Joseph Makin at the University of California, San Francisco, and his colleagues used deep learning algorithms to study the brain signals of four women as they spoke. The women, who all have epilepsy, already had electrodes attached to their brains to monitor seizures.
Each woman was asked to read aloud from a set of sentences as the team measured brain activity. The largest group of sentences contained 250 unique words.
The team fed this brain activity to a neural network algorithm, training it to identify regularly occurring patterns that could be linked to repeated aspects of speech, such as vowels or consonants. These patterns were then fed to a second neural network, which tried to turn them into words to form a sentence.
Each woman repeated the sentences at least twice, and the final repetition didn't form part of the training data, allowing the researchers to test the system.
Each time a person speaks the same sentence, the brain activity associated will be similar but not identical. "Memorising the brain activity of these sentences wouldn't help, so the network instead has to learn what's similar about them so that it can generalise to this final example," says Makin. Across the four women, the AI's best performance was an average translation error rate of 3 per cent.