Çoktan Seçmeli Sorularda Kararlı Durum Görsel Olarak Uyarılmış Potansiyel Tabanlı Beyin Bilgisayar Arayüzü Kullanılması

Yıl 2023, Cilt: 6 Sayı: 2, 61 - 69, 30.01.2024

Elif Yıldırım , Fatih Aydın , Oğuzhan Başer Önder Aydemir

Öz

Önerildiği ilk yıllarda daha çok felçli hastalara yönelik olan beyin bilgisayar arayüzü (BBA) teknolojisine olan ilgi akıllı ev ve araba uygulamalarından oyun sektörüne uzanan geniş bir yelpazede gün geçtikçe artmaktadır. Bu çalışmada kişilerin sadece düşünceleri ile çoktan seçmeli sorulara yanıt vermelerini olanaklı hale getiren bir kararlı durum görsel olarak uyarılmış potansiyel tabanlı bir BBA modeli geliştirilmiştir. Bu model için literatürde kullanılan ticari elektroensefalogram (EEG) cihazlar yerine Texas Instrument firmasının ADS1299 çipi ile bir EEG ölçüm sistemi tasarlanmıştır. Hem önerilen uygulama bakımından hem de geliştirilen EEG ölçüm sistemi açısından yenilikçi olan bu çalışma; engelli statüsünde sınav hakkı olan bireylerin katıldıkları sınavlarda sadece düşünceleri ile sorulara cevap verme olanağı sağlamış olmaktadır.

Anahtar Kelimeler

Beyin Bilgisayar Arayüzü, EEG, Kararlı Durum Görsel Olarak Uyarılmış Potansiyel

Proje Numarası

1139B412202088

Using Steady State Visually Evoked Potential-Based Brain Computer Interface in Multiple Choice Questions

Öz

Interest in brain computer interface (BCI) technology, which was mostly aimed at paralyzed patients in the first years it was first proposed, is increasing day by day in a wide range of areas ranging from smart home and car applications to the gaming industry. In this study, a steady-state visually evoked potential- based BCI model was developed that enables people to answer multiple-choice questions with only using their thoughts. For this model, an EEG measurement system has been designed with Texas Instrument's ADS1299 chip instead of the commercial electroencephalogram (EEG) devices used in the literature. This study, which is innovative both in terms of the proposed application and in terms of the developed EEG measurement system, provides the opportunity for individuals who have the right to take exams with disability status to answer questions only with their thoughts in the exams they participate in.

