Research Article
BibTex RIS Cite

Comparison of ADD and GSY Methods with Machine Learning Algorithms Using EOG Artifacts in EEG Data

Year 2025, Volume: 25 Issue: 6, 1491 - 1510

Sefa Aydın , Mesut Melek , Levent Gökrem

Abstract

Today, Brain-computer interface (BCI) systems have been developed to meet the daily needs of individuals who are completely paralyzed due to nervous system problems such as congenital or spinal cord injury. BCIs are systems that produce output commands to activate peripheral devices by processing the brain signals collected using various devices through computers. However, BCI systems also have some negative effects on system users. In this study, a Human-Machine Interface (HMI) system that uses the moving objects approach is proposed in order to reduce the negative effects of visual stimuli vibrating at different frequency values used in Visual Evoked Potential (VEP) and P300-based BCI systems on the eye health of system users. The system aims to classify movement trajectories with machine learning algorithms by using Electrooculography (EOG) artifacts that occur in Electrocephalography (EEG) signals as a result of the user focusing on moving balls. The study was conducted on 8 healthy subjects using an approach involving balls moving in four different routes through the Emotiv EPOC EEG device. Pre-processing stages were applied to the recorded raw data and effective channel selection was made. Power Spectral Density (PSD) and Discrete Wavelet Transform (DWT) methods were applied to the data of the detected active channels (AF3, AF4). Features were extracted for each method and classified using k-Nearest Neighbor (k-NN) and Linear Discriminant Analysis (LDA) algorithms. For the PSD method, 93.86%, 92.62% accuracy rates and 31.42 (bit/min), 30.10 (bit/min) ITR values were calculated with the LDA and k-NN algorithms, respectively. For the DWT method, accuracy rates of 92.84%, 92.17% and ITR values of 30.36 (bit/min), 29.62 (bit/min) were obtained, respectively. It has been observed that, among the two compared methods, PSD gives higher accuracy rates and ITR values than DWT, and the LDA algorithm is more successful than the k-NN algorithm.

Keywords

Brain-computer interface Electroencephalography Electrooculography Machine learning Classification

Project Number

Tokat Gaziosmanpaşa Üniversitesi Bilimsel Araştırma Projesi (2023/86)

