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Farklı performans ölçümleri kullanılarak kararlı durum görsel uyarılmış potansiyel kontrollü quadcopter yolunun değerlendirilmesi

Bu çalışma, kararlı durum görsel uyarılmış potansiyel (SSVEP) tabanlı beyin-bilgisayar arayüz sistemi kullanılarak bir quadcopter sisteminin kontrol edilmesine odaklanmaktadır. Literatürde araştırmacılar doğruluğu ve bilgi aktarım hızını bildirmektedir. Ancak bu ölçümler tahmin edilen ve hedeflenen yol benzerliği hakkında yeterli bilgi sağlamamaktadır. Drone'un kare şeklinde belli bir yol izlemesi ve başlangıç pozisyonuna dönmesi bekleniyor. Çeşitli sınıflandırıcılar kullanarak ek sonuç ölçüleri olarak nihai ve ortalama mesafeleri hesapladık. Sonuçlar, quadcopter kontrolünün performansında dengeli bir karışıklık matrisine sahip olmanın önemini vurguluyor ve quadcopter performansının değerlendirilmesinde daha eksiksiz bir resim sağlıyor. Sınıflandırma doğruluğu ile mekansal sapma arasındaki ilişkiye odaklanmak BCI tabanlı kontrol sistemleri için yeni bir bakış açısı yaratabilir.

Anahtar Kelimeler:

İnsansız Hava Araçları (İHA), Yol Takip, Kontrol, Beyin-Bilgisayar Arayüzü

Evaluation of a steady-state visual evoked potential controlled quadcopter path using different performance measures

This study focuses on controlling a quadcopter system using a steady-state visual evoked potential (SSVEP)-based brain-computer interface system. In the literature, researchers report the accuracy and information transfer rate. However, these measures do not provide sufficient information about the predicted and target path similarity. The drone is expected to follow a certain square-shaped path and return to its starting position. We calculated the final and mean distances as additional outcome measures using several classifiers. The results emphasize the importance of having a balanced confusion matrix in the performance of quadcopter control and provide a more complete picture in the evaluation of the quadcopter’s performance. Focusing on the relationship between classification accuracy and spatial deviation might create a new perspective for BCI-based control systems.

Keywords:

Unmanned Aerial Vehicles (UAV), Path following, Control, Brain-computer interface,

