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Researchers at the University of California San Francisco Weill Institute for Neurosciences used machine learning techniques and a different type of sensor to improve on previous BCI systems.
“The BCI field has made great progress in recent years, but because existing systems have had to be reset and recalibrated each day, they haven't been able to tap into the brain's natural learning processes. It's like asking someone to learn to ride a bike over and over again from scratch,” said senior author Karunesh Ganguly. “Adapting an artificial learning system to work smoothly with the brain's sophisticated long-term learning schemas is something that's never been shown before in a person with paralysis.”
The ‘plug and play’ performance relied on an ECoG electrode array, a pad of electrodes about the size of a post-it note that is surgically placed on the surface of the brain. They allow long-term, stable recordings of neural activity and have been approved for seizure monitoring in epilepsy patients.
Past BCI efforts have used ‘pin-cushion’ style arrays of sharp electrodes that penetrate the brain tissue for more sensitive recordings but tend to shift or lose signal over time.
The researchers implanted an ECoG electrode array in an individual with paralysis of all four limbs (tetraplegia). The participant is also enrolled in a clinical trial designed to test the use of ECoG arrays for prosthetic arm and hand control, but they controlled a computer cursor in this research.
The team developed a BCI algorithm that uses machine learning to match brain activity recorded by the ECoG electrodes to the user's desired cursor movements. The researchers initially followed the standard practice of resetting the algorithm each day. The participant would begin by imagining specific neck and wrist movements while watching the cursor move across the screen. Gradually the computer algorithm would update itself to match the cursor's movements to the brain activity this generated, effectively passing control of the cursor to the user.
Starting this process over every day put a “severe limit” on the level of control that could be achieved, a research announcement said. It could take hours to master control of the device, and some days the participant had to give up altogether.
The researchers then switched to allow the algorithm to continue updating to match the participant's brain activity without resetting it each day. They found the “continued interplay” between brain signals and the machine learning-enhanced algorithm resulted in continuous improvements in performance over many days. There was initially a small amount of lost ground each day, but soon the participant was able to immediately achieve top level performance.
“We found that we could further improve learning by making sure that the algorithm wasn't updating faster than the brain could follow – a rate of about once every 10 seconds,” said Ganguly, a practising neurologist. “We see this as trying to build a partnership between two learning systems – brain and computer – that ultimately lets the artificial interface become an extension of the user, like their own hand or arm.”
Over time, the participant's brain was able to amplify patterns of neural activity it could use to most effectively drive the artificial interface via the ECoG array, while eliminating less effective signals – a pruning process very similar to how the brain is thought to learn any complex task, the researchers said.
Once expertise was established, the researchers showed they could turn off the algorithm's need to update itself altogether, and the participant could simply begin using the interface each day without any need for retraining or recalibration. Performance did not decline over 44 days without retraining, and the participant could go days without practising with little decline in performance.
The ECoG arrays are less sensitive than traditional ‘pin cushion’ implants, but long-term stability appears to compensate for that shortcoming. The stability of ECoG recordings could be even more important for long-term control of more complex robotic systems such as artificial limbs, a key goal of the next phase of Ganguly's research.
“We've always been mindful of the need to design technology that doesn't end up in a drawer, so to speak, but which will actually improve the day-to-day lives of paralysed patients,” Ganguly said. “These data show that ECoG-based BCIs could be the foundation for such a technology.”
The research was published today (7 September) in Nature Biotechnology.
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