Using their non-invasive sensors, Professor Francesca Iacopi from the University of Technology Sydney and her colleagues have demonstrated hands-free communication with a quadruped robot through brain activity.
Faisal et al. demonstrated three-dimensional micropatterned sensors based on a subnanometer-thick epitaxial graphene for detecting the electroencephalography signal from the challenging occipital region of the head. Image credit: Faisal et al., doi: 10.1021/acsanm.2c05546.
Brain-machine interfaces are hands-free and voice-command-free communication systems that allow an individual to operate external devices through brain waves, with vast potential for future robotics, bionic prosthetics, neurogaming, electronics, and autonomous vehicles.
Such systems typically consist of three modules: an external sensory stimulus, a sensing interface, and a neural signal processing unit.
Among these, the sensing interface plays a crucial part by detecting the cortical electrical activity, which encodes human intent (brain waves at a frequency of 1-150 Hz), through either implanted or wearable neural sensors, such as electroencephalography electrodes.
Non-invasive sensors are often preferred when no severe disabilities are involved.
“The hands-free, voice-free technology works outside laboratory settings, anytime, anywhere,” Professor Iacopi said.
“It makes interfaces such as consoles, keyboards, touchscreens and hand-gesture recognition redundant.”
“By using cutting edge graphene material, combined with silicon, we were able to overcome issues of corrosion, durability and skin contact resistance, to develop the wearable dry sensors.”
The graphene sensors developed by Professor Iacopi and co-authors are very conductive, easy to use and robust.
The hexagon patterned sensors are positioned over the back of the scalp, to detect brain waves from the visual cortex.
The sensors are resilient to harsh conditions so they can be used in extreme operating environments.
The user wears a head-mounted augmented reality lens which displays white flickering squares.
By concentrating on a particular square, the brain waves of the operator are picked up by the biosensor, and a decoder translates the signal into commands.
The technology was recently demonstrated by the Australian Army, where soldiers operated a Ghost Robotics quadruped robot using the brain-machine interface.
The device allowed hands-free command of the robotic dog with up to 94% accuracy.
“Our technology can issue at least nine commands in two seconds,” said Professor Chin-Teng Lin, also from the University of Technology Sydney.
“This means we have nine different kinds of commands and the operator can select one from those nine within that time period.”
“We have also explored how to minimise noise from the body and environment to get a clearer signal from an operator’s brain.”
“We believe the technology will be of interest to the scientific community, industry and government, and hope to continue making advances in brain-computer interface systems.”
The team’s work was published in the journal ACS Applied Nano Materials.
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Shaikh Nayeem Faisal et al. Noninvasive Sensors for Brain-Machine Interfaces Based on Micropatterned Epitaxial Graphene. ACS Appl. Nano Mater, published online March 16, 2023; doi: 10.1021/acsanm.2c05546