References

  • Akram, F., Han, S. M. and Kim, T. S., 2015. An efficient word typing P300-BCI system using a modified T9 interface and random forest classifier. Computers in biology and medicine, 56, 30-36. https://doi.org/10.1016/J.COMPBIOMED.2014.10.021
  • Allison, B., 2009. The I of BCIs: Next Generation Interfaces for Brain–Computer Interface Systems That Adapt to Individual Users. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5611 LNCS(PART 2), 558-568. https://doi.org/10.1007/978-3-642-02577-8_61
  • Altan, G. and İnat, G., 2021. EEG based Spatial Attention Shifts Detection using Time-Frequency features on Empirical Wavelet Transform. Akıllı Sistemler ve Uygulamaları Dergisi, 4(2), 144–149. https://doi.org/10.54856/JISWA.202112181
  • Altan, G., Yayık, A., and Kutlu, Y. 2021. Deep Learning with ConvNet Predicts Imagery Tasks Through EEG. Neural Processing Letters, 53(4), 2917–2932. https://doi.org/10.1007/S11063-021-10533-7
  • Avcu, M. T., Zhang, Z. and Shih Chan, D. W., 2019. Seizure Detection Using Least Eeg Channels by Deep Convolutional Neural Network. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 1120-1124. https://doi.org/10.1109/ICASSP.2019.8683229
  • Aydin, S.; Melek, M.; Gökrem, L. A Safe and Efficient Brain–Computer Interface Using Moving Object Trajectories and LED-Controlled Activation. Micromachines 2025, 16, 340. https://doi.org/10.3390/mi16030340
  • Bin, G., Gao, X., Yan, Z., Hong, B. and Gao, S., 2009. An online multi-channel SSVEP-based brain-computer interface using a canonical correlation analysis method. Journal of neural engineering, 6(4), 046002. https://doi.org/10.1088/1741-2560/6/4/046002
  • Brennan, C. P., McCullagh, P. J., Galway, L. and Lightbody, G., 2015. Promoting autonomy in a smart home environment with a smarter interface. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2015-November, 5032-5035. https://doi.org/10.1109/EMBC.2015.7319522
  • Deng, T., Teng, Z., Takahashi, S., Yi, K., Qiu, W. and Zhong, H., 2025. HCI systems: Real-time Detection and Interaction based on EOG and IOG. IEEE Transactions on Instrumentation and Measurement., 79, 4002614. https://doi.org/10.1109/TIM.2025.3542109
  • Efe, E. and Ozsen, S., 2023. CoSleepNet: Automated sleep staging using a hybrid CNN-LSTM network on imbalanced EEG-EOG datasets. Biomedical Signal Processing and Control, 80, 104299. https://doi.org/10.1016/J.BSPC.2022.104299
  • Fatourechi, M., Bashashati, A., Ward, R. K. and Birch, G. E., 2007. EMG and EOG artifacts in brain computer interface systems: A survey. Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology, 118(3), 480-494. https://doi.org/10.1016/J.CLINPH.2006.10.019
  • Faust, O., Acharya, U. R., Adeli, H. and Adeli, A., 2015. Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis. Seizure, 26, 56-64. https://doi.org/10.1016/J.SEIZURE.2015.01.012
  • Glavas, K., Tzimourta, K. D., Tzallas, A. T., Giannakeas, N. and Tsipouras, M. G., 2024. Empowering Individuals With Disabilities: A 4-DoF BCI Wheelchair Using MI and EOG Signals. IEEE Access, 12, 95417-95433. https://doi.org/10.1109/ACCESS.2024.3424953
  • Goktas, M. S. and Aras, S., 2022. Two Channel EOG Circuit Design and Implementation for Human Computer Interface. 2022 30th Signal Processing and Communications Applications Conference, SIU 2022, 1-4. https://doi.org/10.1109/SIU55565.2022.9864907
  • Hacıbeyoglu, M., Arıcı. N. F., Karaaltun M., 2024. Intrusion Detection System Application with Machine Learning. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi, 24(5), 1165–1179. https://doi.org/10.35414/AKUFEMUBID.1455995
  • Hernández Pérez, S. N., Pérez Reynoso, F. D., Gutiérrez, C. A. G., Cosío León, M. D. los Á. and Ortega Palacios, R., 2023. EOG Signal Classification with Wavelet and Supervised Learning Algorithms KNN, SVM and DT. Sensors 2023, 23(9), 4553. https://doi.org/10.3390/S23094553
  • Holewa, K. and Nawrocka, A., 2014. Emotiv EPOC neuroheadset in brain-Computer interface. Proceedings of the 2014 15th International Carpathian Control Conference, ICCC 2014, 149-152. https://doi.org/10.1109/CARPATHIANCC.2014.6843587
  • Hu, L., Zhu, J., Chen, S., Zhou, Y., Song, Z. and Li, Y., 2024. A Wearable Asynchronous Brain-Computer Interface Based on EEG-EOG Signals With Fewer Channels. IEEE transactions on bio-medical engineering, 71(2), 504-513. https://doi.org/10.1109/TBME.2023.3308371
  • Jiang, Y., Li, K., Liang, Y., Chen, D., Tan, M. and Li, Y., 2025. Daily Assistance for Amyotrophic Lateral Sclerosis Patients Based on a Wearable Multimodal Brain-Computer Interface Mouse. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 33, 150-161 https://doi.org/10.1109/TNSRE.2024.3520984
  • Kabir, S. A., Farhan, F., Siddiquee, A. A., Baroi, O. L., Marium, T. and Rahimi, J., 2023. Effect of Input Channel Reduction on EEG Seizure Detection. Przeglad Elektrotechniczny, 12, 195-200. https://doi.org/10.15199/48.2023.12.35
  • Kamińska, D., Smółka, K., and Zwoliński, G., 2021. Detection of Mental Stress through EEG Signal in Virtual Reality Environment. Electronics , 10(22), 2840. https://doi.org/10.3390/ELECTRONICS10222840
  • Kaya, E. and Saritas, I., 2024. Identifying optimal channels and features for multi-participant motor imagery experiments across a participant’s multi-day multi-class EEG data. Cognitive Neurodynamics, 18(3), 987-1003. https://doi.org/10.1007/S11571-023-09957-9
  • Kondo, S. and Tanaka, H., 2023. High-frequency SSVEP–BCI with less flickering sensation using personalization of stimulus frequency. Artificial Life and Robotics, 28(4), 803-811. https://doi.org/10.1007/S10015-023-00893-9
  • Kosmyna, N. and Lécuyer, A., 2019. A conceptual space for EEG-based brain-computer interfaces. PLOS ONE, 14(1), e0210145. https://doi.org/10.1371/JOURNAL.PONE.0210145
  • Krusienski, D. J., Sellers, E. W., Cabestaing, F., Bayoudh, S., McFarland, D. J., Vaughan, T. M. and Wolpaw, J. R., 2006. A comparison of classification techniques for the P300 Speller. Journal of neural engineering, 3(4), 299. https://doi.org/10.1088/1741-2560/3/4/007
  • Kübler, A., Kotchoubey, B., Kaiser, J., Birbaumer, N. and Wolpaw, J. R., 2001. Brain-computer communication: unlocking the locked in. Psychological bulletin, 127(3), 358-375. https://doi.org/10.1037/0033-2909.127.3.358
  • Li, L., Weinberg, C. R., Darden, T. A. and Pedersen, L. G., 2002. Gene selection for sample classification based on gene expression data: Study of sensitivity to choice of parameters of the GA/KNN method. Bioinformatics, 17(12), 1131-1142. https://doi.org/10.1093/BIOINFORMATICS/17.12.1131
