Detecting the universal adversarial perturbations on high-density sEMG signals

Myoelectric pattern recognition is a promising approach for upper limb neuroprosthetic control. Convolutional neural networks (CNN) are increasingly used in dealing with the electromyography (EMG) signal collected by high-density electrodes due to its capacity to take full advantage of spatial information about muscle activity. However, it has been found that CNN models are very vulnerable to well-designed and tiny perturbations, such like universal adversarial perturbation (UAP). As shown in this work, the CNN-based myoelectric pattern recognition method can achieve a classification accuracy of more than 90%, but can only achieve a classification accuracy of less than 20% after the attack. This type of attack poses a big security concern to prosthetic control. To the best of our knowledge, there is no study on the detection of adversarial attacks to the myoelectric control system. In this paper, a correlation feature based on Chebyshev distance between the adjacent channels is proposed to detect the attack for EMG signals, which will serve as early warning and defense against the adversarial attacks. The performance of the detection framework is assessed with two high-density EMG datasets. The results show that our method has a detection rate of 91.39% and 93.87% for the attacks on both datasets with a latency of no more than 2 ms, which will facilitate the security of muscle–computer interfaces.