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EEG ÜZERİNDE ORTAYA ÇIKAN BEYİN DALGALARININ ÇENE HAREKETLERİ İLE İLİŞKİ The Relationship Between Brain Waves Based on EEG Signals And Jaw Movements

Year 2019, Volume: 9 Issue: 3, 45 - 49, 16.09.2019

Abstract


ÖZET
Amaç: Bu çalışma, SAM32RFO Elektroensefalografi (EEG) cihazı ve 10/20 sistemine sahip EEG kepi kullanılarak,
10 katılımcıdan belli bir düzen içerisinde kayıtlanmış olan EEG üzerinde ortaya çıkan beyin sinyallerinin
çene hareketleri ile ilişkisinin değerlendirilmesi amacıyla yapılmıştır.
Gereç-Yöntem: Çalışma 03/09/2018-03/10/2018 tarihleri arasında üniversitemiz Elektrik-Elektronik Mühendisliği
Bölümünde, 10 katılımcıdan (3 kadın, 7 erkek) EEG sinyallerinin kayıtlanarak bilgisayar ortamına aktarılması
ile tamamlanmıştır. Verilerin değerlendirilmesinde, standart sapma değişimlerinden faydalanılarak
olasılıksal sinir ağı modeli (PNN) kullanılmıştır. Elde edilen sonuçlar yüzde olarak sunulmuştur.
Bulgular: 21-35 yaş aralığındaki sağlıklı katılımcıların, her bir döngüsü 10 saniye süren ve bu süre boyunca
(dudaklar kapalı, diş gıcırdatma ve vücut hareketi olmaksızın) yaklaşık 20 kez sağa ya da 20 kez sola doğru
çene hareketleri yapabildiği görülmüştür. Çıkarılan tüm özelliklerin bilgisayar ortamındaki makine öğrenme
algoritmaları yardımıyla incelenmesi sonucunda, sağ ve sol çene hareketleri sırasında beyinde oluşan iki
farklı EEG sinyalinin, birbirlerinden %90,14 oranında farklı olarak belirlenebildiği bulunmuş ve beyin haritalama
üzerindeki çıkarımlarda bu oranı desteklemiştir. Çenenin sağa veya sola hareketi ile oksipital, frontal ve
temporal loblarda delta dalgalarına rastlanmıştır.
Sonuç: Literatürde EEG üzerinde ortaya çıkan beyin dalgaları ile çene hareketleri arasındaki ilişkinin incelendiği
ilk çalışmadır. EEG üzerinde parazit (artifakt) oluşturduğu düşünülen bu hareketlerin, delta frekans bandı
üzerinde anlamlı bilgiler taşımakta olduğu anlaşılmıştır. Elde edilen sonuçların bilgisayar ara yüzüne aktarılması
ile de, tetraparezi olan hastalara çevrelerindeki cihazları açıp/kapatmak gibi basit günlük aktivitelerde
yardımcı olunması ile onların yaşam kalitesi arttırılabilir.
Anahtar kelimeler: EEG; Beyin; Çene; Makine öğrenmesi
ABSTRACT
Objective: This study was carried out to evaluate on the relationship between jaw movements and brain
waves based on EEG signals recorded in a certain order from 10 participants using SAM32RFO device and
the EEG cap with the international 10/20 electrode placement system.
Material and Method: EEG signals of ten participants (3 female, 7 male) were recorded and stored at the
Department of Electric and Electronics Engineering of Bozok University between 3 September 2018 and 3
October 2018. In the evaluation of the data, probabilistic neural network model (PNN) was used combining
with standard deviation changes. The results are presented as percentage.
Results: Healthy participants with ages in the range of 21-35 years were succeeded roundly 20 times right
and 20 times left jaw movements during each of the 10 seconds (closed lips, no teeth grinding and no
body movement). It was determined that long-term raw EEG signals recorded during jaw movements can
be obtained as a single feature thanks to standard deviation variable. As a result of the computer-aided
machine learning algorithms, it was found that two different EEG signals that occur in the brain during right
and left jaw movements can be determined as 90.14% different from each other and it was understood that
the brain mapping results are support this conclusion. The right or left movements of the jaw showed delta
waves in the occipital, frontal and temporal lobes.
Conclusion: This is the first study to investigate the relationship between brain waves on EEG and jaw
movements in the literature. It is understood that these movements known as noise (artifact) on EEG, carry
significant information on delta frequency band. The quality of life of the patients with tetraparesis can be
increased by assisting in simple daily activities such as turning on/off the devices around them through a
computer interface.
Keywords: EEG; Brain; Chin; Machine learning

