C. Köllod, Nikomidisz Jorgosz Eftimiu, G. Márton, I. Ulbert
{"title":"Classification of Semi-Automated Labeled MindRove Armband Recorded EMG Data","authors":"C. Köllod, Nikomidisz Jorgosz Eftimiu, G. Márton, I. Ulbert","doi":"10.1109/CINTI-MACRo57952.2022.10029540","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":18535,"journal":{"name":"Micro","volume":"14 1","pages":"000381-000386"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Micro","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CINTI-MACRo57952.2022.10029540","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
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.