Stable recordings enable ‘plug-and-play’ control of brain–computer interface

By the need for daily recalibration of the decoder that converts neural activity into control signals. Researchers at the UC San Francisco Weill Institute for Neurosciences have now employed machine learning to help an individual with tetraplegia learn to control a computer cursor using their brain activity, without requiring extensive daily retraining.

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