/New machine learning method can decode brain signal patterns for specific behaviors

New machine learning method can decode brain signal patterns for specific behaviors

Summary: A new algorithm has the ability to decode brain signals because it considers both brain signals and the behavioral signals such as the speed of arm movements.

Original author and publication date: Emily Henderson – November 10, 2020

Futurizonte Editor’s Note: Is it a good idea to allow artificial intelligence to decode our brain?

From the article:

At any given moment in time, our brain is involved in various activities. For example, when typing on a keyboard, our brain not only dictates our finger movements but also how thirsty we feel at that time. As a result, brain signals contain dynamic neural patterns that reflect a combination of these activities simultaneously. A standing challenge has been isolating those patterns in brain signals that relate to a specific behavior, such as finger movements.

Further, developing brain-machine interfaces (BMIs) that help people with neurological and mental disorders requires the translation of brain signals into a specific behavior, a problem called decoding. This decoding also depends on our ability to isolate neural patterns related to specific behaviors. These neural patterns can be masked by patterns related to other activities and can be missed by standard algorithms.

Led by Maryam Shanechi, Assistant Professor and Viterbi Early Career Chair in Electrical and Computer Engineering at the USC Viterbi School of Engineering, researchers have developed a machine learning algorithm that resolved the above challenge. The algorithm published in Nature Neuroscience uncovered neural patterns missed by other methods and enhanced the decoding of behaviors that originated from signals in the brain. This algorithm is a significant advance in modeling and decoding of complex brain activity which could both enable new neuroscience discoveries and enhance future brain-machine interfaces.

Standard algorithms, says Shanechi, can miss some neural patterns related to a given behavior that are masked by patterns related to other functions happening simultaneously. Shanechi and her PhD student Omid Sani developed a machine learning algorithm to resolve this challenge

We have developed an algorithm that, for the first time, can dissociate the dynamic patterns in brain signals that relate to specific behaviors one is interested in. Our algorithm was also much better at decoding these behaviors from the brain signals.”

Maryam Shanechi, Lead Senior Author

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