Spatio-temporal feature extraction in sensory electroneurographic signals

C. Silveira*, R. N. Khushaba, E. Brunton, K. Nazarpour

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

The recording and analysis of peripheral neural signal can provide insight for various prosthetic and bioelectronics medicine applications. However, there are few studies that investigate how informative features can be extracted from population activity electroneurographic (ENG) signals. In this study, five feature extraction frameworks were implemented on sensory ENG datasets and their classification performance was compared. The datasets were collected in acute rat experiments where multi-channel nerve cuffs recorded from the sciatic nerve in response to proprioceptive stimulation of the hindlimb. A novel feature extraction framework, which incorporates spatio-temporal focus and dynamic time warping, achieved classification accuracies above 90% while keeping a low computational cost. This framework outperformed the remaining frameworks tested in this study and has improved the discrimination accuracy of the sensory signals. Thus, this study has extended the tools available to extract features from sensory population activity ENG signals.
Original languageEnglish
Pages (from-to)1-17
Number of pages17
JournalPhilosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
Issue number2228
Early online date6 Jun 2022
Publication statusPublished - 25 Jul 2022

Keywords / Materials (for Non-textual outputs)

  • peripheral nerve recording
  • classification of sensory signals


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