Individualized Inter-Stimulus Interval Estimation for Neural Facilitation in Human Motor System: A Particle Filtering Approach

Quantitative understanding of the human neuromotor system is essential for the implementation of the future robotic therapeutic exercises. For this purpose, sensorimotor adaptations in voluntary and involuntary movements facilitated by peripheral stimulation and resultant motor-evoked potentials (MEP) must be well characterized. One such facilitation exercise is paired associative stimulation (PAS). However, effective inter-stimulus intervals between cortical and peripheral stimulations are highly variable between individuals due to different physiological characteristics. Past studies measured MEPs in a wide range of time by incrementally varying inter-stimulus intervals to find the optimal interval in a specific subject, which has been a time-consuming process. This paper develops a search algorithm based on particle filtering to estimate individualized inter-stimulus intervals for PAS with mechanical muscle tendon stimulation realized by a pneumatically-operated robotic neuromodulatory system. The particle filter-based method reduces the number of PAS trials 70%–80% in comparison to the conventional incremental method. An accelerometer attached to the robotic system that measures exact timings of tendon stimulation can further reduce the number of trials.