Anahtar Kelimeler

Brain Computer Interface, EEG, Steady State Visually Evoked Potential

Proje Numarası

1139B412202088

Kaynakça

• [1] Kawala-Sterniuk, A., ve diğerleri. (2021). Summary of over Fifty Years with Brain-Computer Interfaces—A Review. Brain Sciences, 11(1), 43.
• [2] McFarland, D. J., & Wolpaw, J. R. (2017). EEG-based brain–computer interfaces. Current Opinion in Biomedical Engineering, 4, 194-200.
• [3] Zabcikova, M., Koudelkova, Z., Jasek, R., & Lorenzo Navarro, J. J. (2022). Recent advances and current trends in brain-computer interface research and their applications. International Journal of Developmental Neuroscience, 82(2), 107-123.
• [4] Vaughan, T. M. (2020). Brain-computer interfaces for people with amyotrophic lateral sclerosis. In Handbook of Clinical Neurology, Vol. 168, 33-38.
• [5] Cortez, S. A., Flores, C., & Andreu-Perez, J. (2021). A Smart Home Control Prototype Using a P300-Based Brain–Computer Interface for Post-stroke Patients. In Y. Iano, R. Arthur, O. Saotome, G. Kemper, & A. C. Borges Monteiro (Eds.), Proceedings of the 5th Brazilian Technology Symposium (s. 202). Springer, Cham.
• [6] Amiri, S., Fazel-Rezai, R., & Asadpour, V. (2013). A review of hybrid brain-computer interface systems. Advances in Human-Computer Interaction, 2013, 1-1.
• [7] S. Kundu and S. Ari, "MsCNN: A Deep Learning Framework for P300-Based Brain–Computer Interface Speller," in IEEE Transactions on Medical Robotics and Bionics, vol. 2, no. 1, pp. 86-93, Feb. 2020, doi: 10.1109/TMRB.2019.2959559.
• [8] Zhu, D., Bieger, J., Garcia Molina, G., & Aarts, R. M. (2010). A survey of stimulation methods used in SSVEP-based BCIs. Computational intelligence and neuroscience, 2010.
• [9] Yin, E., Zhou, Z., Jiang, J., Yu, Y., & Hu, D. (2015). A Dynamically Optimized SSVEP Brain–Computer Interface (BCI) Speller. IEEE Transactions on Biomedical Engineering, 62(6), 1447-1456.
• [10] Chen, X., Chen, Z., Gao, S., & Gao, X. (2014). A high-ITR SSVEP-based BCI speller. Brain-Computer Interfaces, 1(3-4), 181-191.
• [11] Albawardi, H., Almoaibed, A., Al Abbas, N., Alsayed, S., Almaghlouth, T., & Alzahrani, S. (2021, December). Design of Low-Cost Steady State Visually Evoked Potential-Based Brain Computer Interface Using OpenBCI and Neuromore. In 2021 4th International Conference on Bio-Engineering for Smart Technologies (BioSMART) (pp. 1-4). IEEE.
• [12] Müller-Putz, G. R., & Pfurtscheller, G. (2008). Control of an electrical prosthesis with an SSVEP-based BCI. IEEE Transactions on Biomedical Engineering, 55(1), 361-364.
• [13] Lalor, E., Kelly, S., Finucane, C., Burke, R., Reilly, R., & Mcdarby, G. (2004). Brain computer interface based on the steady-state VEP for immersive gaming control. Biomed. Tech., 49, 63.
• [14] Jiang, L., Guan, C., Zhang, H., Wang, C., & Jiang, B. (2011). Brain computer interface based 3D game for attention training and rehabilitation. In 2011 6th IEEE Conference on Industrial Electronics and Applications (s. 124-127).
• [15] Shao, L., Zhang, L., Belkacem, A. N., Zhang, Y., Chen, X., Li, J., & Liu, H. (2020). EEG-Controlled Wall-Crawling Cleaning Robot Using SSVEP-Based Brain-Computer Interface. Journal of Healthcare Engineering, vol. 2020, Article ID 6968713, 11 pages.
• [16] Marin, I., Al-Battbooti, M. J. H., & Goga, N. (2020). Drone Control based on Mental Commands and Facial Expressions. In 2020 12th International Conference on Electronics, Computers and Artificial Intelligence (ECAI) (s. 1-4).
• [17] Coogan, C. G., & He, B. (2018). Brain-Computer Interface Control in a Virtual Reality Environment and Applications for the Internet of Things. IEEE Access, 6, 10840-10849.
• [18] Cutter, P. A. (2015). The Shape of Things to Come: The Military Benefits of the Brain-Computer Interface in 2040 Air Command and Staff College.
• [19] Wang, Z., Yu, Y., Xu, M., Liu, Y., Yin, E., & Zhou, Z. (2018). Towards a Hybrid BCI Gaming Paradigm Based on Motor Imagery and SSVEP. International Journal of Human–Computer Interaction, 35(3), 197-205.
• [20] Allison, B. Z., Jin, J., Zhang, Y., & Wang, X. (2014). A four-choice hybrid P300/SSVEP BCI for improved accuracy. Brain-Computer Interfaces, 1(1), 17-26.
• [21] Zhou, C. (2023). SSVEP-Based BCI Wheelchair Control System. arXiv preprint arXiv:2307.08703.
• [22] Albahri, A. S., Al-Qaysi, Z. T., Alzubaidi, L., Alnoor, A., Albahri, O. S., Alamoodi, A. H., & Bakar, A. A. (2023). A systematic review of using deep learning technology in the steady-state visually evoked potential-based brain-computer interface applications: current trends and future trust methodology. International Journal of Telemedicine and Applications, 2023.