An Unsorted Spike-Based Pattern Recognition Method for Real-Time Continuous Sensory Event Detection from Dorsal Root Ganglion Recording

Sungmin Han, Jun Uk Chu, Hyungmin Kim, Kuiwon Choi, Jong Woong Park, Inchan Youn

Research output: Contribution to journalArticle

7 Citations (Scopus)


In functional neuromuscular stimulation systems, sensory information-based closed-loop control can be useful for restoring lost function in patients with hemiplegia or quadriplegia. The goal of this study was to detect sensory events from tactile afferent signals continuously in real time using a novel unsorted spike-based pattern recognition method. The tactile afferent signals were recorded with a 16-channel microelectrode in the dorsal root ganglion, and unsorted spike-based feature vectors were extracted as a novel combination of the time and time-frequency domain features. Principal component analysis was used to reduce the dimensionality of the feature vectors, and a multilayer perceptron classifier was used to detect sensory events. The proposed method showed good performance for classification accuracy, and the processing time delay of sensory event detection was less than 200 ms. These results indicated that the proposed method could be applicable for sensory feedback in closed-loop control systems.

Original languageEnglish
Article number7350222
Pages (from-to)1310-1320
Number of pages11
JournalIEEE Transactions on Biomedical Engineering
Issue number6
Publication statusPublished - 2016 Jun 1



  • Pattern recognition
  • sensory event detection
  • sensory feedback
  • unsorted spike

ASJC Scopus subject areas

  • Biomedical Engineering

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