Electrooculography-Based Voluntary Eye-Blink Detection for Arabic Assistive Communication

Afrah Thamer; Mussab Alaziz

doi:10.35882/jeeemi.v8i4.1848

Afrah Thamer Department of Computer Engineering, College of Engineering, University of Basrah, Basrah, Iraq https://orcid.org/0009-0009-2071-2607
Mussab Alaziz Department of Computer Engineering, College of Engineering, University of Basrah, Basrah, Iraq https://orcid.org/0000-0002-7302-0657

DOI: https://doi.org/10.35882/jeeemi.v8i4.1848

Keywords: Electrooculography (EOG), Biomedical Signal Processing, Adaptive Thresholding, Single/Double Blink, Binary Encoding, Arabic Assistive Vocabulary

Abstract

People with profound neuromotor disabilities commonly experience significant speech disability and lack of voluntary control over their limbs, retaining, however, the ability to perform deliberate eyelid blinks. Such retained capability presents an opportunity to be used as assistive communication. Nevertheless, reliable detection of voluntary blinks and decoding of communication patterns based on them is challenged by variation in the waveforms, amplitudes, durations, and inter-blink intervals of the voluntary eye blinks. In this work, a cost-efficient vertical electrooculography (EOG) approach for detecting voluntary eye blinks and decoding predefined assistive messages in Arabic was presented and assessed. The pipeline consists of signal conditioning, root-mean-square envelope calculation, adaptive hysteresis-based thresholding, temporal segmentation, and rule-based interpretation of single- and double-blink patterns. Quiet-background threshold updating and locking it during the communication window period were used to increase the robustness of the pattern detection process, while autocorrelation analysis and temporal constraints were utilized to interpret patterns. A single blink is associated with binary 0, and a double blink with binary 1; four consecutive pattern positions form a 4-bit frame that represents one of sixteen predefined Arabic messages. Unclear patterns are excluded from processing and not mapped into any valid message. The proposed approach was evaluated offline on 4,000 word-level recordings from 25 healthy participants, with verification using videos. The F1-score of 95.69%, macro-F1-score of 98.30%, and message-decoding accuracy of 92.98% were obtained. These findings demonstrate the possibility of implementing an interpretable and cost-efficient vertical-EOG approach for message-level Arabic assistive communication without morse-code-type encoding and letter-by-letter spelling. Direct mapping of patterns into messages can help reduce communication effort and time. Nevertheless, since the present evaluation was conducted offline and included only healthy subjects, further validation with target users and real-time implementation should be conducted before deployment.

Downloads

Download data is not yet available.

Author Biography

Mussab Alaziz, Department of Computer Engineering, College of Engineering, University of Basrah, Basrah, Iraq

Musaab Alaziz Holds a PhD in degree in Electrical and Computer Engineering from Rutgers University, the state university of New Jersey, NJ, USA in 2017. MSc, BSc in Computer Engineering from University of Basrah, Basrah, Iraq. 2005,2002 respectively. He is Currently a faculty member at University of Basrah, College of Engineering and Head of the Computer Engineering Department at the same college. Research interests: unobtrusive monitoring and neurobehavioral assessment and computational modeling. Currently he have research about smart rooms that can track people's activities using sensors with smart systems.

References

Y. Elsahar, S. Hu, K. Bouazza-Marouf, D. Kerr, and A. Mansor, “Augmentative and alternative communication (AAC) advances: A review of configurations for individuals with a speech disability,” Sensors, vol. 19, no. 8, Art. no. 1911, 2019, doi: 10.3390/s19081911.

F. Gonçalves, C. S. Fernandes, M. I. Teixeira, C. Melo, and C. Dias, “Bridging silence: A scoping review of technological advancements in augmentative and alternative communication for amyotrophic lateral sclerosis,” Sclerosis, vol. 4, no. 1, Art. no. 2, 2026, doi: 10.3390/sclerosis4010002.

A. Tonin, A. Jaramillo-Gonzalez, A. Rana, M. Khalili-Ardali, N. Birbaumer, and U. Chaudhary, “Auditory electrooculogram-based communication system for ALS patients in transition from locked-in to complete locked-in state,” Scientific Reports, vol. 10, Art. no. 8452, 2020, doi: 10.1038/s41598-020-65333-1.

G. Chiarion, F. Ponzio, S. Terna, C. Moglia, E. Patti, S. Di Cataldo, and S. Roatta, “e-Pupil: IoT-based augmentative and alternative communication device exploiting the pupillary near-reflex,” IEEE Access, vol. 10, pp. 130078–130088, 2022, doi: 10.1109/ACCESS.2022.3229143.

R. Tamai, T. Saitoh, K. Itoh, and H. Zhang, “Eyeglass-type switch: A wearable eye-movement and blink switch for ALS nurse call,” Electronics, vol. 14, no. 21, Art. no. 4201, 2025, doi: 10.3390/electronics14214201.

E. Cardillo, L. Ferro, G. Sapienza, and C. Li, “Reliable eye-blinking detection with millimeter-wave radar glasses,” IEEE Transactions on Microwave Theory and Techniques, vol. 72, no. 1, pp. 771–779, Jan. 2024, doi: 10.1109/TMTT.2023.3329707.

