P300 ve DHGUP Tabanlı Hibrid BBA Heceleyicisi için Yeni bir Yaklaşım

Öz

Hem P300 hem de durağan hal görsel uyarılmış potansiyel (DHGUP) yüksek sinyal yanıtı ve yüksek sinyal gürültü oranına sahip olduğu için beyin bilgisayar arayüzü (BBA) sistemlerinde yaygın olarak kullanılan elektroensefalografi (EEG) fenomenleridir. Sinyallerdeki sınıflandırma doğruluk oranı ve sinyal tespit süresi değerleri BBA sistemlerinin performansını etkiler. Bu iki değer bir BBA sistemi için anahtar performans göstergesi olan bilgi aktarım hızının (BAH) hesaplanması için kullanılır. Bir P300 tabanlı BBA veya DHGUP tabanlı BBA diğer BBA sistemlerine göre daha yüksek bilgi aktarım hızı değerine sahiptir. Bundan dolayı, bu çalışmadaki amacımız, P300 ve DHGUP fenomenini aynı anda bir BBA heceleyicisinde kullanmaktır. P300 ve DHGUP tabanlı yenir bir hibrid BBA heceleyicisini çalışmamız ile sunuyoruz. Ayrıca önerdiğimiz BBA heceleyecisi sadece P300 uyaranlı veya sadece DHGUP ya da hybrid uyaranlı çalışmayada olanak vermektedir. Bu BBA heceleyicisinde 3 × 3 matris formunda 9 sayı P300 sinyali elde etmek için vardır ve üstelik 9 adet beyaz kare şeklinde yanıp sönen objeler DHGUP elde etmek için sayıların yanına yerleştirilmiştir. Bu araştırmada, deneyler 2 adımda (eğitim ve çevirimçi adımlar) ve 3 oturumda (sadece DHGUP uyaran, sadece P300 uyaran ve hybrid uyaran oturumlar) gerçekleştirilmiştir. Beş farklı kullanıcı deneylere katılmıştır. P300 sinyali ve DHGUP tespiti için destek vektör makinesi metodu kullanılmıştır. Deney sonuçlarına göre ortalama sınıflandırma doğruluk oranı sırasıyla sadece DHGUP uyaran kullanarak %83.78, sadece P300 uyaran kullanarak %84.67 ve hybrid uyaran kullanarak %90.89’ dur. Ortalama bilgi aktarım hızı sırasıyla DHGUP uyaran kullanarak 6.81, sadece P300 uyaran kullanarak 6.97 bit/dk ve hybrid uyaran kullanarak 8.19’ dur. Elde edilen bulgulara göre, önerilen P300 ve DHGUP tabanlı BBA heceleyicisi, sadece DHGUP uyaranlı BBA ya da sadece P300 uyaranı BBA heceleyicilerine göre daha yüksek sınıflandırma doğruluk oranına ve bilgi aktarım hızına ulaşmıştır.

Anahtar Kelimeler

• EEG,Beyin Bilgisayar Arayüzü,Destek Vektör Makinesi,P300,DHGUP

A new Approach for Hybrid BCI speller based on P300 and SSVEP

Abstract

P300 and steady state visual evoked potential (SSVEP) are type of electroencephalography (EEG) phenomena that widely used in brain computer interface (BCI) systems since both of them have high signal response and signal noise ratio. Classification accuracy rate of signal, and signal detection time affect overall performance of BCI systems. These both values are used for calculation information transfer rate (ITR) that is a key performance indicator for a BCI system. A P300 based BCI or a SSVEP based BCI have higher ITR values than other type of BCI systems. Thus, in this study our aim was to use together these both P300 and SSVEP phenomena in a BCI speller. We proposed a hybrid BCI speller based on P300 and SSVEP. Moreover, our proposed BCI speller interface allows to use only P300 stimuli, only SSVEP stimuli, or hybrid stimuli. In this BCI speller, there are numbers in 3 × 3 matrix form for elicitind P300 signal and also 9 white square flickering objects were placed near numbers for eliciting SSVEP. In this research, experiments were performed in two stage (training and online stages) with three sessions (only SSVEP stimuli session, only P300 stimuli session, and hybrid session). Five subjects participated experiments. We used support vector machine method for detection of P300 signal and SSVEP. According to experiment results, average classification accuracy values were 83.78%, 84.67%, and 90.89% with using only SSVEP stimuli, only P300 stimuli, and hybrid stimuli, respectively. Furhermore, average information transfer rate values were 6.81, 6.97, and, 8.19 bit/min with using only SSVEP stimuli, only P300 stimuli, and hybrid stimuli, respectively. Results showed that the proposed hybrid BCI speller based on P300 and SSVEP reached higher classification accuracy and ITR values than using only SSVEP stimuli or only P300 stimuli based BCI spellers.

