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Erschienen in:
2019 | OriginalPaper | Buchkapitel
Hybrid Gaussian Point-Process Model for Finer Control of Myoelectric Robotic Hands
verfasst von : Sohail Siadatnejad, Francesco Negro, Luca Citi
Erschienen in: Converging Clinical and Engineering Research on Neurorehabilitation III
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Abstract
This paper presents a novel mathematical approach to decode information from neural spike trains recorded using high-density surface EMG for the control of a robotic hand. During precision grasps characterised by low firing rates, the algorithm uses a point-process approach to extract the information encoded in the spike times. When performing power grasps, the increased firing rate makes the point-process less reliable and the algorithm automatically and gradually switches to an approach based on the EMG power. Unlike current point-process decoding algorithm relying on a Gaussian approximations of the observation model, this model adopts a fully Gaussian state-space representation thanks to a parametrization of the rate of an inverse Gaussian distribution and by modelling the inter-spike interval directly.