Towards robust HD EMG pattern recognition: Reducing electrode displacement effect using structural similarity

Even small changes of electrode recording sites after training a classifier heavily influence robustness and usability of traditional pattern recognition-based myoelectric control schemes. This effect occurs during donning and doffing of the prosthesis or when changing the arm position and generally leads to a significant decrease of classification accuracy. On the other hand, image representations taken from high density electromyographic (EMG) signals offer high spatial resolution and only seem to change slightly during electrode shift, preserving most structural information. In this paper, we present a simple one-against-one nearest neighbor classifier based on the Structural Similarity Index (SSIM). SSIM quantifies visual similarity of two images based on decomposition into three components: luminance, contrast and structure. Our experimental results indicate that an SSIM-based classifier can outperform an LDA-based classifier using structural information taken from high density EMG signals during simulated electrode shift.