Adaptive Calibration of Electrode Array Shifts Enables Robust Myoelectric Control

The objective of this work is to develop a novel method for adaptive calibration of the electrode array shifts toward achieving robust myoelectric pattern-recognition control.

This work is inspired by the idea of image object detection when high-density surface electromyogram signals recorded from a two-dimensional electrode array carry rich spatial information and can serve as a muscular activation image. A convolutional neural network involving transfer learning (from a network for image recognition) is used to learn muscular activity patterns at an original/baseline position of an electrode array. The shift of an electrode array can be estimated by identifying and matching partially overlapped regions between the training images of muscular activation and the testing images at any other position, in an unsupervised manner. Given the calibration of an electrode array shift, the identification of muscular activity patterns is implemented accordingly. The performance of the proposed method was evaluated with data recorded by a 10 × 10 electrode array placed over the forearm extensors of 10 subjects performing 6 wrist and finger extension tasks.

The proposed method achieved high task classification accuracies around 95% and outperformed five traditional methods under conditions with multiple designated shifts in offline experiments and a random shift in online testing.

The proposed method is demonstrated to be a promising solution for the automatic and adaptive calibration of electrode array shifts.

This work will enhance the robustness of myoelectric control systems.