Imperial College London
Portrait Picture

Professor Aldo Faisal

Faculty of Engineering, Department of Bioengineering

Professor of AI & Neuroscience
 
 
 
//

Contact

 

+44 (0)20 7594 6373a.faisal Website

 
 
//

Assistant

 

Miss Teresa Ng +44 (0)20 7594 8300

 
//

Location

 

4.08Royal School of MinesSouth Kensington Campus

//

Summary

 

Publications

Citation

BibTex format

@inproceedings{Fara:2013:10.1109/NER.2013.6696036,
author = {Fara, S and Vikram, CS and Gavriel, C and Faisal, AA},
doi = {10.1109/NER.2013.6696036},
pages = {723--726--723--726},
title = {Robust, ultra low-cost MMG system with brain-machine-interface applications},
url = {http://dx.doi.org/10.1109/NER.2013.6696036},
year = {2013}
}
Download

RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - 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.
AU  - Fara,S
AU  - Vikram,CS
AU  - Gavriel,C
AU  - Faisal,AA
DO  - 10.1109/NER.2013.6696036
EP  - 726
PY  - 2013///
SN  - 1948-3546
SP  - 723
TI  - Robust, ultra low-cost MMG system with brain-machine-interface applications
UR  - http://dx.doi.org/10.1109/NER.2013.6696036
ER  -
Download