Raw EMG classification using extreme value machine

Electromyogram (EMG) signal is considered as an easy-to-capture (i.e. skin-mounted) and promissing biometric for the control of prosthetic hands. Despite the plethora number of researches on EMG-based control of prosthetics, two major challenges have not sufficiently been addressed , i.e. realtime classification, and robustness against noise. Our contribution in this paper is two fold. First, we have proposed a deep neural network (DNN) model with customized architecture to learn features directly from raw EMG data. In this model, a self-improvement module enhances the accuracy and robustness against artifact. Second, we utilize extreme value machine (EVM) for classification of the learnt feature. EVM models the statistical distribution of the latent space and classifies finger movements based on the maximum cumulative distribution probability to the fitted model. Our experimental results, using public domain EMG data, are very promising. They indicate the average accuracy of 98.5% for 750 msec window for 10-class classification. This result is superior or competitive compared with other online classifications reported in the literature on the same dataset. Additionally, our results illustrate that self-improvement feature learner (SIFL) is much more resistant against the noise than the models that employ engineered features. For example, in presence of severe white noise, the accuracy of our methodology degrades 10 - 15% compared to 30 - 50% of others.