Continuous Detection and Decoding of Dexterous Finger Flexions With Implantable MyoElectric Sensors

A rhesus monkey was trained to perform individuated and combined finger flexions of the thumb, index, and middle finger. Nine implantable myoelectric sensors (IMES) were then surgically implanted into the finger muscles of the monkey's forearm, without any adverse effects over two years postimplantation. Using an inductive link, EMG was wirelessly recorded from the IMES as the monkey performed a finger flexion task. The EMG from the different IMES implants showed very little cross correlation. An offline parallel linear discriminant analysis (LDA) based algorithm was used to decode finger activity based on features extracted from continuously presented frames of recorded EMG. The offline parallel LDA was run on intraday sessions as well as on sessions where the algorithm was trained on one day and tested on following days. The performance of the algorithm was evaluated continuously by comparing classification output by the algorithm to the current state of the finger switches. The algorithm detected and classified seven different finger movements, including individual and combined finger flexions, and a no-movement state (chance performance = 12.5%) . When the algorithm was trained and tested on data collected the same day, the average performance was 43.8±3.6% n=10. When the training-testing separation period was five months, the average performance of the algorithm was 46.5±3.4% n=8. These results demonstrated that using EMG recorded and wirelessly transmitted by IMES offers a promising approach for providing intuitive, dexterous control of artificial limbs where human patients have sufficient, functional residual muscle following amputation.

I. Introduction

A major challenge facing the field of neural prosthetics is the long-term and stable acquisition of a sufficient number of neural signals to allow users intuitive control of their prosthesis. As upper limb prosthetics are built with increasingly more degrees of freedom [1], the need to access information rich, and independent control signals in a minimally invasive but robust manner will become even greater. Neural prosthetic devices typically gain access to neural signals by using percutaneous connectors. These percutaneous connectors are susceptible to infection and increase the risk of the prosthesis user suffering additional limb trauma, e.g., dislodging the connector and/or the implanted neural recording device thus damaging the nerve and the area surrounding the implant. Electromyographically (EMG) controlled prostheses using surface EMG electrodes avoid problems with infection and connector failure and have been shown to have comparable decode accuracy to intramuscular EMG [2]; however, it can be difficult to obtain more than three or four stable, sufficiently uncorrelated control signals on a residual limb using surface EMG electrodes [3]. Implantable EMG sensors may be able to obtain a higher number of stable, long-term, sufficiently uncorrelated control signals from a residual limb than would surface electrodes, while avoiding the risks associated with percutaneous connectors. Therefore we tested whether Implantable MyoElectric Sensors (IMES) could serve as a stable long-term interface for EMG signals from the finger and thumb flexors and extensors in a macaque monkey.