Kişiden Bağımsız Çevrimiçi P300-Tabanlı Beyin-Bilgisayar Arayüzü Sınıflandırma Modeli Oluşturulması

Year 2022, Volume: 27 Issue: 1, 73 - 85, 25.04.2022

Onur Erdem Korkmaz , Önder Aydemir , Emin Argun Oral , Yücel Özbek

https://doi.org/10.53433/yyufbed.1077648

Abstract

Beyin-bilgisayar arayüzleri, elektroensefalografi sinyallerini bilgisayar komutlarına çevirerek insan beyni ile bilgisayar veya harici cihazlar arasında iletişim kurmaya yarayan sistemlerdir. Bu sistemlerin en büyük sınırlamalarından biri, kişiye özgü modelin geliştirilmesinin uzun sürmesi, böylelikle de hasta bireylerin tak-çalıştır konforundan yararlanamamasıdır. Yapılan bu çalışma ile geliştirilen yeni paradigma kullanılarak çevrimdışı oturumda 10 katılımcıdan toplanılan verilerle kişiden bağımsız çalışan sınıflandırma modeli geliştirildi. Öncelikle olay ilişkili potansiyel ve olay ilişkili olmayan potansiyel tespitinin gerçekleştirildiği bu ikili sınıflandırma probleminde 50 kez tekrarlanan sınıflandırma işlemi sonucunda %99.40 ± 0.21 test doğruluğu elde edilmiş ve kişiden bağımsız model olarak kaydedilmiştir. Geliştirilen bu model farklı 30 katılımcının kendilerinin belirlediği kelimelerin yazdırılması şeklinde test edilmiştir. Önerilen kişiden bağımsız bu modelin çevrimiçi karakter tespit etme başarısı ise %95.41 olarak hesaplanmıştır.

Keywords

Beyin-bilgisayar arayüzü, Çevrimiçi, Kişiden bağımsız model, Olay ilişkili potansiyeller, EEG

Supporting Institution

Atatürk Üniversitesi

Project Number

FOA-2018-6524

Thanks

Bu çalışma Atatürk Üniversitesi Bilimsel Araştırma Projeleri Koordinasyon Birimi tarafından FOA-2018-6524 proje numarası ile desteklenmiştir.

Creating an Online Subject Independent P300-Based Brain-Computer Interface Classification Model

Abstract

Brain-computer interfaces convert electroencephalography signals
into computer commands to communicate between the human brain and computer
or external devices. However, one of the most significant limitations of these
systems is that it takes a long time to develop a subject-independent model, so
patients cannot benefit from the plug-and-play comfort. With this study, we
created our data set with the data we collected from 10 people in the offline
session using new paradigm we developed. A brain-computer interface
classification model that works subject-independent was created with this data
set. First of all, in the binary classification problem in which event-related
potential and non-event-related potential detection were performed, 99.40% ±
0.21 test accuracy was obtained due to the classification process being repeated
50 times, and this model was saved. This developed model was tested by printing
the words that 30 different participants determined themselves. This model's
online character detection success, subject independent, was 95.41%

Keywords

Brain-computer interface, Online, Subject-independent model, Event related potentials, EEG