  • Liu, X., Hu, B., Si, Y. and Wang, Q., 2024. The role of eye movement signals in non-invasive brain-computer interface typing system. Medical and biological engineering & computing, 62(7), 1981-1990. https://doi.org/10.1007/S11517-024-03070-7
  • Liu, Y., Jiang, X., Cao, T., Wan, F., Mak, P. U., Mak, P. I. and Vai, M. I., 2012. Implementation of SSVEP based BCI with Emotiv EPOC. Proceedings of IEEE International Conference on Virtual Environments, Human-Computer Interfaces, and Measurement Systems, VECIMS, 34-37. https://doi.org/10.1109/VECIMS.2012.6273184
  • López, A., Villar, J. R., Fernández, M. and Ferrero, F. J., 2023. Comparison of classification techniques for the control of EOG-based HCIs. Biomedical Signal Processing and Control, 80, 104263. https://doi.org/10.1016/J.BSPC.2022.104263
  • Lotte, F. and Guan, C., 2011. Regularizing common spatial patterns to improve BCI designs: Unified theory and new algorithms. IEEE Transactions on Biomedical Engineering, 58(2), 355-362. https://doi.org/10.1109/TBME.2010.2082539
  • Maciej Serda, Becker, F. G., Cleary, M., Team, R. M., Holtermann, H., The, D., Agenda, N., Science, P., Sk, S. K., Hinnebusch, R., Hinnebusch A, R., Rabinovich, I., Olmert, Y., Uld, D. Q. G. L. Q., Ri, W. K. H. U., Lq, V., Frxqwu, W. K. H., Zklfk, E., Edvhg, L. V, 2020. Neuromarketing approach: An overview and future research directions. Journal of Theoretical and Applied Information Technology, 98(7), 343-354. https://doi.org/10.2/JQUERY.MIN.JS
  • Mai, X., Ai, J., Ji, M., Zhu, X. and Meng, J., 2024. A hybrid BCI combining SSVEP and EOG and its application for continuous wheelchair control. Biomedical Signal Processing and Control, 88, 105530. https://doi.org/10.1016/J.BSPC.2023.105530
  • Martínez-Cerveró, J., Ardali, M. K., Jaramillo-Gonzalez, A., Wu, S., Tonin, A., Birbaumer, N. and Chaudhary, U., 2020. Open Software/Hardware Platform for Human-Computer Interface Based on Electrooculography (EOG) Signal Classification. Sensors, 20(9), 2443. https://doi.org/10.3390/S20092443
  • Melek, M., Manshouri, N. and Kayikcioglu, T., 2020. Low-cost brain-computer interface using the emotiv epoc headset based on rotating vanes. Traitement du Signal, 37(5), 831-837. https://doi.org/10.18280/TS.370516
  • Mifsud, M., Camilleri, T. A. and Camilleri, K. P., 2024. A distance robust EOG-based feature for gaze trajectory inference. Biomedical Signal Processing and Control, 90, 105852. https://doi.org/10.1016/J.BSPC.2023.105852
  • Mitra, P. and Bokil, H., 2007. Observed Brain Dynamics. Observed Brain Dynamics, Oxford University Press, 1-404. https://doi.org/10.1093/ACPROF:OSO/9780195178081.001.0001
  • Mouli, S. and Palaniappan, R., 2020. DIY hybrid SSVEP-P300 LED stimuli for BCI platform using EMOTIV EEG headset. HardwareX, 8, e00113. https://doi.org/10.1016/J.OHX.2020.E00113
  • Mwata-Velu, T., Ruiz-Pinales, J., Rostro-Gonzalez, H., Ibarra-Manzano, M. A., Cruz-Duarte, J. M. and Avina-Cervantes, J. G., 2021. Motor imagery classification based on a recurrent-convolutional architecture to control a hexapod robot. Mathematics, 9(6), 606. https://doi.org/10.3390/MATH9060606
  • Raj, A. and Kumar, A., 2024. Developing an EOG-Based Communication Interface for Quadriplegic Patients: Prototype, Signal Processing, and Algorithm Design. International Journal of Advance Research, Ideas and Innovations in Technology, 10(6), 258-264. https://doi.org/xx.xxx/ijariit-v10i6-1342
  • Sanei, S., and Chambers, J. A., 2013. EEG Signal Processing. EEG Signal Processing, John Wiley & Sons Ltd. https://doi.org/10.1002/9780470511923
  • Sarhan, S. M., Al-Faiz, M. Z. and Takhakh, A. M., 2024. EEG-Based Control of a 3D-Printed Upper Limb Exoskeleton for Stroke Rehabilitation. International journal of online and biomedical engineering, 20(9), 99-112. https://doi.org/10.3991/IJOE.V20I09.48475
  • Shah, V., Golmohammadi, M., Ziyabari, S., Von Weltin, E., Obeid, I. and Picone, J., 2017. Optimizing channel selection for seizure detection. 2017 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2017-Proceedings, 2018-January, 1-5. https://doi.org/10.1109/SPMB.2017.8257019
  • Sörnmo, L., and Laguna, P., 2005. Bioelectrical Signal Processing in Cardiac and Neurological Applications. Bioelectrical Signal Processing in Cardiac and Neurological Applications, Academic Press. https://doi.org/10.1016/B978-0-12-437552-9.X5000-4
  • Tiwari, S., Goel, S., and Bhardwaj, A., 2022. MIDNN- a classification approach for the EEG based motor imagery tasks using deep neural network. Applied Intelligence, 52(5), 4824-4843. https://doi.org/10.1007/S10489-021-02622-W
  • Top, A. E., Yeniad, M., Özdoğan, M. S., ve Nar, F., 2024 DAC: Differentiable Auto-Cropping in Deep Learning. Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, 24(6), 1382–1394. https://doi.org/10.35414/AKUFEMUBID.1475807
  • Volosyak, I., 2011. SSVEP-based Bremen-BCI interface--boosting information transfer rates. Journal of neural engineering, 8(3), 036020. https://doi.org/10.1088/1741-2560/8/3/036020
  • Welch, P. D., 1967. The Use of Fast Fourier Transform for the Estimation of Power Spectra: A Method Based on Time Averaging Over Short, Modified Periodograms. IEEE Transactions on Audio and Electroacoustics, 15(2), 70-73. https://doi.org/10.1109/TAU.1967.1161901
  • Wolpaw, J. R., Ramoser, H., McFarland, D. J., and Pfurtscheller, G., 1998. EEG-based communication: improved accuracy by response verification. IEEE transactions on rehabilitation engineering: a publication of the IEEE Engineering in Medicine and Biology Society, 6(3), 326-333. https://doi.org/10.1109/86.712231
  • Zhang, J., Gao, S., Zhou, K., Cheng, Y., and Mao, S., 2023. An online hybrid BCI combining SSVEP and EOG-based eye movements. Frontiers in Human Neuroscience, 17, 1103935. https://doi.org/10.3389/FNHUM.2023.1103935
  • Zhang, Z., 2016. Introduction to machine learning: k-nearest neighbors. Annals of Translational Medicine, 4(11), 218. https://doi.org/10.21037/ATM.2016.03.37
  • Zhao, Y., 2025. Multivariate emotional AI model for enhancing students’ ideological education and mental health via brain-computer interfaces and biomechanics. Molecular and Cellular Biomechanics, 22(3), 1049-1049. https://doi.org/10.62617/MCB1049
  • Zhu, Y., Li, Y., Lu, J. and Li, P., 2020. A Hybrid BCI Based on SSVEP and EOG for Robotic Arm Control. Frontiers in Neurorobotics, 14, 583641. https://doi.org/10.3389/FNBOT.2020.583641

