Mind-reading cap turns thoughts into text in world first

‘Pioneering’ research could transform how humans interact with machines

Anthony Cuthbertson
Wednesday 13 December 2023 10:23 EST
Comments
A participant of the mind-reading study wears a cap that records electrical brain activity while they silently read text
A participant of the mind-reading study wears a cap that records electrical brain activity while they silently read text (UTS)

Your support helps us to tell the story

From reproductive rights to climate change to Big Tech, The Independent is on the ground when the story is developing. Whether it's investigating the financials of Elon Musk's pro-Trump PAC or producing our latest documentary, 'The A Word', which shines a light on the American women fighting for reproductive rights, we know how important it is to parse out the facts from the messaging.

At such a critical moment in US history, we need reporters on the ground. Your donation allows us to keep sending journalists to speak to both sides of the story.

The Independent is trusted by Americans across the entire political spectrum. And unlike many other quality news outlets, we choose not to lock Americans out of our reporting and analysis with paywalls. We believe quality journalism should be available to everyone, paid for by those who can afford it.

Your support makes all the difference.

Researchers have invented a mind-reading cap capable of non-invasively decoding thoughts into text for the first time.

The technology, developed by a team at the University of Technology Sydney in Australia, could help people unable to speak due to illness or injury, while also providing a way for humans to interact directly with machines.

In tests of the cap, participants were told to silently read passages of text while an electroencephalogram (EEG) recorded their electrical brain activity.

An artificial intelligence model called DeWave was then used to decipher the thoughts into written text with an accuracy of between 40 and 60 per cent.

“This research represents a pioneering effort in translating raw EEG waves directly into language, marking a significant breakthrough in the field,” said CT Lin, a professor at the University of Technology Sydney.

“It is the first to incorporate discrete encoding techniques in the brain-to-text translation process, introducing an innovative approach to neural decoding.”

Professor Lin added that the integration with large language models would “open new frontiers in neuroscience and AI”.

Other brain-computer interfaces that can translate thoughts into text currently involve either MRI scans or invasive procedures through the nose or skull in order to implant the electrodes.

Elon Musk’s neurotech startup Neuralink uses a surgical robot to implant a chip into the brain that he claims will one day provide wearers with “enhanced abilities” like greater reasoning and improved vision.

Recruitment for the first human trials of the technology began earlier this year after receiving approval from the US Food and Drug Administration (FDA).

The company has faced criticism from animal rights groups for testing of the brain chip on monkeys, with the Physicians Committee for Responsible Medicine (PCRM) accusing Neuralink of subjecting primates to “extreme suffering”. Both Neuralink and Mr Musk have denied the allegations.

Other brain-computer interface systems that use invasive approaches have achieved higher accuracy rates than the latest system, however the team behind the non-invasive cap believe it has potential to reach a level closer to 90 per cent.

“Despite the challenges, our model yields meaningful results, aligning keywords and forming similar sentence structures,” said Yiqun Duan, one of the scientists behind the study.

The research was presented at the NeurIPS conference in New Orleans on 12 December.

Join our commenting forum

Join thought-provoking conversations, follow other Independent readers and see their replies

Comments

Thank you for registering

Please refresh the page or navigate to another page on the site to be automatically logged inPlease refresh your browser to be logged in