The EEG-Based Fusion Entropy-Featured Identification of Isometric Contraction Forces under the Same Action

This study explores the important role of assessing force levels in accurately controlling upper limb movements in human-computer interfaces. It uses a new method that combines entropy to improve the recognition of force levels. This research aims to differentiate between different levels of isometr...

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Bibliographic Details
Published inSensors (Basel, Switzerland) Vol. 24; no. 7; p. 2323
Main Authors Yao, Bo, Wu, Chengzhen, Zhang, Xing, Yao, Junjie, Xue, Jianchao, Zhao, Yu, Li, Ting, Pu, Jiangbo
Format Journal Article
LanguageEnglish
Published Switzerland MDPI AG 01.04.2024
Subjects
Accuracy
Analysis
Brain research
Classification
Classification schemes
EEG
Electrodes
Electroencephalography
Entropy
fusion entropy
Humans
Isometric Contraction
Movement
Phase transitions
Quality of life
Recognition, Psychology
Rehabilitation
Signal processing
China
entropy
isometric contraction
EEG
fusion entropy
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Summary:This study explores the important role of assessing force levels in accurately controlling upper limb movements in human-computer interfaces. It uses a new method that combines entropy to improve the recognition of force levels. This research aims to differentiate between different levels of isometric contraction forces using electroencephalogram (EEG) signal analysis. It integrates eight different entropy measures: power spectrum entropy (PSE), singular spectrum entropy (SSE), logarithmic energy entropy (LEE), approximation entropy (AE), sample entropy (SE), fuzzy entropy (FE), alignment entropy (PE), and envelope entropy (EE). The findings emphasize two important advances: first, including a wide range of entropy features significantly improves classification efficiency; second, the fusion entropy method shows exceptional accuracy in classifying isometric contraction forces. It achieves an accuracy rate of 91.73% in distinguishing between 15% and 60% maximum voluntary contraction (MVC) forces, along with 69.59% accuracy in identifying variations across 15%, 30%, 45%, and 60% MVC. These results illuminate the efficacy of employing fusion entropy in EEG signal analysis for isometric contraction detection, heralding new opportunities for advancing motor control and facilitating fine motor movements through sophisticated human-computer interface technologies.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s24072323