EOG Artefaktları Kullanılarak Yörünge Tabanlı Sınıflandırma: Felçli Bireyler İçin Alternatif Bir İnsan-Makine Arayüz Yaklaşımı

Year 2025, Volume: 25 Issue: 6, 1491 - 1510

Sefa Aydın , Mesut Melek , Levent Gökrem

Abstract

Günümüzde, doğuştan veya omurilik yaralanması gibi sinir sistemi problemleri sebebiyle tamamen felç olmuş bireylerin günlük ihtiyaçlarını karşılayabilmeleri için Beyin-bilgisayar arayüz (BBA) sistemleri geliştirilmiştir. BBAlar, çeşitli cihazları kullanarak topladığı beyin sinyallerini bilgisayarlar aracılığıyla işleyerek, çevre cihazları harekete geçirmek için çıktı komutları üreten sistemlerdir. Fakat BBA sistemlerinin kullanıcılar üzerinde bazı olumsuz etkileri vardır. Bu çalışmada, Görsel uyaran potansiyel (GUP) ve P300 gibi görsel uyaran tabanlı BBA sistemlerinde kullanılan farklı frekans değerlerinde titreşen uyaranların sistem kullanıcılarının göz sağlığı üzerinde meydana getirdiği olumsuz etkiyi azaltmak amacıyla; hareketli nesneler yaklaşımını kullanan bir İnsan-Makine Arayüz (İMA) sistemi önerilmiştir. Sistem, kullanıcının hareketli topları odaklanarak takip etmesi sonucunda Elektrosefalografi (EEG) sinyallerinde meydana gelen Elektrookülografi (EOG) artefaktlarını kullanarak hareket yörüngelerini makine öğrenme algoritmaları ile sınıflandırmayı amaçlamaktadır. Çalışma, Emotiv EPOC EEG cihazı aracılığıyla dört farklı rotada hareket eden toplar içeren yaklaşım kullanılarak sağlıklı 8 denek üzerinde gerçekleştirilmiştir. Kaydedilen ham verilere ön işleme aşamaları uygulanmıştır ve etkin kanal seçimi yapılmıştır. Tespit edilen etkin kanallara (AF3, AF4) ait verilere Güç Spektral Yoğunluğu (GSY) ve Ayrık Dalgacık Dönüşümü (ADD) yöntemleri uygulanmıştır. Her yöntem için öznitelikler çıkartılmış ve k-en yakın komşu (k-EYK) ve Lineer Diskriminant Analiz (LDA) algoritmaları kullanılarak sınıflandırılmıştır. GSY yöntemi için LDA ve k-EYK algoritmaları ile sırasıyla 93.86%, 92.62% doğruluk oranları ve 31.42 (bit/dk), 30.10 (bit/dk) ITR değerleri hesaplanmıştır. ADD yöntemi için ise sırasıyla 92.84%, 92.17% doğruluk oranları ve 30.36 (bit/dk), 29.62 (bit/dk) ITR değerleri elde edilmiştir. Karşılaştırılan iki yöntemden GSY’nin ADD’ye göre daha yüksek doğruluk oranları ve ITR değerleri verdiği, LDA algoritmasının k-EYK algoritmasından daha başarılı olduğu gözlemlenmiştir.