References

  • 1. Wei L, Hu H, Yuan K. Use of forehead bio-signals for controlling an intelligent wheelchair. IEEE International Conference on Robotics and Biomimetics, 2008 22; 108:113. doi:10.1109/ROBIO.2009.4912988 2. Wei L, Hu H, Lu T, Yuan K. Evaluating the performance of a face movement based wheelchair control interface in an indoor environment. IEEE International Conf. on Robotics and Biomimetics, 2010 14; 387:392. doi:10.1109/ROBIO.2010.5723358 3. Jeong JW, Yeo WH, Akhtar A, Norton JJ, Kwack YJ, Li S, et al. Materials and Optimized Designs for Brain‐Machine Interfaces Via Epidermal Electronics. Advanced Materials. 2013; 25: 6839-6846. 4. Paul GM, Cao F, Torah R, Yang K, Beeby S, Tudor J. A smart textile based facial EMG and EOG computer interface. IEEE Sensors Journal. 2014; 14: 393-400. 5.Wei L, Hu H. A hybrid brain-machine interface for hands-free control of an intelligent wheelchair. International Journal of Mechatronics and Automation. 2011; 1: 97-111. 6. Rechy-Ramirez EJ, Hu H. Bi-modal human machine interface for controlling an intelligent wheelchair. IEEE Fourth International Conference on Emerging Security Technologies, 2013 9; 66-70. doi:10.1109/EST.2013.19 7. Costa A, Hortal E, Ianez E, Azorin JM. A supplementary system for a brain–machine interface based on jaw artifacts for the bidimensional control of a robotic arm. PLoS One. 2014; 9:e112352. 8. Linden M, Habib T, Radojevic V. A controlled study of the effects of EEG biofeedback on cognition and behavior of children with attention deficit disorder and learning disabilities. Biofeedback and self-regulation. 1996; 21: 35-49. 9. Azami H, Sanei S, Mohammadi K. A novel signal segmentation method based on standard deviation and variable threshold. Journal of Computer Applications. 2011; 34: 27-34. 10. Specht DF. Probabilistic neural networks. Neural Networks. 1990; 3: 109-118.
Year 2019, Volume: 9 Issue: 3, 45 - 49, 16.09.2019

Abstract

References

  • 1. Wei L, Hu H, Yuan K. Use of forehead bio-signals for controlling an intelligent wheelchair. IEEE International Conference on Robotics and Biomimetics, 2008 22; 108:113. doi:10.1109/ROBIO.2009.4912988 2. Wei L, Hu H, Lu T, Yuan K. Evaluating the performance of a face movement based wheelchair control interface in an indoor environment. IEEE International Conf. on Robotics and Biomimetics, 2010 14; 387:392. doi:10.1109/ROBIO.2010.5723358 3. Jeong JW, Yeo WH, Akhtar A, Norton JJ, Kwack YJ, Li S, et al. Materials and Optimized Designs for Brain‐Machine Interfaces Via Epidermal Electronics. Advanced Materials. 2013; 25: 6839-6846. 4. Paul GM, Cao F, Torah R, Yang K, Beeby S, Tudor J. A smart textile based facial EMG and EOG computer interface. IEEE Sensors Journal. 2014; 14: 393-400. 5.Wei L, Hu H. A hybrid brain-machine interface for hands-free control of an intelligent wheelchair. International Journal of Mechatronics and Automation. 2011; 1: 97-111. 6. Rechy-Ramirez EJ, Hu H. Bi-modal human machine interface for controlling an intelligent wheelchair. IEEE Fourth International Conference on Emerging Security Technologies, 2013 9; 66-70. doi:10.1109/EST.2013.19 7. Costa A, Hortal E, Ianez E, Azorin JM. A supplementary system for a brain–machine interface based on jaw artifacts for the bidimensional control of a robotic arm. PLoS One. 2014; 9:e112352. 8. Linden M, Habib T, Radojevic V. A controlled study of the effects of EEG biofeedback on cognition and behavior of children with attention deficit disorder and learning disabilities. Biofeedback and self-regulation. 1996; 21: 35-49. 9. Azami H, Sanei S, Mohammadi K. A novel signal segmentation method based on standard deviation and variable threshold. Journal of Computer Applications. 2011; 34: 27-34. 10. Specht DF. Probabilistic neural networks. Neural Networks. 1990; 3: 109-118.
There are 1 citations in total.

