A cryptography-based approach has been used to decode the activity of motor neurons. A team of researchers from the University of Pennsylvania, the Georgia Institute of Technology, and Northwestern University report that the use of cryptographic techniques has allowed them to predict, from brain data, and with only generic knowledge of typical movements, which direction monkeys will move their arms.
They say that the same cryptography-inspired technique could eventually be used to decode more complex patterns of muscle activation, for use in prosthetic devices, or even speech, to aid those with total paralysis.
The research team was led by Konrad Kording, a Penn Integrates Knowledge Professor with appointments in the department of neuroscience in Penn's Perelman School of Medicine and in the department of bioengineering. The results were published in Nature Biomedical Engineering earlier this week.
In an experiment with three rhesus macaques, the researchers took data from several hundred neurons associated with arm movement. As the monkeys completed tasks where they had to reach to a target that appeared at different points around a central starting point, in-dwelling electrodes recorded spikes of electrical activity that corresponded with the movement of the monkey's arm.
Starting with each neuron's firing pattern, the team's goal was to discover a consistent way of mathematically mapping those patterns to the monkeys' actual arm movements. Their approach was made possible by finding statistical structures of movements.
"Essentially, the algorithm tries a range of possible decoders until we get something where the output looks like typical movements," Kording said. "There are issues scaling this up—it's a hard computer science problem—but this is a proof-of-concept that cryptanalysis can work in the context of neural activity."
A cryptanalysis approach to decoding neural activity is particularly attractive when it comes to brain-computer interfaces that control prosthetics. Having robotic limbs that can directly interpret their user's thoughts without having to be calibrated would represent a significant quality-of-life improvement.
This cryptanalysis approach could also provide a way for brain-computer interfaces to achieve literal mindreading.
"You could ask a 'locked-in' patient to generate neural patterns associated with specific words, but the corpus of language is very large," Kording said. "Rather than having them generate a pattern that is associated with every word they want to say, we could build a decoder that transforms those patterns until it looks like language."
"I think we should be able to do this within the next decade," he added.