Keywords

Beyin-bilgisayar Arayüz Elektrosefalografi Elektrookülografi Makine öğrenme Sınıflandırma

Ethical Statement

Yazarlar bu çalışmada herhangi bir çıkar çatışması olmadığını beyan ederler.

Supporting Institution

Tokat Gaziosmanpaşa Üniversitesi

Project Number

Tokat Gaziosmanpaşa Üniversitesi Bilimsel Araştırma Projesi (2023/86)

Thanks

Bu makalenin inceleme ve değerlendirme aşamasında yapmış oldukları katkılardan dolayı editör ve hakemlere teşekkür ederiz.

References

  • Akram, F., Han, S. M. and Kim, T. S., 2015. An efficient word typing P300-BCI system using a modified T9 interface and random forest classifier. Computers in biology and medicine, 56, 30-36. https://doi.org/10.1016/J.COMPBIOMED.2014.10.021
  • Allison, B., 2009. The I of BCIs: Next Generation Interfaces for Brain–Computer Interface Systems That Adapt to Individual Users. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5611 LNCS(PART 2), 558-568. https://doi.org/10.1007/978-3-642-02577-8_61
  • Altan, G. and İnat, G., 2021. EEG based Spatial Attention Shifts Detection using Time-Frequency features on Empirical Wavelet Transform. Akıllı Sistemler ve Uygulamaları Dergisi, 4(2), 144–149. https://doi.org/10.54856/JISWA.202112181
  • Altan, G., Yayık, A., and Kutlu, Y. 2021. Deep Learning with ConvNet Predicts Imagery Tasks Through EEG. Neural Processing Letters, 53(4), 2917–2932. https://doi.org/10.1007/S11063-021-10533-7
  • Avcu, M. T., Zhang, Z. and Shih Chan, D. W., 2019. Seizure Detection Using Least Eeg Channels by Deep Convolutional Neural Network. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 1120-1124. https://doi.org/10.1109/ICASSP.2019.8683229
  • Aydin, S.; Melek, M.; Gökrem, L. A Safe and Efficient Brain–Computer Interface Using Moving Object Trajectories and LED-Controlled Activation. Micromachines 2025, 16, 340. https://doi.org/10.3390/mi16030340
  • Bin, G., Gao, X., Yan, Z., Hong, B. and Gao, S., 2009. An online multi-channel SSVEP-based brain-computer interface using a canonical correlation analysis method. Journal of neural engineering, 6(4), 046002. https://doi.org/10.1088/1741-2560/6/4/046002
  • Brennan, C. P., McCullagh, P. J., Galway, L. and Lightbody, G., 2015. Promoting autonomy in a smart home environment with a smarter interface. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2015-November, 5032-5035. https://doi.org/10.1109/EMBC.2015.7319522
  • Deng, T., Teng, Z., Takahashi, S., Yi, K., Qiu, W. and Zhong, H., 2025. HCI systems: Real-time Detection and Interaction based on EOG and IOG. IEEE Transactions on Instrumentation and Measurement., 79, 4002614. https://doi.org/10.1109/TIM.2025.3542109
  • Efe, E. and Ozsen, S., 2023. CoSleepNet: Automated sleep staging using a hybrid CNN-LSTM network on imbalanced EEG-EOG datasets. Biomedical Signal Processing and Control, 80, 104299. https://doi.org/10.1016/J.BSPC.2022.104299
  • Fatourechi, M., Bashashati, A., Ward, R. K. and Birch, G. E., 2007. EMG and EOG artifacts in brain computer interface systems: A survey. Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology, 118(3), 480-494. https://doi.org/10.1016/J.CLINPH.2006.10.019
  • Faust, O., Acharya, U. R., Adeli, H. and Adeli, A., 2015. Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis. Seizure, 26, 56-64. https://doi.org/10.1016/J.SEIZURE.2015.01.012
  • Glavas, K., Tzimourta, K. D., Tzallas, A. T., Giannakeas, N. and Tsipouras, M. G., 2024. Empowering Individuals With Disabilities: A 4-DoF BCI Wheelchair Using MI and EOG Signals. IEEE Access, 12, 95417-95433. https://doi.org/10.1109/ACCESS.2024.3424953
  • Goktas, M. S. and Aras, S., 2022. Two Channel EOG Circuit Design and Implementation for Human Computer Interface. 2022 30th Signal Processing and Communications Applications Conference, SIU 2022, 1-4. https://doi.org/10.1109/SIU55565.2022.9864907
  • Hacıbeyoglu, M., Arıcı. N. F., Karaaltun M., 2024. Intrusion Detection System Application with Machine Learning. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi, 24(5), 1165–1179. https://doi.org/10.35414/AKUFEMUBID.1455995
  • Hernández Pérez, S. N., Pérez Reynoso, F. D., Gutiérrez, C. A. G., Cosío León, M. D. los Á. and Ortega Palacios, R., 2023. EOG Signal Classification with Wavelet and Supervised Learning Algorithms KNN, SVM and DT. Sensors 2023, 23(9), 4553. https://doi.org/10.3390/S23094553
  • Holewa, K. and Nawrocka, A., 2014. Emotiv EPOC neuroheadset in brain-Computer interface. Proceedings of the 2014 15th International Carpathian Control Conference, ICCC 2014, 149-152. https://doi.org/10.1109/CARPATHIANCC.2014.6843587
