Researchers at the University of Southern California Viterbi School of Engineering (USC Viterbi) and New York University have developed a machine learning algorithm that isolates and decodes behaviors based on signals from the brain.
USC Viterbi's Maryam Shanechi said the algorithm "can dissociate the dynamic patterns in brain signals that relate to specific behaviors one is interested in."
The algorithm also can find neural patterns overlooked by other methods, as it considers both brain and behavioral signals, finding common patterns and more effectively decoding behavior represented by neural signals.
USC Viterbi's Omid Sani added that the algorithm simulates common dynamic patterns between any signals, like between signals from different brain regions.
Said Shanechi, "By isolating dynamic neural patterns relevant to different brain functions, this machine learning algorithm can help us investigate basic questions about [the] brain's functions and develop enhanced brain-machine interfaces to restore lost function in neurological and mental disorders."
From USC Viterbi School of Engineering
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