Dynamic Fusion of Electromyographic and Electroencephalographic Data towards Use in Robotic Prosthesis Control
Dynamic Fusion of Electromyographic and Electroencephalographic Data towards Use in Robotic Prosthesis Control
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DOI：
10.1088/1742-6596/1828/1/012056
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发表时间：
2021-02
期刊：
影响因子：
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通讯作者：
Michael Pritchard;Abraham Itzhak Weinberg;John A R Williams;F. Campelo;Harry Goldingay;D. Faria
中科院分区：
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作者：
Michael Pritchard;Abraham Itzhak Weinberg;John A R Williams;F. Campelo;Harry Goldingay;D. Faria
我们证明了改进的性能在分类的生物电数据用于系统，如机器人假肢控制，通过数据融合，使用低成本的肌电图（EMG）和脑电图（EEG）设备。假肢通常通过EMG进行控制，虽然有大量研究将EEG用作脑机接口（BCI）的一部分，但EEG设备的成本通常会阻止这种方法在实验室之外被采用。这项研究表明，作为一个概念验证，多模态分类可以通过使用低成本的EMG和EEG设备串联，统计决策级融合，以达到很高的准确度。我们提出了多种融合方法，包括那些以前没有被应用到这个问题的詹森-香农发散的基础上。我们报告的准确性高达99%时，合并两种信号模式，改善最佳情况下的单一模式分类。因此，我们展示了在多模态分类系统中结合EMG和EEG的优势，该系统将来可以作为机器人假体的替代控制机制。
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.