Robust, ultra low-cost MMG system with brain-machine-interface applications

Muscle activity is the basis of many brain-machine interface (BMI) applications, but the mainstream EMG-based technology to decode muscle activity has significant constraints when long-term BMI usage “in-the-wild” is required (e.g. controlling neuroprosthetics throughout the day). We use the surface mechanomyogram (MMG), the mechano-acoustic signal generated by lateral oscillations of the muscle fibres during muscle contraction, as source of reliable and robust information of muscle activity. We present our novel MMG sensor and instrumentation, which is designed to match the acoustical properties of muscle signals, while costing at ≈ 10 USD per channel a fraction of current commercial systems. We are able to derive an `MMG Score´ from our sensor-specific signal, which correlates linearly with isometric contraction forces. We test the effectiveness of our MMG system vs EMG using a simple BMI task, where subjects have to interactively control three distinct force states with their muscle activity. Crucially, our MMG Score is robust across subjects, thus calibration on one set of subjects, allows us to predict muscle force production from MMG on other subjects. This limits the need for re-calibration when (re)applying our MMG system to patients on a daily basis, important to minimise carer-dependence and maximise ease of use for BMI users.