The objective of this work is to develop a robust method for myoelectric control towards alleviating the in-terference of electrode shift. Methods: In the proposed method, a preprocessing approach was first performed to convert high-den-sity surface electromyogram (HD-sEMG) signals into a series of images, and the electrode shift appeared as pixel shift in these im-ages. Next, a data augmentation approach was applied to the train-ing data from just one position (no shift), so as to simulate HD-sEMG images derived from fictitious shift positions. The dilated convolutional neural network (DCNN) was subsequently adopted for classification. Compared to common convolutional neural net-work, DCNN always contained a larger receptive field that was supposed to be adept at mining wider spatial contextual infor-mation in images. This property was further confirmed to facili-tate the classification of myoelectric patterns using HD-sEMG. The performance of the proposed method was evaluated with HD-sEMG data recorded by a 10 10 electrode array placed over forearm extensors of ten subjects during their performance of six wrist and finger extension tasks. Results: Under a variety of actual electrode shift conditions, the proposed method achieved a mean classification accuracy of 95.34%, and it outperformed other com-mon methods. Conclusion: This work demonstrated feasibility and usability of combining data augmentation and DCNN in predict-ing myoelectric patterns in the context of electrode shifts. Signifi-cance: The proposed method is a practical solution for robust my-oelectric control against electrode array shifts.
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