M. Ezzat, M. Maged, Y. Gamal, M. Adel, M. Alrahmawy, and S. El-Metwally, “Blink-To-Live eye-based communication system for users with speech impairments,” Scientific Reports, vol. 13, Art. no. 7961, 2023, doi: 10.1038/s41598-023-34310-9.

M. Thilagaraj, B. Dwarakanath, S. Ramkumar, K. Karthikeyan, A. Prabhu, G. Saravanakumar, M. Pallikonda Rajasekaran, and N. Arunkumar, “Eye movement signal classification for developing human-computer interface using electrooculogram,” Journal of Healthcare Engineering, vol. 2021, Art. no. 7901310, 2021, doi: 10.1155/2021/7901310.

G. Ekim, N. Ikizler, and A. Atasoy, “EEG-based communication system by using artificial neural networks,” in 2023 Medical Technologies Congress (TIPTEKNO), 2023, pp. 1–4, doi: 10.1109/TIPTEKNO59875.2023.10359177.

M. Rabbani, N. U. S. Sabith, A. Parida, I. Iqbal, S. M. Mamun, R. A. Khan, F. Ahmed, and S. I. Ahamed, “EEG-based real-time classification of consecutive two eye blinks for brain-computer interface applications,” Scientific Reports, vol. 15, Art. no. 21007, 2025, doi: 10.1038/s41598-025-07205-0.

C. Belkhiria, A. Boudir, C. Hurter, and V. Peysakhovich, “EOG-based human–computer interface: 2000–2020 review,” Sensors, vol. 22, no. 13, Art. no. 4914, 2022, doi: 10.3390/s22134914.

W.-D. Chang, “Electrooculograms for human–computer interaction: A review,” Sensors, vol. 19, no. 12, Art. no. 2690, 2019, doi: 10.3390/s19122690.

L. Tao, H. Huang, C. Chen, L. Feijs, J. Hu, and W. Chen, “Review of electrooculography-based human-computer interaction: Recent technologies, challenges and future trends,” Connected Health and Telemedicine, vol. 2, no. 3, Art. no. 2000010, 2023, doi: 10.20517/chatmed.2023.05.

H. W. Son, T. M. Lee, S. H. Kim, and H. J. Baek, “1D convolutional neural network-based hierarchical classification of eye movements using noncontact electrooculography,” IEEE Access, vol. 13, pp. 78182–78193, 2025, doi: 10.1109/ACCESS.2025.3566142.

C.-T. Lin, W.-L. Jiang, S.-F. Chen, K.-C. Huang, and L.-D. Liao, “Design of a wearable eye-movement detection system based on electrooculography signals and its experimental validation,” Biosensors, vol. 11, no. 9, Art. no. 343, 2021, doi: 10.3390/bios11090343.

S. Ban, Y. J. Lee, K. R. Kim, J.-H. Kim, and W.-H. Yeo, “Advances in materials, sensors, and integrated systems for monitoring eye movements,” Biosensors, vol. 12, no. 11, Art. no. 1039, 2022, doi: 10.3390/bios12111039.

M. Liu, S. Bian, Z. Zhao, B. Zhou, and P. Lukowicz, “Energy-efficient, low-latency, and non-contact eye blink detection with capacitive sensing,” Frontiers in Computer Science, vol. 6, Art. no. 1394397, 2024, doi: 10.3389/fcomp.2024.1394397.

M. Nyström, R. Andersson, D. C. Niehorster, R. S. Hessels, and I. T. C. Hooge, “What is a blink? Classifying and characterizing blinks in eye openness signals,” Behavior Research Methods, vol. 56, no. 4, pp. 3280–3299, 2024, doi: 10.3758/s13428-023-02333-9.

A. M. D. E. Hassanein, A. G. M. A. Mohamed, and M. A. H. M. Abdullah, “Classifying blinking and winking EOG signals using statistical analysis and LSTM algorithm,” Journal of Electrical Systems and Information Technology, vol. 10, Art. no. 44, 2023, doi: 10.1186/s43067-023-00112-2.

D. Das, M. H. Chowdhury, A. Chowdhury, K. Hasan, Q. D. Hossain, and R. C. C. Cheung, “Application specific reconfigurable processor for eyeblink detection from dual-channel EOG signal,” Journal of Low Power Electronics and Applications, vol. 13, no. 4, Art. no. 61, 2023, doi: 10.3390/jlpea13040061.

J. Xiong, W. Dai, Q. Wang, X. Dong, B. Ye, and J. Yang, “A review of deep learning in blink detection,” PeerJ Computer Science, vol. 11, Art. no. e2594, 2025, doi: 10.7717/peerj-cs.2594.

K. Suzuki, A. S. M. Miah, and J. Shin, “Deep learning-based eye-writing recognition with improved preprocessing and data augmentation techniques,” Sensors, vol. 25, no. 20, Art. no. 6325, 2025, doi: 10.3390/s25206325.

