Open AccessDynamic Fusion of Electromyographic and Electroencephalographic Data towards Use in Robotic Prosthesis Control
Author(s) -
Michael Pritchard,
Abraham Itzhak Weinberg,
J.A.R. Williams,
Felipe Campelo,
Harry Goldingay,
Diego R. Faria
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1828/1/012056
Subject(s) - electroencephalography , brain–computer interface , computer science , interface (matter) , sensor fusion , electromyography , artificial intelligence , pattern recognition (psychology) , neurofeedback , modalities , signal (programming language) , physical medicine and rehabilitation , medicine , neuroscience , psychology , social science , bubble , maximum bubble pressure method , parallel computing , sociology , programming language
We demonstrate improved performance in the classification of bioelectric data for use in systems such as robotic prosthesis control, by data fusion using low-cost electromyography (EMG) and electroencephalography (EEG) devices. Prosthetic limbs are typically controlled through EMG, and whilst there is a wealth of research into the use of EEG as part of a brain-computer interface (BCI) the cost of EEG equipment commonly prevents this approach from being adopted outside the lab. This study demonstrates as a proof-of-concept that multimodal classification can be achieved by using low-cost EMG and EEG devices in tandem, with statistical decision-level fusion, to a high degree of accuracy. We present multiple fusion methods, including those based on Jensen-Shannon divergence which had not previously been applied to this problem. We report accuracies of up to 99% when merging both signal modalities, improving on the best-case single-mode classification. We hence demonstrate the strengths of combining EMG and EEG in a multimodal classification system that could in future be leveraged as an alternative control mechanism for robotic prostheses.