Abstract
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.
Highlights
The type of assistive robot perhaps most closely integrated to the life of an individual is the robotic prosthetic
We demonstrate that statistical fusion of electromyographic and electroencephalographic classification can be achieved, improving classification accuracy of multi-tasking activities
We present a proof-ofconcept multimodal system utilising low-cost EMG and EEG sensors along with statistical late fusion methods to successfully classify bioelectric data, with accuracy of up to 99%
Summary
The type of assistive robot perhaps most closely integrated to the life of an individual is the robotic prosthetic. There is a significant need in much of assistive robotics to reduce the degree of abstraction between the controller and the robot; when a device acts as a literal extension of the human body it ought to be as natural to use and as responsive as the body itself. This is one of the core justifications for the use of bioelectric data in robotic control, the high cost [1] of many medical devices is a barrier to accessibility for many amputees.
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