Neuron Selection Based on Deflection Coefficient Maximization for the Neural Decoding of Dexterous Finger Movements


Future generations of brain-machine interface (BMI) will need a lot of dexterous motion control like hand and finger movements. Since a population of neurons in the first motor cortex (M1) space is correlated with finger movements, neural activities recorded in M1 area are used to reconstruct an supposed finger movement. In an exceedingly BMI system, decoding discrete finger movements from a giant range of input neurons will not guarantee the next decoding accuracy notwithstanding the rise in computational burden. Hence, we hypothesize that choosing neurons important for coding dexterous flexion/extension of finger movements would improve the BMI performance. In this paper, two metrics are presented to quantitatively live the importance of each neuron primarily based on Bayes risk minimization and deflection coefficient maximization in an exceedingly statistical decision drawback. Since motor cortical neurons are active with movements of several different fingers, the proposed methodology is additional appropriate for a discrete decoding of flexion-extension finger movements than the previous strategies for decoding reaching movements. In particular, the proposed metrics yielded high decoding accuracies across all subjects and conjointly within the case of including six combined 2-finger movements. Whereas our knowledge acquisition and analysis was done off-line and post processing, our results point to the importance of highly coding neurons in improving BMI performance.

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