Details

Primary Language Turkish
Journal Section Original Research
Authors

Muhammet Serdar Başçıl

Publication Date September 16, 2019
Published in Issue Year 2019 Volume: 9 Issue: 3

Cite

APA Başçıl, M. S. (2019). EEG ÜZERİNDE ORTAYA ÇIKAN BEYİN DALGALARININ ÇENE HAREKETLERİ İLE İLİŞKİ The Relationship Between Brain Waves Based on EEG Signals And Jaw Movements. Bozok Tıp Dergisi, 9(3), 45-49.
AMA Başçıl MS. EEG ÜZERİNDE ORTAYA ÇIKAN BEYİN DALGALARININ ÇENE HAREKETLERİ İLE İLİŞKİ The Relationship Between Brain Waves Based on EEG Signals And Jaw Movements. Bozok Tıp Dergisi. September 2019;9(3):45-49.
Chicago Başçıl, Muhammet Serdar. “EEG ÜZERİNDE ORTAYA ÇIKAN BEYİN DALGALARININ ÇENE HAREKETLERİ İLE İLİŞKİ The Relationship Between Brain Waves Based on EEG Signals And Jaw Movements”. Bozok Tıp Dergisi 9, no. 3 (September 2019): 45-49.
EndNote Başçıl MS (September 1, 2019) EEG ÜZERİNDE ORTAYA ÇIKAN BEYİN DALGALARININ ÇENE HAREKETLERİ İLE İLİŞKİ The Relationship Between Brain Waves Based on EEG Signals And Jaw Movements. Bozok Tıp Dergisi 9 3 45–49.
IEEE M. S. Başçıl, “EEG ÜZERİNDE ORTAYA ÇIKAN BEYİN DALGALARININ ÇENE HAREKETLERİ İLE İLİŞKİ The Relationship Between Brain Waves Based on EEG Signals And Jaw Movements”, Bozok Tıp Dergisi, vol. 9, no. 3, pp. 45–49, 2019.
ISNAD Başçıl, Muhammet Serdar. “EEG ÜZERİNDE ORTAYA ÇIKAN BEYİN DALGALARININ ÇENE HAREKETLERİ İLE İLİŞKİ The Relationship Between Brain Waves Based on EEG Signals And Jaw Movements”. Bozok Tıp Dergisi 9/3 (September 2019), 45-49.
JAMA Başçıl MS. EEG ÜZERİNDE ORTAYA ÇIKAN BEYİN DALGALARININ ÇENE HAREKETLERİ İLE İLİŞKİ The Relationship Between Brain Waves Based on EEG Signals And Jaw Movements. Bozok Tıp Dergisi. 2019;9:45–49.
MLA Başçıl, Muhammet Serdar. “EEG ÜZERİNDE ORTAYA ÇIKAN BEYİN DALGALARININ ÇENE HAREKETLERİ İLE İLİŞKİ The Relationship Between Brain Waves Based on EEG Signals And Jaw Movements”. Bozok Tıp Dergisi, vol. 9, no. 3, 2019, pp. 45-49.
Vancouver Başçıl MS. EEG ÜZERİNDE ORTAYA ÇIKAN BEYİN DALGALARININ ÇENE HAREKETLERİ İLE İLİŞKİ The Relationship Between Brain Waves Based on EEG Signals And Jaw Movements. Bozok Tıp Dergisi. 2019;9(3):45-9.
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