Keywords

• EEG,Brain Computer Interface,Support Vector Machine,P300,SSVEP

References

1. Beverina, F., Palmas, G., Silvoni, S., Piccione, F., & Giove, S. (2003). User adaptive BCIs: SSVEP and P300 based interfaces. PsychNology Journal, 1(4), 331-354.
2. Belitski, A., Farquhar, J., & Desain, P. (2011). P300 audio-visual speller. Journal of Neural Engineering, 8(2), 025022.
3. Chaudhary, U., Birbaumer, N., & Ramos-Murguialday, A. (2016). Brain–computer interfaces for communication and rehabilitation. Nature Reviews Neurology, 12(9), 513.
4. Edlinger, G., Holzner, C., & Guger, C. (2011, July). A hybrid brain-computer interface for smart home control. In International Conference on Human-Computer Interaction (pp. 417-426). Springer, Berlin, Heidelberg.
5. Farwell, L. A., & Donchin, E. (1988). Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalography and clinical Neurophysiology, 70(6), 510-523.
6. Fazel-Rezai, R., & Ahmad, W. (2011). P300-based brain-computer interface paradigm design. Recent Advances in Brain-Computer Interface Systems, 83-98.
7. Fazel-Rezai, R., Amiri, S., Rabbi, A., & Azinfar, L. (2013). A Review of P300, SSVEP, and Hybrid P300/SSVEP Brain-Computer Interface Systems.
8. Gao, X., Xu, D., Cheng, M., & Gao, S. (2003). A BCI-based environmental controller for the motion-disabled. IEEE Transactions on neural systems and rehabilitation engineering, 11(2), 137-140.

9. Halder, S., Pinegger, A., Käthner, I., Wriessnegger, S. C., Faller, J., Antunes, J. B. P., & Kübler, A. (2015). Brain-controlled applications using dynamic P300 speller matrices. Artificial intelligence in medicine, 63(1), 7-17.
10. Hoffmann, U., Vesin, J. M., Ebrahimi, T., & Diserens, K. (2008). An efficient P300-based brain–computer interface for disabled subjects. Journal of Neuroscience methods, 167(1), 115-125.
11. İşcan, Z., & Nikulin, V. V. (2018). Steady state visual evoked potential (SSVEP) based brain-computer interface (BCI) performance under different perturbations. PloS one, 13(1), e0191673.
12. Kauhanen, L., Nykopp, T., Lehtonen, J., Jylanki, P., Heikkonen, J., Rantanen, P., & Sams, M. (2006). EEG and MEG brain-computer interface for tetraplegic patients. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 14(2), 190-193.
13. Li, Y., Pan, J., Wang, F., & Yu, Z. (2013). A hybrid BCI system combining P300 and SSVEP and its application to wheelchair control. IEEE Transactions on Biomedical Engineering, 60(11), 3156-3166.
14. Marx, E., Benda, M., & Volosyak, I. (2019, October). Optimal Electrode Positions for an SSVEP-based BCI. In 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC) (pp. 2731-2736). IEEE.
15. Oralhan, Z. (2019). 2 Stages-region-based P300 Speller in Brain–Computer Interface. IETE Journal of Research, 65(6), 740-748.
16. Oralhan, Z. (2019). Advanced SSVEP stimulator for brain–computer interface and signal classification with using convolutional neural network. Electronics Letters, 55(25), 1329-1331.
17. Oralhan, Z. (2019). A New Paradigm for Region-Based P300 Speller in Brain Computer Interface. IEEE Access, 7, 106618-106627.
18. Oralhan, Z., & Tokmakçi, M. (2016). The Effect of Duty Cycle and Brightness Variation of Visual Stimuli on SSVEP in Brain Computer Interface Systems. IETE Journal of Research, 62(6), 795-803.
19. Ramadan, R. A., & Vasilakos, A. V. (2017). Brain computer interface: control signals review. Neurocomputing, 223, 26-44.
20. Sutter, E. E. (1992). The brain response interface: communication through visually-induced electrical brain responses. Journal of Microcomputer Applications, 15(1), 31-45.
21. Sutton, S., Braren, M., Zubin, J., & John, E. R. (1965). Evoked-potential correlates of stimulus uncertainty. Science, 150(3700), 1187-1188.
22. Walsh, M. M., Gunzelmann, G., & Anderson, J. R. (2017). Relationship of P3b single-trial latencies and response times in one, two, and three-stimulus oddball tasks. Biological psychology, 123, 47-61.
23. Wang, H., Chang, W., & Zhang, C. (2016). Functional brain network and multichannel analysis for the P300-based brain computer interface system of lying detection. Expert Systems with Applications, 53, 117-128.
24. Wang, M., Daly, I., Allison, B. Z., Jin, J., Zhang, Y., Chen, L., & Wang, X. (2015). A new hybrid BCI paradigm based on P300 and SSVEP. Journal of neuroscience methods, 244, 16-25.
25. Yin, E., Zhou, Z., Jiang, J., Chen, F., Liu, Y., & Hu, D. (2013). A speedy hybrid BCI spelling approach combining P300 and SSVEP. IEEE Transactions on Biomedical Engineering, 61(2), 473-483.
26. Yin, E., Zhou, Z., Jiang, J., Chen, F., Liu, Y., & Hu, D. (2013). A novel hybrid BCI speller based on the incorporation of SSVEP into the P300 paradigm. Journal of neural engineering, 10(2), 026012.