When it comes to advancements for people with neurological disorders and paralysis, brain-computer interface (BCI) projects from Neuralink and the Defense Advanced Research Projects Agency (DARPAs) Brain Initiative are promising, especially when it comes to mobility.
There are some major problems, however. Progress in this area is often slow, expensive, and generally fails to transition from a lab environment to the real world.
But that’s not to say there aren’t other organizations making huge strides in BCI technology for people with paralysis.
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This week saw one paper published by a German, Italian, Swiss and American academic team looking at exactly that. It shared a successful research project in which three participants with tetraplegic spinal cord injuries (paralyzed from the shoulders down) successfully operated an electric wheelchair using their minds. This included navigating, steering, turning and controlling the speed of the chair through an obstacle course in the hospital.
Participants wore an electrode-covered hood that recorded the brain’s electrical activity, known as an electroencephalogram (EEG). An amplifying device sent these electrical signals to a computer that interpreted the intentions and turned them into motion.
The participants trained by visualizing the wheelchair in motion, such as turning a steering wheel, and expanded their capabilities over three sessions per week over five months.
Why this research matters
Besides being a good example of inter-institutional cooperation, the research is important for several reasons. Until now, this kind of machine control required people to undergo surgical insertion of electrical implants into the motor areas of their brains.
The operation is not only risky, but also unsuitable for many people. In addition, this is the only study of its kind that is not limited to able-bodied subjects. You read that right, most studies do not use the people who would be the end users of the technology.
The study is also important because it involved an intensive and long training period that allowed the participants’ skills to grow over time. It also offered skill development in ways applicable to a real-world setting.
At the end of the research project, two participants were able to move the wheelchairs with an impressive accuracy of 95 to 98%, an improvement over the original scores of 43 to 55%. This result stemmed from improvements in the computer’s ability to decode the brain activity patterns that indicate a desire to move left or right. The third participant did not improve.
And as the computer’s AI got smarter during the longitudinal training, the most successful participants also learned how to work of the computer. Users were able to help the computer understand their intentions, effectively learning as they learned. This led to a change in their brain activity and shows possibilities for people who could benefit from training to control a motorized wheelchair using a brain-machine interface.
Tetraplegia is one of the most debilitating conditions out there. While the study represents only a small test sample of people with severe disabilities, it sets a precedent for future research and shows a new way forward for neurological research.