Classification of Semi-Automated Labeled MindRove Armband Recorded EMG Data

Accurate Multi-class EMG signal classification is one of the key aspects of EMG-based prosthesis control. The other is a sufficient database. In this article, the process and classification of EMG signals are presented, which were recorded with the lightweight, easy-to-setup, semi-dry, 8-channeled, wireless MindRove Armband electrode system. Individual finger movements were captured with depth cameras, while the corresponding EMG signal was recorded. The labels about the executed movements were generated with a semi-automated algorithm. On the generated dataset Multiple classifiers, namely Random Forest, Extra Trees, Support Vector Machine, Nu-SVM, EEGNet, Ensemble, and Voting methods were tested and compared. Moreover, parameter searches were conducted, to increase the accuracy levels. In the case of EEGNet, the effect of transfer learning was also investigated.