Neural decoding of finger movements using skellam-based maximum-likelihood decoding

Hyun Chool Shin, Vikram Aggarwal, Soumyadipta Acharya, Marc H. Schieber, Nitish V. Thakor

Research output: Contribution to journalArticlepeer-review

35 Scopus citations


We present an optimal method for decoding the activity of primary motor cortex (M1) neurons in a nonhuman primate during single finger movements. The method is based on the maximum-likelihood (ML) inference, which assuming the probability of finger movements is uniform, is equivalent to the maximum a posteriori (MAP) inference. Each neurons activation is first quantified by the change in firing rate before and after finger movement. We then estimate the probability density function of this activation given finger movement, i.e., Pr(neuronal activation (x)| finger movements (m)). Based on the ML criterion, we choose finger movements to maximize Pr(x|m). Experimentally, data were collected from 115 task-related neurons in M1 as the monkey performed flexion and extension of each finger and the wrist (12 movements). With as few as 20-25 randomly selected neurons, the proposed method decoded single-finger movements with 99% accuracy. Since the training and decoding procedures in the proposed method are simple and computationally efficient, the method can be extended for real-time neuroprosthetic control of a dexterous hand.

Original languageEnglish (US)
Article number4895277
Pages (from-to)754-760
Number of pages7
JournalIEEE Transactions on Biomedical Engineering
Issue number3
StatePublished - Mar 2010


  • Finger movements
  • Maximum likelihood
  • Neural decoding
  • Neural prosthetics
  • Skellam

ASJC Scopus subject areas

  • Biomedical Engineering


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