N. Zendehdel, K. G. Zadeh, H. Chen, Y. S. Song, and M. C. Leu, “Hands-free UAV control: Real-time eye movement detection using EOG and LSTM networks,” IEEE Access, vol. 13, pp. 101852–101868, 2025, doi: 10.1109/ACCESS.2025.3578558.

P. Liu, S. Puthusserypady, I. S. MacKenzie, C. Uyanik, and J. P. Hansen, “EarEOG: Using headphones and around-the-ear EOG signals for real-time wheelchair control,” Proceedings of the ACM on Human-Computer Interaction, vol. 9, no. 3, Art. no. ETRA08, pp. 1–16, 2025, doi: 10.1145/3725833.

A. G. A. Abdel-Samei, A. S. Shaaban, A. M. Brisha, F. E. Abd El-Samie, and A. S. Ali, “EOG acquisition system based on ATmega AVR microcontroller,” Journal of Ambient Intelligence and Humanized Computing, vol. 14, no. 12, pp. 16589–16605, 2023, doi: 10.1007/s12652-023-04622-9.

X. Ding and Z. Lv, “Design and development of an EOG-based simplified Chinese eye-writing system,” Biomedical Signal Processing and Control, vol. 57, Art. no. 101767, 2020, doi: 10.1016/j.bspc.2019.101767.

A. M. Choudhari, P. Porwal, V. Jonnalagedda, and F. Mériaudeau, “An electrooculography-based human–machine interface for wheelchair control,” Biocybernetics and Biomedical Engineering, vol. 39, no. 3, pp. 673–685, 2019, doi: 10.1016/j.bbe.2019.04.002.

S. N. Hernández Pérez, F. D. Pérez Reynoso, C. A. González Gutiérrez, M. de los Á. Cosío León, and R. Ortega Palacios, “EOG signal classification with wavelet and supervised learning algorithms KNN, SVM and DT,” Sensors, vol. 23, no. 9, Art. no. 4553, 2023, doi: 10.3390/s23094553.

A. J. Molina-Cantero, C. Lebrato-Vázquez, M. Merino-Monge, R. Quesada-Tabares, J. A. Castro-García, and I. M. Gómez-González, “Communication technologies based on voluntary blinks: Assessment and design,” IEEE Access, vol. 7, pp. 70770–70798, 2019, doi: 10.1109/ACCESS.2019.2919324.

N. Tarek, M. A. Mandour, N. El-Madah, R. Ali, S. Yahia, B. Mohamed, D. Mostafa, and S. El-Metwally, “Morse glasses: An IoT communication system based on Morse code for users with speech impairments,” Computing, vol. 104, no. 4, pp. 789–808, 2022, doi: 10.1007/s00607-021-00959-1.

G. Ekim, N. Ikizler, and A. Atasoy, “A study on eye-blink detection-based communication system by using K-nearest neighbors classifier,” Advances in Electrical and Computer Engineering, vol. 23, no. 1, pp. 71–78, 2023, doi: 10.4316/AECE.2023.01008.

N. Ikizler, G. Ekim, and A. Atasoy, “A novel approach on converting eye blink signals in EEG to speech with cross correlation technique,” Advances in Electrical and Computer Engineering, vol. 23, no. 2, pp. 29–38, 2023, doi: 10.4316/AECE.2023.02004.

I. Käthner, A. Kübler, and S. Halder, “Comparison of eye tracking, electrooculography and an auditory brain-computer interface for binary communication: A case study with a participant in the locked-in state,” Journal of NeuroEngineering and Rehabilitation, vol. 12, Art. no. 76, 2015, doi: 10.1186/s12984-015-0071-z.

K. S. Moon, S. Q. Lee, J. S. Kang, A. Hnat, and D. B. Karen, “A wireless electrooculogram (EOG) wearable using conductive fiber electrode,” Electronics, vol. 12, no. 3, Art. no. 571, 2023, doi: 10.3390/electronics12030571.

H. N. Jo, S. W. Park, H. G. Choi, S. H. Han, and T. S. Kim, “Development of an electrooculogram (EOG) and surface electromyogram (sEMG)-based human computer interface (HCI) using a bone conduction headphone integrated bio-signal acquisition system,” Electronics, vol. 11, no. 16, Art. no. 2561, 2022, doi: 10.3390/electronics11162561.

H. Liu, H. Wei, G. Yang, C. Xia, and S. Zhao, “An improved ViBe algorithm based on adaptive thresholding and the deep learning-driven frame difference method,” Electronics, vol. 12, no. 16, Art. no. 3481, 2023, doi: 10.3390/electronics12163481.

B. Voloh, M. R. Watson, S. König, and T. Womelsdorf, “MAD saccade: Statistically robust saccade threshold estimation via the median absolute deviation,” Journal of Eye Movement Research, vol. 12, no. 8, Art. no. 3, 2019, doi: 10.16910/jemr.12.8.3.

Z. Yang, L. Hou, and X. Zhao, “Robust autocorrelation for period detection in time series,” in Proceedings of the 17th International Conference on Agents and Artificial Intelligence (ICAART), Volume 2, Porto, Portugal, 2025, pp.