Dynamic fusion of electromyographic and electroencephalographic data towards use in robotic prosthesis control

Michael Pritchard, Abraham Itzhak Weinberg, John A.R. Williams, Felipe Campelo, Harry Goldingay, Diego R. Faria

Research output: Contribution to journalConference articlepeer-review

3 Scopus citations


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.

Original languageEnglish
Article number012056
JournalJournal of Physics: Conference Series
Issue number1
StatePublished - 4 Mar 2021
Externally publishedYes
Event2020 International Symposium on Automation, Information and Computing, ISAIC 2020 - Beijing, Virtual, China
Duration: 2 Dec 20204 Dec 2020

ASJC Scopus subject areas

  • Physics and Astronomy (all)


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