A Comprehensive sLORETA Study on the Contribution of Cortical Somatomotor Regions to Motor Imagery
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Department of Computer Engineering, Faculty of Engineering, Karadeniz Technical University, 61080 Trabzon, Turkey
2
Department of Physiology, Faculty of Medicine, Karadeniz Technical University, 61080 Trabzon, Turkey
*
Author to whom correspondence should be addressed.
Brain Sci. 2019, 9(12), 372; https://doi.org/10.3390/brainsci9120372
Received: 5 November 2019
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Revised: 6 December 2019
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Accepted: 7 December 2019
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Published: 13 December 2019
(This article belongs to the Collection Collection on Theoretical and Computational Neuroscience)
Brain–computer interface (BCI) is a technology used to convert brain signals to control external devices. Researchers have designed and built many interfaces and applications in the last couple of decades. BCI is used for prevention, detection, diagnosis, rehabilitation, and restoration in healthcare. EEG signals are analyzed in this paper to help paralyzed people in rehabilitation. The electroencephalogram (EEG) signals recorded from five healthy subjects are used in this study. The sensor level EEG signals are converted to source signals using the inverse problem solution. Then, the cortical sources are calculated using sLORETA methods at nine regions marked by a neurophysiologist. The features are extracted from cortical sources by using the common spatial pattern (CSP) method and classified by a support vector machine (SVM). Both the sensor and the computed cortical signals corresponding to motor imagery of the hand and foot are used to train the SVM algorithm. Then, the signals outside the training set are used to test the classification performance of the classifier. The 0.1–30 Hz and mu rhythm band-pass filtered activity is also analyzed for the EEG signals. The classification performance and recognition of the imagery improved up to 100% under some conditions for the cortical level. The cortical source signals at the regions contributing to motor commands are investigated and used to improve the classification of motor imagery.
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Keywords:
EEG; motor imagery; somatomotor area; brain–computer interface (BCI)
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MDPI and ACS Style
Yazici, M.; Ulutas, M.; Okuyan, M. A Comprehensive sLORETA Study on the Contribution of Cortical Somatomotor Regions to Motor Imagery. Brain Sci. 2019, 9, 372. https://doi.org/10.3390/brainsci9120372
AMA Style
Yazici M, Ulutas M, Okuyan M. A Comprehensive sLORETA Study on the Contribution of Cortical Somatomotor Regions to Motor Imagery. Brain Sciences. 2019; 9(12):372. https://doi.org/10.3390/brainsci9120372
Chicago/Turabian StyleYazici, Mustafa, Mustafa Ulutas, and Mukadder Okuyan. 2019. "A Comprehensive sLORETA Study on the Contribution of Cortical Somatomotor Regions to Motor Imagery" Brain Sciences 9, no. 12: 372. https://doi.org/10.3390/brainsci9120372
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