İki ve Üç Boyutlu Paradigmaların Olay İlişkili P300 Potansiyeli Üzerindeki Etkilerinin Dalgacık Dönüşümü Esaslı İncelenmesi

Yıl 2022, Sayı: 41, 258 - 268, 30.11.2022

Onur Erdem Korkmaz

https://doi.org/10.31590/ejosat.1179944

Öz

Olay ilişkili P300 potansiyeli tabanlı Beyin Bilgisayar Arayüzü (BBA) sistemlerinde genellikle farklı uyaran tiplerinin sistem performansını nasıl etkilediği yapılan çalışmaların ana odak noktasıdır. Literatürde yapılan bir çalışmada klasik iki boyutlu satır-sütun ve önerilen üç boyutlu sütun flaşlanması esaslı paradigmalar kullanılarak veri seti oluşturulmuştur. Elde edilen sonuçlara göre önerilen üç boyutlu sütun flaşlanması esaslı uyaran sunumu sınıflandırma doğruluğu açısından yüksek performans göstermektedir. Ancak bu paradigmanın P300 potansiyeli üzerinde nasıl bir değişim meydana getirdiği gösterilmemiştir. Yapılan bu çalışmada dalgacık dönüşümü esaslı bir yöntem kullanılarak her iki paradigmanın olay ilişkili P300 potansiyeli üzerindeki etkisi hem zaman hem de frekans uzayı açısından ele alınmıştır. Elde edilen sonuçlara göre, önerilen paradigmanın P300 potansiyeli üzerinde daha fazla frekans bandında aktivasyon meydana getirdiği görülmüştür. Ayrıca yine önerilen yöntem kullanılarak birçok kanalda daha yüksek P300 genliği elde edilmiştir. Sonuç olarak, önerilen paradigma kullanılarak gerçekleştirilen uyaran sunumunda daha etkin P300 sinyalleri elde edilmekte, buda BBA sistem performansını artırmaktadır.

Anahtar Kelimeler

Dalgacık dönüşümü, P300, BBA, EEG

Destekleyen Kurum

Atatürk Üniversitesi

Proje Numarası

FOA-2018-6524

Investigation of the Effects of Two and Three Dimensional Paradigms on Event-Related Potentials Using Wavelet Transform Based Method

Öz

In event-related P300 potential-based Brain Computer Interface (BCI) systems, the main focus of the studies is how different stimulus types affect system performance. In a study, a data set was created using classical two-dimensional row-column flashing-based and proposed three-dimensional column flashing-based paradigms. According to the results obtained, the proposed three-dimensional column flashing shows high performance in terms of classification accuracy of stimulus presentation. However, how this paradigm changes the P300 potential has not been demonstrated. In this study, the effect of both paradigms on the event-related P300 potential is discussed using a wavelet transform-based method in terms of both time and frequency space. According to the results obtained, it was observed that the proposed paradigm activated more frequency bands on the P300 potential. In addition, using the proposed method, higher P300 amplitude was obtained in many channels. As a result, more effective P300 signals are received in stimulus presentation using the proposed paradigm, increasing the BCI system performance.

Anahtar Kelimeler

Wavelet transform, P300, BCI, EEG

Proje Numarası

FOA-2018-6524

Kaynakça

• Adeli, H., Zhou, Z., & Dadmehr, N. (2003). Analysis of EEG records in an epileptic patient using wavelet transform. Journal of neuroscience methods, 123(1), 69-87.
• Aggarwal, S., & Chugh, N. (2022). Review of machine learning techniques for EEG based brain computer interface. Archives of Computational Methods in Engineering, 1-20.
• 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, 65-85.
• Arpaia, P., Esposito, A., Natalizio, A., & Parvis, M. (2022). How to successfully classify EEG in motor imagery BCI: a metrological analysis of the state of the art. Journal of Neural Engineering.
• Arvaneh, M., Robertson, I. H., & Ward, T. E. (2019). A p300-based brain-computer interface for improving attention. Frontiers in human neuroscience, 12, 524.
• Aydemir, O., & Kayikcioglu, T. (2011). Wavelet transform based classification of invasive brain computer interface data. Radioengineering, 20(1), 31-38.
• Cao, L., Li, G., Xu, Y., Zhang, H., Shu, X., & Zhang, D. (2021). A brain-actuated robotic arm system using non-invasive hybrid brain–computer interface and shared control strategy. Journal of Neural Engineering, 18(4), 046045.
• 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.
• 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.
• Janapati, R., Dalal, V., & Sengupta, R. (2022). Advances in Experimental Paradigms for EEG-BCI. Paper presented at the Proceedings of the 2nd International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications.
• 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.
• Kim, K. H., Kim, J. H., Yoon, J., & Jung, K.-Y. (2008). Influence of task difficulty on the features of event-related potential during visual oddball task. Neuroscience letters, 445(2), 179-183.
• Korkmaz, O. E., Aydemir, O., Oral, E. A., & Ozbek, I. Y. (2022). An efficient 3D column-only P300 speller paradigm utilizing few numbers of electrodes and flashings for practical BCI implementation. PloS one, 17(4), e0265904.
• Li, M., Li, F., Pan, J., Zhang, D., Zhao, S., Li, J., & Wang, F. (2021). The mindgomoku: An online P300 BCI game based on Bayesian deep learning. Sensors, 21(5), 1613.
• 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.
• McFarland, D. J., Krusienski, D. J., Sarnacki, W. A., & Wolpaw, J. R. (2008). Emulation of computer mouse control with a noninvasive brain–computer interface. Journal of Neural Engineering, 5(2), 101.
• 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.
• Obeidat, Q. T., Campbell, T. A., & Kong, J. (2015). Introducing the edges paradigm: a P300 brain–computer interface for spelling written words. IEEE Transactions on Human-Machine Systems, 45(6), 727-738.
• Palumbo, A., Gramigna, V., Calabrese, B., & Ielpo, N. (2021). Motor-imagery EEG-based BCIs in wheelchair movement and control: A systematic literature review. Sensors, 21(18), 6285.
• Polich, J. (1987). Task difficulty, probability, and inter-stimulus interval as determinants of P300 from auditory stimuli. Electroencephalography and Clinical Neurophysiology/Evoked Potentials Section, 68(4), 311-320.
• 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.
• 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.
• 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.
• 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.
• Stephe, S., & Kumar, K. V. (2022). Motor Imagery EEG Recognition using Deep Generative Adversarial Network with EMD for BCI Applications. Tehnički vjesnik, 29(1), 92-100.
• Won, K., Kwon, M., Ahn, M., & Jun, S. C. (2022). EEG Dataset for RSVP and P300 Speller Brain-Computer Interfaces. Scientific Data, 9(1), 1-11.
• 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, 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.
• 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), 026001.
• Yin, E., Zhou, Z., Jiang, J., Yu, Y., & Hu, D. (2014). A dynamically optimized SSVEP brain–computer interface (BCI) speller. IEEE Transactions on biomedical engineering, 62(6), 1447-1456.
• 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, 15(5), 873-886.