  • Hu, L., Zhu, J., Chen, S., Zhou, Y., Song, Z. and Li, Y., 2024. A Wearable Asynchronous Brain-Computer Interface Based on EEG-EOG Signals With Fewer Channels. IEEE transactions on bio-medical engineering, 71(2), 504-513. https://doi.org/10.1109/TBME.2023.3308371
  • Jiang, Y., Li, K., Liang, Y., Chen, D., Tan, M. and Li, Y., 2025. Daily Assistance for Amyotrophic Lateral Sclerosis Patients Based on a Wearable Multimodal Brain-Computer Interface Mouse. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 33, 150-161 https://doi.org/10.1109/TNSRE.2024.3520984
  • Kabir, S. A., Farhan, F., Siddiquee, A. A., Baroi, O. L., Marium, T. and Rahimi, J., 2023. Effect of Input Channel Reduction on EEG Seizure Detection. Przeglad Elektrotechniczny, 12, 195-200. https://doi.org/10.15199/48.2023.12.35
  • Kamińska, D., Smółka, K., and Zwoliński, G., 2021. Detection of Mental Stress through EEG Signal in Virtual Reality Environment. Electronics , 10(22), 2840. https://doi.org/10.3390/ELECTRONICS10222840
  • Kaya, E. and Saritas, I., 2024. Identifying optimal channels and features for multi-participant motor imagery experiments across a participant’s multi-day multi-class EEG data. Cognitive Neurodynamics, 18(3), 987-1003. https://doi.org/10.1007/S11571-023-09957-9
  • Kondo, S. and Tanaka, H., 2023. High-frequency SSVEP–BCI with less flickering sensation using personalization of stimulus frequency. Artificial Life and Robotics, 28(4), 803-811. https://doi.org/10.1007/S10015-023-00893-9
  • Kosmyna, N. and Lécuyer, A., 2019. A conceptual space for EEG-based brain-computer interfaces. PLOS ONE, 14(1), e0210145. https://doi.org/10.1371/JOURNAL.PONE.0210145
  • Krusienski, D. J., Sellers, E. W., Cabestaing, F., Bayoudh, S., McFarland, D. J., Vaughan, T. M. and Wolpaw, J. R., 2006. A comparison of classification techniques for the P300 Speller. Journal of neural engineering, 3(4), 299. https://doi.org/10.1088/1741-2560/3/4/007
  • Kübler, A., Kotchoubey, B., Kaiser, J., Birbaumer, N. and Wolpaw, J. R., 2001. Brain-computer communication: unlocking the locked in. Psychological bulletin, 127(3), 358-375. https://doi.org/10.1037/0033-2909.127.3.358
  • Li, L., Weinberg, C. R., Darden, T. A. and Pedersen, L. G., 2002. Gene selection for sample classification based on gene expression data: Study of sensitivity to choice of parameters of the GA/KNN method. Bioinformatics, 17(12), 1131-1142. https://doi.org/10.1093/BIOINFORMATICS/17.12.1131
  • Liu, X., Hu, B., Si, Y. and Wang, Q., 2024. The role of eye movement signals in non-invasive brain-computer interface typing system. Medical and biological engineering & computing, 62(7), 1981-1990. https://doi.org/10.1007/S11517-024-03070-7
  • Liu, Y., Jiang, X., Cao, T., Wan, F., Mak, P. U., Mak, P. I. and Vai, M. I., 2012. Implementation of SSVEP based BCI with Emotiv EPOC. Proceedings of IEEE International Conference on Virtual Environments, Human-Computer Interfaces, and Measurement Systems, VECIMS, 34-37. https://doi.org/10.1109/VECIMS.2012.6273184
  • López, A., Villar, J. R., Fernández, M. and Ferrero, F. J., 2023. Comparison of classification techniques for the control of EOG-based HCIs. Biomedical Signal Processing and Control, 80, 104263. https://doi.org/10.1016/J.BSPC.2022.104263
  • Lotte, F. and Guan, C., 2011. Regularizing common spatial patterns to improve BCI designs: Unified theory and new algorithms. IEEE Transactions on Biomedical Engineering, 58(2), 355-362. https://doi.org/10.1109/TBME.2010.2082539
  • Maciej Serda, Becker, F. G., Cleary, M., Team, R. M., Holtermann, H., The, D., Agenda, N., Science, P., Sk, S. K., Hinnebusch, R., Hinnebusch A, R., Rabinovich, I., Olmert, Y., Uld, D. Q. G. L. Q., Ri, W. K. H. U., Lq, V., Frxqwu, W. K. H., Zklfk, E., Edvhg, L. V, 2020. Neuromarketing approach: An overview and future research directions. Journal of Theoretical and Applied Information Technology, 98(7), 343-354. https://doi.org/10.2/JQUERY.MIN.JS
  • Mai, X., Ai, J., Ji, M., Zhu, X. and Meng, J., 2024. A hybrid BCI combining SSVEP and EOG and its application for continuous wheelchair control. Biomedical Signal Processing and Control, 88, 105530. https://doi.org/10.1016/J.BSPC.2023.105530
  • Martínez-Cerveró, J., Ardali, M. K., Jaramillo-Gonzalez, A., Wu, S., Tonin, A., Birbaumer, N. and Chaudhary, U., 2020. Open Software/Hardware Platform for Human-Computer Interface Based on Electrooculography (EOG) Signal Classification. Sensors, 20(9), 2443. https://doi.org/10.3390/S20092443
