Timing Analysis of Robotic Neuromodulatory Rehabilitation System for Paired Associative Stimulation

Euisun Kim, Ilya Kovalenko, Lauren Lacey, Minoru Shinohara, Jun Ueda

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11 Scopus citations


In this paper, the timing accuracy of a tapping motion is analyzed for a robotic neuromodulatory rehabilitation system (RNRS). Paired associative stimulation (PAS) refers to repetitive application of conditioned transcranial magnetic stimulation (TMS) with peripheral stimulation at appropriate interstimulus intervals. When overlapped properly, this pairing can be used for rehabilitation through the rebuilding of neural pathways. Therefore in PAS, providing peripheral stimulation at the right time is crucial. The common procedure of PAS in the literature is to use electrical stimulation as peripheral stimulation. However, inspired by the repetitive facilitation exercise (RFE), mechanical stimulation is adopted in this RNRS experiment. Experiments show that RNRS can give mechanical stimulation accurately within a standard deviation of 5 ms. This stimulation is accurate enough when considering a 50 ms overlapping time window, the effective time range over which the overlap happens. The overlapping time window of mechanical stimulation is longer than that of electrical stimulation and it is expected to relax timing requirements in PAS by using RNRS. In addition, fMRI-compatible RNRS will allow further research on how mechanical stimulation itself affects on brain by observing brain activity through fMRI, where a general electrical stimulation device and TMS are not allowed. These results will help to get a consistent neural plasticity effect with RNRS for individuals with stroke and allow performing further research on brain plasticity in fMRI, contributing to the neuromodulation research.

Original languageEnglish (US)
Article number7409975
Pages (from-to)1028-1035
Number of pages8
JournalIEEE Robotics and Automation Letters
Issue number2
StatePublished - Jul 2016

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Biomedical Engineering
  • Human-Computer Interaction
  • Mechanical Engineering
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Control and Optimization
  • Artificial Intelligence


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