A chip for a brain-machine interface is being built in Australia

UNSW Engineering fibre optic chip. Image: UNSW
  • Engineers at the University of NSW are working on a brain-machine interface.
  • They have developed pixel-like sensors on a chip that pick up electrical signals when placed on brain tissue.
  • The researchers have has been awarded a $US359,000 ($A490,215) US Naval Research grant.

Australian researchers have just won funding from the US to help develop chips to read neural activity from the brain.

The University of NSW Sydney Engineering researchers have been awarded a $US359,000 ($A490,215) US Naval Research grant for work which could lead to the creation of a brain-machine interface.

Dr Leonardo Silvestri and Professors Francois Ladouceur and Nigel Lovell from UNSW Engineering are working on a direct communication pathway between a wired brain and an external device.

The ultimate goal is to enable two-way communication between the brain and a machine, such as an artificial limb or a computer.

The team invented the first chip to use fibre optics to measure signals from the brain and convert them to an image that can be read.

“We’ve developed optrodes — pixel-like sensors on a chip — that pick up electrical signals when placed on brain tissue,” says Dr Silvestri.

“At the back of the chip are optical fibres that measure this brain activity and translate it into a visual image.

“This is the information we can interpret and use to control a connected device, which could have potential applications in numerous fields.”

Fibre optic technology uses light pulses to send data via optical fibres, which have a much higher bandwidth than electrical wiring.

“What we are building is a chip with a million electrodes, with a small bundle of thin, lightweight glass fibs going from the brain to a small device in your pocket, from where it can transmit wirelessly,” says Dr Silvestri.

“Our solution is far easier to scale than those using traditional wiring and it’s also less susceptible to interference — so when instructing a machine we can be much more precise. This will be especially valuable for medical applications.”

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