Next Article in Journal
Predicting Children with ADHD Using Behavioral Activity: A Machine Learning Analysis
Previous Article in Journal
An Effective Tangent Stiffness of Train–Track–Bridge Systems Based on Artificial Neural Network
 
Search for Articles:
Title / Keyword
Author / Affiliation
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
Journals
Applied Sciences
Volume 12
Issue 5
10.3390/app12052736
applsci-logo
Submit to this Journal Review for this Journal Edit a Special Issue
► Article Menu

Article Menu

share announcement question_answer thumb_up
...
textsms
...
Article

Discriminative Frequencies and Temporal EEG Segmentation in the Motor Imagery Classification Approach

by
Dmitry Lazurenko
*,
Igor Shepelev
,
Dmitry Shaposhnikov
,
Anton Saevskiy
* and
Valery Kiroy
Research Center for Neurotechnology, Southern Federal University, 344006 Rostov-on-Don, Russia
*
Authors to whom correspondence should be addressed.
Academic Editor: Rubén Usamentiaga
Appl. Sci. 2022, 12(5), 2736; https://doi.org/10.3390/app12052736
Received: 25 November 2021 / Revised: 25 February 2022 / Accepted: 2 March 2022 / Published: 7 March 2022
View Full-Text Download PDF
Browse Figures

Abstract

A linear discriminant analysis transformation-based approach to the classification of three different motor imagery types for brain–computer interfaces was considered. The study involved 16 conditionally healthy subjects (12 men, 4 women, mean age of 21.5 years). First, the search for subject-specific discriminative frequencies was conducted in the task of movement-related activity. This procedure was shown to increase the classification accuracy compared to the conditional common spatial pattern (CSP) algorithm, followed by a linear classifier considered as a baseline approach. In addition, an original approach to finding discriminative temporal segments for each motor imagery was tested. This led to a further increase in accuracy under the conditions of using Hjorth parameters and interchannel correlation coefficients as features calculated for the EEG segments. In particular, classification by the latter feature led to the best accuracy of 71.6%, averaged over all subjects (intrasubject classification), and, surprisingly, it also allowed us to obtain a comparable value of intersubject classification accuracy of 68%. Furthermore, scatter plots demonstrated that two out of three pairs of motor imagery were discriminated by the approach presented. View Full-Text
Keywords: EEG; brain–computer interfaces; motor imagery; machine learning; cross-correlation; frequency power spectrum EEG; brain–computer interfaces; motor imagery; machine learning; cross-correlation; frequency power spectrum
►▼ Show Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
SciFeed

Share and Cite

MDPI and ACS Style

Lazurenko, D.; Shepelev, I.; Shaposhnikov, D.; Saevskiy, A.; Kiroy, V. Discriminative Frequencies and Temporal EEG Segmentation in the Motor Imagery Classification Approach. Appl. Sci. 2022, 12, 2736. https://doi.org/10.3390/app12052736

AMA Style

Lazurenko D, Shepelev I, Shaposhnikov D, Saevskiy A, Kiroy V. Discriminative Frequencies and Temporal EEG Segmentation in the Motor Imagery Classification Approach. Applied Sciences. 2022; 12(5):2736. https://doi.org/10.3390/app12052736

Chicago/Turabian Style

Lazurenko, Dmitry, Igor Shepelev, Dmitry Shaposhnikov, Anton Saevskiy, and Valery Kiroy. 2022. "Discriminative Frequencies and Temporal EEG Segmentation in the Motor Imagery Classification Approach" Applied Sciences 12, no. 5: 2736. https://doi.org/10.3390/app12052736

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Metrics

Article Access Map by Country/Region

1
Back to TopTop