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

Research Center for Neurotechnology, Southern Federal University, 344006 Rostov-on-Don, Russia
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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
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
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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

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