Parallels have been drawn between the human brain and the computer since technology’s earliest days. One day, however, computing could be used to help brains damaged by traumatic events like a stroke to work once again.
Like a computer, the brain requires huge numbers of connections to work, allowing messages to be passed from one part of the brain to another, or from the brain to the body. If any of those connections are blocked or broken, the messages can’t get through. In the case of spinal cord injury, messages from the brain to the muscles of the limb might be cut off, leaving paralysis. In the case of stroke, if the language production center of the brain can’t talk to the part that forms speech, the person can be left unable to talk.
The Center for Sensorimotor Neural Engineering (CSNE), based in the US and funded by the country’s National Science Foundation, is developing a mixture of homegrown machine learning software and off-the-shelf hardware that could, in the future, be used to restore limb function to those with brain or spinal cord injury.
Often in the past, researchers focused on trying to tackle the problem of limb paralysis by creating robotic hands or other prostheses that a patient could control using the electrical signals made by their brain. The CSNE is instead hoping to use technology as a bridge between different parts of the nervous system that have become disconnected, enabling those parts that have lost function to become active once again.
“We’re designing devices called brain-computer interfaces. These are implantable devices that can be used for reconnecting parts of the brain and nervous system that have become damaged or disconnected due to injury or other neurological disease… We’re pursuing a non-traditional approach, which is directly building devices that could enhance rehabilitation and allow paralysed limbs to be reanimated,” Rajesh Rao, director of the CSNE, told ZDNet.
The devices could either collect information from one part of the brain, process it, and convey to another using a single integrated chip, or transmit the data wirelessly to an external device where an AI can deal with it before passing it on to the spinal cord, which will in turn translate it into signals to the person’s muscles. Alternatively, if the nature of their injury required a system with more computational power, the device could be stored in another space in their body — in the chest cavity, for example, with wires running from electrodes in the brain under the skin to the device.
“The bottom line is, depending on patient’s requirements and needs, we would have different amounts of computation and algorithmic sophistication in the software and machine learning,” Rao said.
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