Detection of motor imagery based on short-term entropy of time–frequency representations

BioMedical Engineering OnLine

Research

Isidora Stanković¹  Luka Batistić²  Jonatan Lerga² 

[1] University of Montenegro, Džordža Vašingtona bb, 81000, Podgorica, Montenegro;University of Rijeka - Faculty of Engineering, Vukovarska 58, 51000, Rijeka, Croatia;Center for Artificial Intelligence and Cybersecurity, University of Rijeka, R. Matejčić 2, 51000, Rijeka, Croatia;

关键词: Brain–computer interface;    Electroencephalography;    Information entropy;    Motor imagery;    Movement detection;    Time–frequency representations;   

DOI  :  10.1186/s12938-023-01102-1

 received in 2022-11-14, accepted in 2023-04-25,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundMotor imagery is a cognitive process of imagining a performance of a motor task without employing the actual movement of muscles. It is often used in rehabilitation and utilized in assistive technologies to control a brain–computer interface (BCI). This paper provides a comparison of different time–frequency representations (TFR) and their Rényi and Shannon entropies for sensorimotor rhythm (SMR) based motor imagery control signals in electroencephalographic (EEG) data. The motor imagery task was guided by visual guidance, visual and vibrotactile (somatosensory) guidance or visual cue only.ResultsWhen using TFR-based entropy features as an input for classification of different interaction intentions, higher accuracies were achieved (up to 99.87%) in comparison to regular time-series amplitude features (for which accuracy was up to 85.91%), which is an increase when compared to existing methods. In particular, the highest accuracy was achieved for the classification of the motor imagery versus the baseline (rest state) when using Shannon entropy with Reassigned Pseudo Wigner–Ville time–frequency representation.ConclusionsOur findings suggest that the quantity of useful classifiable motor imagery information (entropy output) changes during the period of motor imagery in comparison to baseline period; as a result, there is an increase in the accuracy and F1 score of classification when using entropy features in comparison to the accuracy and the F1 of classification when using amplitude features, hence, it is manifested as an improvement of the ability to detect motor imagery.

【 授权许可】

CC BY   
© The Author(s) 2023

【 图 表 】

41116_2023_36_Article_IEq165.gif

41116_2023_36_Article_IEq164.gif

41116_2023_36_Article_IEq163.gif

41116_2023_36_Article_IEq162.gif

41116_2023_36_Article_IEq161.gif

41116_2023_36_Article_IEq160.gif

41116_2023_36_Article_IEq159.gif

41116_2023_36_Article_IEq158.gif

41116_2023_36_Article_IEq157.gif

41116_2023_36_Article_IEq156.gif

41116_2023_36_Article_IEq155.gif

41116_2023_36_Article_IEq154.gif

40517_2023_256_Article_IEq75.gif

41116_2023_36_Article_IEq153.gif

41116_2023_36_Article_IEq152.gif

41116_2023_36_Article_IEq151.gif

Fig. 13

41116_2023_36_Article_IEq150.gif

40517_2023_256_Article_IEq264.gif

41116_2023_36_Article_IEq149.gif

41116_2023_36_Article_IEq146.gif

Fig. 1

41116_2023_36_Article_IEq145.gif

Fig. 1

41116_2023_36_Article_IEq143.gif

41116_2023_36_Article_IEq142.gif

Fig. 7

Fig. 2

40517_2023_256_Article_IEq71.gif

40517_2023_256_Article_IEq68.gif

Fig. 1

Fig. 5

41116_2023_36_Article_IEq141.gif

41116_2023_36_Article_IEq140.gif

41116_2023_36_Article_IEq139.gif

41116_2023_36_Article_IEq138.gif

41116_2023_36_Article_IEq137.gif

41116_2023_36_Article_IEq135.gif

41116_2023_36_Article_IEq134.gif

40517_2023_256_Article_IEq58.gif

41116_2023_36_Article_IEq133.gif

41116_2023_36_Article_IEq132.gif

41116_2023_36_Article_IEq131.gif

41116_2023_36_Article_IEq130.gif

41116_2023_36_Article_IEq129.gif

41116_2023_36_Article_IEq128.gif

41116_2023_36_Article_IEq127.gif

41116_2023_36_Article_IEq122.gif

41116_2023_36_Article_IEq120.gif

41116_2023_36_Article_IEq109.gif

Fig. 3

41116_2023_36_Article_IEq32.gif

41116_2023_36_Article_IEq28.gif