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Ali, N., Neagu, D. and Trundle, P. (2019), “Evaluation of k-nearest neighbour classifier performance for heterogeneous data sets”, SN Applied Sciences, Springer Nature, Vol. 1 No. 12, doi: 10.1007/s42452-019-1356-9.
Bakardjian, H., Tanaka, T. and Cichocki, A. (2010), “Optimization of SSVEP brain responses with application to eight-command Brain-Computer Interface”, Neuroscience Letters, Vol. 469 No. 1, pp. 34–38, doi: 10.1016/j.neulet.2009.11.039.
Bin, G.Y., Gao, X.R., Yan, Z., Hong, B. and Gao, S.K. (2009), “An online multi-channel SSVEP-based brain-computer interface using a canonical correlation analysis method”, J Neural Eng., Vol. 6, doi: 10.1088/1741-2560/6/4/046002.
Bousseta, R., El Ouakouak, I., Gharbi, M. and Regragui, F. (2018), “EEG Based Brain Computer Interface for Controlling a Robot Arm Movement Through Thought”, IRBM, Elsevier Masson SAS, Vol. 39 No. 2, pp. 129–135, doi: 10.1016/j.irbm.2018.02.001.
Cattan, G., Mendoza, C., Andreev, A. and Congedo, M. (2018), “Recommendations for integrating a P300-based brain computer interface in virtual reality environments for gaming”, Computers, doi: 10.3390/computers7020034.
Fabiani, M., Gratton, G., Karis, D. and Donchin, E. (1981), “Definition, Identification, and Reliability of Measurement of the P300 Component of the Event-Related Brain Potential”, Advances in Psychophysiology, Vol. 2 No. 3, pp. 1–78.
Hammer, E.M., Halder, S., Kleih, S.C. and Kübler, A. (2018), “Psychological predictors of visual and auditory P300 Brain-Computer Interface performance”, Frontiers in Neuroscience, Frontiers Media S.A., Vol. 12 No. MAY, doi: 10.3389/fnins.2018.00307.
Herrmann, C.S. (2001), “Human EEG responses to 1-100 Hz flicker: Resonance phenomena in visual cortex and their potential correlation to cognitive phenomena”, Experimental Brain Research, Vol. 137 No. 3–4, doi: 10.1007/s002210100682.
Hotelling, H. (1936), “Relations between two sets of variates”, Biometrika, Vol. 28, doi: 10.1093/biomet/28.3-4.321. Ingel, A., Kuzovkin, I. and Vicente, R. (2019), “Direct information transfer rate optimisation for SSVEP-based BCI”, Journal of Neural Engineering, IOP Publishing, Vol. 16 No. 1, p. 16016, doi: 10.1088/1741-2552/aae8c7.
Işcan, Z. and Nikulin, V.V. (2018), “Steady state visual evoked potential (SSVEP) based brain-computer interface (BCI) performance under different perturbations”, PLoS ONE, Vol. 13 No. 1, doi: 10.1371/journal.pone.0191673.
Johnson, N.N., Carey, J., Edelman, B.J., Doud, A., Grande, A., Lakshminarayan, K. and He, B. (2018), “Combined rTMS and virtual reality brain-computer interface training for motor recovery after stroke”, Journal of Neural Engineering, Institute of Physics Publishing, Vol. 15 No. 1, doi: 10.1088/1741-2552/aa8ce3.
Kotsiantis, S.B. (2013), “Decision trees: A recent overview”, Artificial Intelligence Review, doi: 10.1007/s10462-011-9272-4.
Kumar, S., Mamun, K. and Sharma, A. (2017), “CSP-TSM: Optimizing the performance of Riemannian tangent space mapping using common spatial pattern for MI-BCI”, Computers in Biology and Medicine, Elsevier Ltd, Vol. 91, pp. 231–242, doi: 10.1016/j.compbiomed.2017.10.025.
Mei, J., Xu, M., Wang, L., Ke, Y., Wang, Y., Jung, T.P. and Ming, D. (2020), “Using SSVEP-BCI to Continuous Control a Quadcopter with 4-DOF Motions”, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, Vol. 2020-July, doi: 10.1109/EMBC44109.2020.9176131.
Meriño, L., Nayak, T., Kolar, P., Hall, G., Mao, Z., Pack, D.J. and Huang, Y. (2017), “Asynchronous control of unmanned aerial vehicles using a steady-state visual evoked potential-based brain computer interface”, Brain-Computer Interfaces, Taylor and Francis Ltd., Vol. 4 No. 1–2, pp. 122–135, doi: 10.1080/2326263X.2017.1292721.
Milekovic, T., Sarma, A.A., Bacher, D., Simeral, J.D., Saab, J., Pandarinath, C., Sorice, B.L., et al. (2018), “Stable long-term BCI-enabled communication in ALS and locked-in syndrome using LFP signals”, Journal of Neurophysiology, Vol. 120 No. 1, doi: 10.1152/jn.00493.2017.
Polich, J. (2007), “Updating P300: An integrative theory of P3a and P3b”, Clinical Neurophysiology, October, doi: 10.1016/j.clinph.2007.04.019.
Rosca, S., Leba, M., Ionica, A. and Gamulescu, O. (2018), “Quadcopter control using a BCI”, IOP Conference Series: Materials Science and Engineering, Vol. 294, Institute of Physics Publishing, doi: 10.1088/1757-899X/294/1/012048.
Rutkowski, T.M. (2016), “Robotic and virtual reality BCIs using spatial tactile and auditory oddball paradigms”, Frontiers in Neurorobotics, doi: 10.3389/fnbot.2016.00020.
Saritas, M.M. (2019), “Performance Analysis of ANN and Naive Bayes Classification Algorithm for Data Classification”, International Journal of Intelligent Systems and Applications in Engineering, International Journal of Intelligent Systems and Applications in Engineering, Vol. 7 No. 2, pp. 88–91, doi: 10.18201/ijisae.2019252786.
Shao, L., Zhang, L., Belkacem, A.N., Zhang, Y., Chen, X., Li, J., Liu, H., et al. (2020), “EEG-Controlled Wall-Crawling Cleaning Robot Using SSVEP-Based Brain-Computer Interface”, Journal of Healthcare Engineering, Hindawi Limited, Vol. 2020, doi: 10.1155/2020/6968713.
Speier, W., Arnold, C. and Pouratian, N. (2013), “Evaluating True BCI Communication Rate through Mutual Information and Language Models”, PLoS ONE, Vol. 8 No. 10, doi: 10.1371/journal.pone.0078432.
Speier, W., Arnold, C. and Pouratian, N. (2016), “Integrating language models into classifiers for BCI communication: A review”, Journal of Neural Engineering, doi: 10.1088/1741-2560/13/3/031002.
Strehl, U., Leins, U., Goth, G., Klinger, C., Hinterberger, T. and Birbaumer, N. (2006), “Self-regulation of slow cortical potentials: A new treatment for children with attention-deficit/hyperactivity disorder”, Pediatrics, Vol. 118 No. 5, doi: 10.1542/peds.2005-2478.
Thomas, E., Fruitet, J. and Clerc, M. (2013), “Combining ERD and ERS features to create a system-paced BCI”, Journal of Neuroscience Methods, Vol. 216 No. 2, doi: 10.1016/j.jneumeth.2013.03.026.
Victorio Yasin, T., Pasila, F. and Lim, R. (2018), “A Study of Mobile Robot Control using EEG Emotiv Epoch Sensor”, MATEC Web of Conferences, Vol. 164, EDP Sciences, doi: 10.1051/matecconf/201816401044.
Wang, Y., Chen, X., Gao, X. and Gao, S. (2017), “A Benchmark Dataset for SSVEP-Based Brain-Computer Interfaces”, IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 25 No. 10, doi: 10.1109/TNSRE.2016.2627556.
Wolpaw, J.R., Birbaumer, N., McFarland, D.J., Pfurtscheller, G. and Vaughan, T.M. (2002), “Brain-computer interfaces for communication and control”, Clin. Neurophysiol., Vol. 113, doi: 10.1016/S1388-2457(02)00057-3. Wu, L.W., Liao, H.C., Hu, J.S. and Lo, P.C. (2008), “Brain-controlled robot agent: An EEG-based eRobot agent”, Industrial Robot, Vol. 35 No. 6, pp. 507–519, doi: 10.1108/01439910810909501.