Decoding M1 neurons during multiple finger movements

Abstract

We tested several techniques for decoding the activity of primary motor cortex (M1) neurons during movements of single fingers or pairs of fingers. We report that single finger movements can be decoded with >99% accuracy using as few as 30 neurons randomly selected from populations of task-related neurons recorded from the M1 hand representation. This number was reduced to 20 neurons or less when the neurons were not picked randomly but selected on the basis of their information content. We extended techniques for decoding single finger movements to the problem of decoding the simultaneous movement of two fingers. Movements of pairs of fingers were decoded with 90.9% accuracy from 100 neurons. The techniques we used to obtain these results can be applied, not only to movements of single fingers and pairs of fingers as reported here, but also to movements of arbitrary combinations of fingers. The remarkably small number of neurons needed to decode a relatively large repertoire of movements involving either one or two effectors is encouraging for the development of neural prosthetics that will control hand movements.