A new machine-learning algorithm can successfully determine which specific behaviors—like walking and breathing—belong to which specific brain signal, and it has the potential to help the military maintain a more ready force.
At any given time, people perform a myriad of tasks. All of the brain and behavioral signals associated with these tasks mix together to form a complicated web. Until now, this web has been difficult to untangle and translate.
But researchers funded by the U.S. Army developed a machine-learning algorithm that can model and decode these signals, according to a Nov. 12 press release. The research, which used standard brain datasets for analysis, was recently published in the journal Nature Neuroscience.
“Our algorithm can, for the first time, dissociate the dynamic patterns in brain signals that relate to specific behaviors and is much better at decoding these behaviors,” Dr. Maryam Shanechi, the engineering professor at the University of Southern California who led the research, said in a statement.
Dr. Hamid Krim, a program manager at the Army Research Office, part of the U.S. Army Combat Capabilities Development Command’s Army Research Laboratory, told Nextgov Shanechi and her team used the algorithm to separate what they call behaviorally relevant brain signals from behaviorally irrelevant brain signals.
“This presents a potential way of reliably measuring, for instance, the mental overload of an individual, of a soldier,” Krim said.
If the algorithm detects behavior indicating a soldier is stressed or overloaded, then a machine could alert that soldier before they are even able to recognize their own fatigue, Krim said. Improving self-awareness is central to the Army’s interest in this research, he added.
The research is part ..
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