Project Number

FOA-2018-6524

References

• Adobe. (2017). Adobe Inc. Adobe Photoshop for Windows.
• Ahmad, M. M., & Ahuja, K. (2022). Role of 5G Communication Along with Blockchain Security in Brain-Computer Interfacing: A Review. Futuristic Design and Intelligent Computational Techniques in Neuroscience and Neuroengineering. doi: 10.4018/978-1-7998-7433-1.ch004
• Brysbaert, M. (2019). How many words do we read per minute? A review and meta-analysis of reading rate. Journal of Memory and Language, 109, 104047. doi: 10.1016/j.jml.2019.104047
• Devlaminck, D., Wyns, B., Grosse-Wentrup, M., Otte, G., & Santens, P. (2011). Multisubject learning for common spatial patterns in motor-imagery BCI. Computational intelligence and neuroscience, 2011. doi: 10.1155/2011/217987
• Ergün, E., & Aydemir, Ö. (2020). A hybrid BCI using singular value decomposition values of the fast walsh hadamard transform coefficients. IEEE Transactions on Cognitive and Developmental Systems. doi: 10.1109/TCDS.2020.3028785
• Jalilpour, S., Sardouie, S. H., & Mijani, A. (2020). A novel hybrid BCI speller based on RSVP and SSVEP paradigm. Computer methods and programs in biomedicine, 187, 105326. doi: 10.1016/j.cmpb.2020.105326
• Kevric, J., & Subasi, A. (2017). Comparison of signal decomposition methods in classification of EEG signals for motor-imagery BCI system. Biomedical Signal Processing and Control, 31, 398-406. doi: 10.1016/j.bspc.2016.09.007
• Kirasirova, L., Bulanov, V., Ossadtchi, A., Kolsanov, A., Pyatin, V., & Lebedev, M. (2020). A P300 brain-computer interface with a reduced visual field. Frontiers in neuroscience, 14, 1246. doi: 10.3389/fnins.2020.604629
• Kleih, S. C., & Kübler, A. (2013). Empathy, motivation, and P300 BCI performance. Frontiers in human neuroscience, 7, 642. doi: 10.3389/fnhum.2013.00642
• Loizidou, P., Rios, E., Marttini, A., Keluo-Udeke, O., Soetedjo, J., Belay, J., & Speier, W. (2022). Extending brain-computer interface access with a multilingual language model in the P300 speller. Brain-Computer Interfaces, 9(1), doi: 10.1080/2326263X.2021.1993426
• Lu, Z., Li, Q., Gao, N., & Yang, J. (2020). The self-face paradigm improves the performance of the P300-speller system. Frontiers in computational neuroscience, 13, 93. doi: 10.3389/fncom.2019.00093
• Matlab. (2018). MathWorks for Windows.
• Muller-Putz, G. R., & Pfurtscheller, G. (2007). Control of an electrical prosthesis with an SSVEP-based BCI. IEEE Transactions on Biomedical Engineering, 55(1), 361-364. doi: 10.1109/TBME.2007.897815
• Mussabayeva, A., Jamwal, P. K., & Akhtar, M. T. (2021). Ensemble learning approach for subject-independent P300 speller. Paper presented at the 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). doi: 10.1109/EMBC46164.2021.9629679
• Orlandi, A., & Proverbio, A. M. (2019). Left-hemispheric asymmetry for object-based attention: an ERP study. Brain sciences, 9(11), 315. doi: 10.3390/brainsci9110315
• Park, C., Looney, D., ur Rehman, N., Ahrabian, A., & Mandic, D. P. (2012). Classification of motor imagery BCI using multivariate empirical mode decomposition. IEEE transactions on neural systems and rehabilitation engineering, 21(1), 10-22. doi: 10.1109/TNSRE.2012.2229296
• Qu, J., Wang, F., Xia, Z., Yu, T., Xiao, J., Yu, Z., Li, Y. (2018). A novel three-dimensional P300 speller based on stereo visual stimuli. IEEE Transactions on Human-Machine Systems, 48(4), 392-399. doi: 10.1109/THMS.2018.2799525
• Rakotomamonjy, A., & Guigue, V. (2008). BCI competition III: dataset II-ensemble of SVMs for BCI P300 speller. IEEE Transactions on Biomedical Engineering, 55(3), 1147-1154. doi: 10.1109/TBME.2008.915728
• Ramirez-Quintana, J. A., Madrid-Herrera, L., Chacon-Murguia, M. I., & Corral-Martinez, L. F. (2021). Brain-computer interface system based on P300 processing with convolutional neural network, novel speller, and low number of electrodes. Cognitive Computation, 13(1), 108-124. doi: 10.1007/s12559-020-09744-2
• Sellers, E. W., Krusienski, D. J., McFarland, D. J., Vaughan, T. M., & Wolpaw, J. R. (2006). A P300 event-related potential brain–computer interface (BCI): the effects of matrix size and inter stimulus interval on performance. Biological psychology, 73(3), 242-252. doi: 10.1016/j.biopsycho.2006.04.007
• Wolpaw, J. R., Birbaumer, N., Heetderks, W. J., McFarland, D. J., Peckham, P. H., Schalk, G., Vaughan, T. M. (2000). Brain-computer interface technology: a review of the first international meeting. IEEE transactions on rehabilitation engineering, 8(2), 164-173.
• Wu, Y., Zhou, W., Lu, Z., & Li, Q. (2020). A spelling paradigm with an added red dot improved the P300 speller system performance. Frontiers in neuroinformatics, 14, 57. doi: 10.3389/fninf.2020.589169
• Wu, Z., Lai, Y., Xia, Y., Wu, D., & Yao, D. (2008). Stimulator selection in SSVEP-based BCI. Medical engineering & physics, 30(8), 1079-1088. doi: 10.1016/j.medengphy.2008.01.004
• Xu, M., Qi, H., Wan, B., Yin, T., Liu, Z., & Ming, D. (2013). A hybrid BCI speller paradigm combining P300 potential and the SSVEP blocking feature. Journal of neural engineering, 10(2). doi: 10.1088/1741-2560/10/2/026001
• Xu, M., Xiao, X., Wang, Y., Qi, H., Jung, T.-P., & Ming, D. (2018). A brain–computer interface based on miniature-event-related potentials induced by very small lateral visual stimuli. IEEE Transactions on Biomedical Engineering, 65(5), 1166-1175. doi: 10.1109/TBME.2018.2799661
• Zhang, X., Jin, J., Li, S., Wang, X., & Cichocki, A. (2021). Evaluation of color modulation in visual P300-speller using new stimulus patterns. Cognitive Neurodynamics, 1-14. doi: 10.1007/s11571-021-09669-y
• Zhang, Y., Zhang, X., Sun, H., Fan, Z., & Zhong, X. (2019). Portable brain-computer interface based on novel convolutional neural network. Computers in biology and medicine, 107, 248-256. doi: 10.1016/j.compbiomed.2019.02.023