Recognition of Motor Imagery Based on Dynamic Features of Electroencephalography Signals

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Dashti Negar; KHEZRI MAHDI

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: Journal of Intelligent Procedures in Electrical Technology Year:2020 | Volume:11 | Issue:43 Page(s): 13-27

Download Full-Text

Persian Verion

View:

354

Download:

Cites:

Information Journal Paper

Title

Recognition of Motor Imagery Based on Dynamic Features of Electroencephalography Signals

Author(s)

Dashti Negar | KHEZRI MAHDI | Issue Writer Certificate

Keywords

Motor imageryQ1

ClassificationQ2

Nonlinear featuresQ1

Wavelet transformQ2

SVMQ1

Abstract

The control of artificial limbs can be done by distinguishing the patterns of imagined movement using the Electroencephalography (EEG) signals. The aim of this study was to identify hand and foot imagery movements based on EEG signals. The IVA dataset of BCI Competition III, which includes EEG signals from 5 healthy individuals in C3, C4 and CZ channels, was used to design the imagery movements detection system. Initially, the basic components of EEG signal noise were removed using the MSPCA method. In the next step, the EEG signals were decomposed in two different ways including frequency filtering using the Butterworth filter and the wavelet packet transform (WPT). In this study, the detrended Fluctuation analysis, Fractal dimension, Correlation dimension, Lempel-ziv complexity and Entropy as nonlinear dynamics features, were calculated for the signals. In both decomposition methods, the desired features were calculated for the temporal version of the specified subbands. In order to determine the best performance of the system, different combinations of the channels and the features were evaluated. The wavelet-based decomposition method, in the case of using all three channels and five features, provided the highest recognition accuracy; So that using support vector machine (SVM) Classification method, the accuracy of 93% was obtained in identifying the desired movements.

Cites

No record.

References

No record.

Cite

APA: Copy

Dashti, Negar, & KHEZRI, MAHDI. (2020). Recognition of Motor Imagery Based on Dynamic Features of Electroencephalography Signals. JOURNAL OF INTELLIGENT PROCEDURES IN ELECTRICAL TECHNOLOGY, 11(43 ), 13-27. SID. https://sid.ir/paper/391746/en

Vancouver: Copy

Dashti Negar, KHEZRI MAHDI. Recognition of Motor Imagery Based on Dynamic Features of Electroencephalography Signals. JOURNAL OF INTELLIGENT PROCEDURES IN ELECTRICAL TECHNOLOGY[Internet]. 2020;11(43 ):13-27. Available from: https://sid.ir/paper/391746/en

IEEE: Copy

Negar Dashti, and MAHDI KHEZRI, “Recognition of Motor Imagery Based on Dynamic Features of Electroencephalography Signals,” JOURNAL OF INTELLIGENT PROCEDURES IN ELECTRICAL TECHNOLOGY, vol. 11, no. 43 , pp. 13–27, 2020, [Online]. Available: https://sid.ir/paper/391746/en

Related Journal Papers