Researchers at the University of Michigan in Ann Arbor are paving the way for long-lasting brain implants that can treat neurological diseases and enable mind-controlled prosthetics and machines.
The scientists have reduced the power requirements of neural interfaces while improving their accuracy. The team, led by Cynthia Chestek, associate professor of biomedical engineering and faculty at the school’s Robotics Institute, estimated a 90 percent drop in power consumption of neural interfaces.
“Currently, interpreting brain signals into someone’s intentions requires computers as tall as people and lots of electrical power – several car batteries worth,” says Samuel Nason, first author of the study and a Ph.D. candidate in Chestek’s cortical neural prosthetics laboratory. “Reducing the amount of electrical power by an order of magnitude will eventually allow for at-home brain-machine interfaces.”
The computers and electrodes can gather information from neurons, the cells in human brains that relay information and action around the body. The neurons, however, give off a lot of “buzzing,” and the computers and electrodes must distinguish the necessary information. Because of all the extra noise, devices have to use power and processing beyond the limits of safe implantable devices.
Currently, to predict complex behaviors such as grasping an item in a hand from neuron activity, scientists can use transcutaneous electrodes, or direct wiring through the skin to the brain. This is achievable through 100 electrodes that capture 20,000 signals per second and enables accomplishments such as reenabling an arm that was paralyzed or allowing someone with a prosthetic hand to feel how hard or soft an object is. This approach, however, is impractical outside of a lab environment and carries a risk of infection.
Some wireless implants, created using efficient, application-specific integrated circuits, can achieve almost equal performance as the transcutaneous systems, with the ability to gather and transmit about 16,000 signals per second. However, scientists have yet to achieve consistent operation, and it is difficult to get safety approval for custom-built chips as opposed to industrial-made ones.
“This is a big leap forward,” Chestek says. “To get the high bandwidth signals we currently need for brain machine interfaces out wirelessly would be completely impossible given the power supplies of existing pacemaker-style devices.”
To reduce power and data needs, researchers compress the brain signals. Focusing on neural activity spikes that cross a certain threshold of power, called threshold crossing rate, means less data needs to be processed while still being able to predict firing neurons. However, the rate requires the device to listen to all activity in the brain to determine when a threshold is crossed, and the threshold can change from one brain to another and within the same brain on different days. This requires tuning the threshold, and additional hardware, battery, and time to do so.
The U-M team compressed the data in another way. It dialed in to a specific feature of neuron data called spiking-band power. The power is an integrated set of frequencies from multiple neurons, between 300 and 1,000 hertz. By listening only to this range of frequencies and ignoring others, the team found a highly accurate prediction of behavior with dramatically lower power needs.
Compared to transcutaneous systems, the team found the spiking-band power technique to be as accurate while taking in one-tenth as many signals, 2,000 versus 20,000 per second. Compared to other methods such as using a threshold crossing rate, the team’s approach requires less raw data but is more accurate at predicting neuron firing and does not require tuning a threshold.
The team’s method solves another problem limiting an implant’s useful life. Over time, an interface’s electrodes fail to read the signals over the “noise” in the brain. Because the new technique performs just as well when a signal is half of what is required from other techniques, implants could be left in place and used longer.
The method can be used in new devices and implemented in existing ones.
“It turns out that many devices have been selling themselves short,” Nason says. “These existing circuits, using the same bandwidth and power, are now applicable to the whole realm of brain-machine interfaces.”
The study, “A low-power band of neuronal spiking activity dominated by local single units improves the performance of brain-machine interfaces,” is published in Nature Biomedical Engineering.
The research was supported by the National Science Foundation, the Craig H. Neilsen Foundation, the A. Alfred Taubman Medical Research Institute, the National Institutes of Health, and MCubed.