news abounds with tales of scientists who have created “mind-reading” machines that can convert our thoughts into images, most of these stories including a throwaway line about one day recording our dreams. But visualising our everyday thoughts is no easy matter, and dream-reading is more difficult still.
The task of decoding dreams comes down to interpreting the activity of the brain’s 100 billion or so neurons, or nerve cells. And to interpret, you first have to measure. Contrary to the hype, our tools for measuring human brain activity leave a lot to be desired. “Our methods are really lousy,” says Professor Jack Gallant, a neuroscientist at the University of California, Berkeley.
Some techniques, like electroencephalography (EEG) and magnetoencephalography (MEG), measure the electric and magnetic fields that we produce when our neurons fire. Their resolution is terrible. They can only home in on 5-10 millimetres of brain tissue at a time at best – a space that contains only a few hundred million neurons. And because of the folded nature of the brain, those neurons can be located in nearby areas that have radically different functions.
More recently, some scientists have used small grids of electrodes to isolate the activity of a handful of neurons. You get much better spatial resolution, but with two disadvantages: you can only look at a tiny portion of the brain, and you need to open up a hole in the volunteer’s skull first. It is not exactly a technique that is ready for the mass market.
Other methods are indirect. The most common one, functional magnetic resonance imaging (fMRI), is the darling of modern neuroscience. Neurons need sugar and oxygen to fuel their activity, and local blood vessels must increase their supply to meet the demand. It’s this blood flow that fMRI measures, and the information is used to create an activation map of the brain. However, this provides only an indirect echo of neural activity, according to Gallant. “Imagine you tried to work out what was going on in an office, but rather than asking people what they did, you went into the kitchen to see how much water they used,” he says.
Despite these weaknesses, Gallant has repeatedly used fMRI to decipher the images encoded in our brain activity. For his latest trick, three of his team watched hours of YouTube clips while Gallant scanned the visual centres of their brains. He plugged the data into a mathematical model that acted as a brain-movie “dictionary”, capable of translating neural activity into moving images. The dictionary could later reconstruct what the volunteers saw, by scanning hours of random clips and finding those that matched any particular burst of brain activity.
The reconstructed images were blurry and grainy, but Gallant thinks that this will improve with time, as we develop better ways of measuring brain activity, better models for analysing it and faster computers to handle the intense processing. “Science marches on,” he says. “You know that in the future, it will be possible to measure brain activity better than you can today.”