  • Melek, M., Manshouri, N. and Kayikcioglu, T., 2020. Low-cost brain-computer interface using the emotiv epoc headset based on rotating vanes. Traitement du Signal, 37(5), 831-837. https://doi.org/10.18280/TS.370516
  • Mifsud, M., Camilleri, T. A. and Camilleri, K. P., 2024. A distance robust EOG-based feature for gaze trajectory inference. Biomedical Signal Processing and Control, 90, 105852. https://doi.org/10.1016/J.BSPC.2023.105852
  • Mitra, P. and Bokil, H., 2007. Observed Brain Dynamics. Observed Brain Dynamics, Oxford University Press, 1-404. https://doi.org/10.1093/ACPROF:OSO/9780195178081.001.0001
  • Mouli, S. and Palaniappan, R., 2020. DIY hybrid SSVEP-P300 LED stimuli for BCI platform using EMOTIV EEG headset. HardwareX, 8, e00113. https://doi.org/10.1016/J.OHX.2020.E00113
  • Mwata-Velu, T., Ruiz-Pinales, J., Rostro-Gonzalez, H., Ibarra-Manzano, M. A., Cruz-Duarte, J. M. and Avina-Cervantes, J. G., 2021. Motor imagery classification based on a recurrent-convolutional architecture to control a hexapod robot. Mathematics, 9(6), 606. https://doi.org/10.3390/MATH9060606
  • Raj, A. and Kumar, A., 2024. Developing an EOG-Based Communication Interface for Quadriplegic Patients: Prototype, Signal Processing, and Algorithm Design. International Journal of Advance Research, Ideas and Innovations in Technology, 10(6), 258-264. https://doi.org/xx.xxx/ijariit-v10i6-1342
  • Sanei, S., and Chambers, J. A., 2013. EEG Signal Processing. EEG Signal Processing, John Wiley & Sons Ltd. https://doi.org/10.1002/9780470511923
  • Sarhan, S. M., Al-Faiz, M. Z. and Takhakh, A. M., 2024. EEG-Based Control of a 3D-Printed Upper Limb Exoskeleton for Stroke Rehabilitation. International journal of online and biomedical engineering, 20(9), 99-112. https://doi.org/10.3991/IJOE.V20I09.48475
  • Shah, V., Golmohammadi, M., Ziyabari, S., Von Weltin, E., Obeid, I. and Picone, J., 2017. Optimizing channel selection for seizure detection. 2017 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2017-Proceedings, 2018-January, 1-5. https://doi.org/10.1109/SPMB.2017.8257019
  • Sörnmo, L., and Laguna, P., 2005. Bioelectrical Signal Processing in Cardiac and Neurological Applications. Bioelectrical Signal Processing in Cardiac and Neurological Applications, Academic Press. https://doi.org/10.1016/B978-0-12-437552-9.X5000-4
  • Tiwari, S., Goel, S., and Bhardwaj, A., 2022. MIDNN- a classification approach for the EEG based motor imagery tasks using deep neural network. Applied Intelligence, 52(5), 4824-4843. https://doi.org/10.1007/S10489-021-02622-W
  • Top, A. E., Yeniad, M., Özdoğan, M. S., ve Nar, F., 2024 DAC: Differentiable Auto-Cropping in Deep Learning. Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, 24(6), 1382–1394. https://doi.org/10.35414/AKUFEMUBID.1475807
  • Volosyak, I., 2011. SSVEP-based Bremen-BCI interface--boosting information transfer rates. Journal of neural engineering, 8(3), 036020. https://doi.org/10.1088/1741-2560/8/3/036020
  • Welch, P. D., 1967. The Use of Fast Fourier Transform for the Estimation of Power Spectra: A Method Based on Time Averaging Over Short, Modified Periodograms. IEEE Transactions on Audio and Electroacoustics, 15(2), 70-73. https://doi.org/10.1109/TAU.1967.1161901
  • Wolpaw, J. R., Ramoser, H., McFarland, D. J., and Pfurtscheller, G., 1998. EEG-based communication: improved accuracy by response verification. IEEE transactions on rehabilitation engineering: a publication of the IEEE Engineering in Medicine and Biology Society, 6(3), 326-333. https://doi.org/10.1109/86.712231
  • Zhang, J., Gao, S., Zhou, K., Cheng, Y., and Mao, S., 2023. An online hybrid BCI combining SSVEP and EOG-based eye movements. Frontiers in Human Neuroscience, 17, 1103935. https://doi.org/10.3389/FNHUM.2023.1103935
  • Zhang, Z., 2016. Introduction to machine learning: k-nearest neighbors. Annals of Translational Medicine, 4(11), 218. https://doi.org/10.21037/ATM.2016.03.37
  • Zhao, Y., 2025. Multivariate emotional AI model for enhancing students’ ideological education and mental health via brain-computer interfaces and biomechanics. Molecular and Cellular Biomechanics, 22(3), 1049-1049. https://doi.org/10.62617/MCB1049
  • Zhu, Y., Li, Y., Lu, J. and Li, P., 2020. A Hybrid BCI Based on SSVEP and EOG for Robotic Arm Control. Frontiers in Neurorobotics, 14, 583641. https://doi.org/10.3389/FNBOT.2020.583641
There are 53 citations in total.

Details

Primary Language Turkish
Subjects Biomedical Engineering (Other)
Journal Section Articles
Authors

Sefa Aydın 0000-0001-9965-2903

Mesut Melek 0000-0002-7152-7788

Levent Gökrem 0000-0003-2101-5378

Project Number Tokat Gaziosmanpaşa Üniversitesi Bilimsel Araştırma Projesi (2023/86)
Early Pub Date November 13, 2025
Publication Date November 15, 2025
Submission Date February 20, 2025
Acceptance Date May 28, 2025
Published in Issue Year 2025 Volume: 25 Issue: 6

Cite

APA Aydın, S., Melek, M., & Gökrem, L. (2025). EOG Artefaktları Kullanılarak Yörünge Tabanlı Sınıflandırma: Felçli Bireyler İçin Alternatif Bir İnsan-Makine Arayüz Yaklaşımı. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi, 25(6), 1491-1510.
AMA Aydın S, Melek M, Gökrem L. EOG Artefaktları Kullanılarak Yörünge Tabanlı Sınıflandırma: Felçli Bireyler İçin Alternatif Bir İnsan-Makine Arayüz Yaklaşımı. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi. November 2025;25(6):1491-1510.
Chicago Aydın, Sefa, Mesut Melek, and Levent Gökrem. “EOG Artefaktları Kullanılarak Yörünge Tabanlı Sınıflandırma: Felçli Bireyler İçin Alternatif Bir İnsan-Makine Arayüz Yaklaşımı”. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi 25, no. 6 (November 2025): 1491-1510.
EndNote Aydın S, Melek M, Gökrem L (November 1, 2025) EOG Artefaktları Kullanılarak Yörünge Tabanlı Sınıflandırma: Felçli Bireyler İçin Alternatif Bir İnsan-Makine Arayüz Yaklaşımı. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi 25 6 1491–1510.
IEEE S. Aydın, M. Melek, and L. Gökrem, “EOG Artefaktları Kullanılarak Yörünge Tabanlı Sınıflandırma: Felçli Bireyler İçin Alternatif Bir İnsan-Makine Arayüz Yaklaşımı”, Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi, vol. 25, no. 6, pp. 1491–1510, 2025.
ISNAD Aydın, Sefa et al. “EOG Artefaktları Kullanılarak Yörünge Tabanlı Sınıflandırma: Felçli Bireyler İçin Alternatif Bir İnsan-Makine Arayüz Yaklaşımı”. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi 25/6 (November2025), 1491-1510.
JAMA Aydın S, Melek M, Gökrem L. EOG Artefaktları Kullanılarak Yörünge Tabanlı Sınıflandırma: Felçli Bireyler İçin Alternatif Bir İnsan-Makine Arayüz Yaklaşımı. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi. 2025;25:1491–1510.
MLA Aydın, Sefa et al. “EOG Artefaktları Kullanılarak Yörünge Tabanlı Sınıflandırma: Felçli Bireyler İçin Alternatif Bir İnsan-Makine Arayüz Yaklaşımı”. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi, vol. 25, no. 6, 2025, pp. 1491-10.
Vancouver Aydın S, Melek M, Gökrem L. EOG Artefaktları Kullanılarak Yörünge Tabanlı Sınıflandırma: Felçli Bireyler İçin Alternatif Bir İnsan-Makine Arayüz Yaklaşımı. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi. 2025;25(6):1491-510.
 Download Cover Image


This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.