RESEARCH OF ELECTRO-OCULOGRAM (EOG) CONTROLLED MOUSE CURSOR
Keywords:
Artificial Neural Network, Bio-Signal Acquisition, Electro-oculogram, Electromyogram, Human-Computer Interface, Mouse Control
Abstract
In this article a Human Computer Interface (HCI) based on electro-oculogram (EOG) measurements will be presented. EOG domain is focusing on the human eye’s movements. The signals are recorded using Ag/AgCl electrodes and fed into an analog-to-digital converter (ADC) and are processed by a computer or laptop. In our application the EOG signal processing program is running on a PC. Only 3 recording channels and electrodes were used in our setup.
After processing and filtering, the program was able to give different commands based on the recorded EOG signals. The program used Artificial Neural Network (ANN) toolbox of MATLAB®. The proposed HCI can be used by healthy or by disabled people. Disabled people can use this HCI to control the computer / laptop or any electronic device connected to it.
This HCI is meant to offer a new way of computer control - different than the other existing standard control possibilities (like keyboard and/or mouse) and it can be especially useful in the case of diseased people - by giving them a new or even the only way to communicate with the external world.
References
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31. Guo, X., Pei, W., Wang, Y., Chen, Y., Zhang, H., Wu, X. et al., A human-machine interface based on single channel EOG and patchable sensor, Biomedical Signal Processing and Control 30, pp. 98-105, (2016).
32. Aungsakul, S., Phinyomark, A., Phukpattaranont, P., Limsakul, C., Evaluating feature extraction methods of electrooculography (EOG) signal for human-computer interface, Procedia Engineering 32, pp. 246-252, (2012).
33. Soltani, S., Mahnamn, A., A practical efficient human computer interface based on saccadic eye movements for people with disabilities, Computers in Biology and Medicine 70, pp. 163-173, (2016).
34. Nagy, R.B., Vesselenyi, T., Popențiu-Vlădicescu, F., An analysis of Electro-oculogram signals processing using Artificial Neural Networks, The 13th International Scientific Conference, eLearning and Software for Education, Bucharest, Volume 3, pp. 560-567, DOI: 10.12753/2066-026X-17-257, (2017).
35. Nagy, R.B., Vesselenyi T, Research regarding electro-oculogram based Human Computer Interface (HCI), Recent Innovations in Mechatronics (RIiM), Vol. 4., No. 1., ISSN 2064-9622, DOI 10.17667, (2017).
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2. Amudha, J., Reddy, S.R., Supraja, Y.R., Blink Analysis using Eye gaze tracker, Intelligent Systems Technologies and Applications, Volume 530, Advances in Intelligent Systems and Computing, pp. 237-244, (2016).
3. Pal, A., Gautam, A. K., Singh, Y. N., Evaluation of Bioelectric Signals for Human Recognition, Procedia Computer Science 48, pp. 746-752, (2015).
4. Ying, R., Wall, C. E., A method for discrimination of noise and EMG signal regions recorded during rhythmic behaviors, Journal of Biomechanics 49, pp. 4113-4118, (2016).
5. Barzilay, O., Wolf, A., A fast implementation for EMG signal linear envelope computation, Journal of Electromyography and Kinesiology 21, pp. 678-682, (2011).
6. Chandrasekaran, C., Computational principles and models of multisensory integration, Current Opinion in Neurobiology, Volume 43, pp. 25-34, (2017).
7. Mishra, V. K., Bajaj, V., Kumar, A., Sharma, D., Singh, G.K., An efficient method for analysis of EMG signals using improved empirical mode decomposition, Int. J. Electron. Commun. (AEÜ) 72, pp. 200-209, (2017).
8. Lin, M., Li, B., Liu, Q.H., Identification of eye movements from non-frontal face images for eye-controlled systems, International Journal of Automation and Computing, Volume 11, Issue 5, pp. 543-554, (2014).
9. Ghovanloo, M., Huo, X., Wearable Sensors, Fundamentals, Implementation and Applications, Chapter 7.3 - Wearable and Non-Invasive Assistive Technologies, pp. 563-590, (2014).
10. Amudha, J., Reddy, S.R., Supraja, Y.R., Blink analysis using eye gaze tracker, Intelligent Systems Technologies and Applications 2016, Volume 530, pp. 237-244, (2016).
11. Marouf, M., Saranovac, L., Vukomanovic, G., Algorithm for EMG noise level approximation in ECG signals, Biomedical Signal Processing and Control 34, pp. 158-165, (2017).
12. Yang, D., Zhang, H., Gu, Y., Liu, H., Accurate EMG onset detection in pathological, weak and noisy myoelectric signals, Biomedical Signal Processing and Control 33, pp. 306-315, (2017).
13. Hu, J., Wang, C., Wu, M., Du, Y., He, Y., She, J., Removal of EOG and EMG artifacts from EEG using combination of functional link neural network and adaptive neural fuzzy inference system, Neurocomputing 151, pp. 278-287, (2015).
14. Nguyen, H.-A. T., Musson, J., Li, F., Wang, W., Zhang, G., Xu, G., et al., EOG artifact removal using a wavelet neural network, Neurocomputing 97, pp. 374-389, (2012).
15. Burger, C., van den Heever, D. J., Removal of EOG artefacts by combining wavelet neural network and independent component analysis, Biomedical Signal Processing and Control 15, pp. 67-79, (2015).
16. Fatourechi, M., Bashashati, A., Ward, R. K., Birch, G. E., EMG and EOG artifacts in brain computer interface systems: A survey, Clinical Neurophysiology 118, pp. 480-494, (2007).
17. Klados, M. A., Bamidis, P. D., A semi-simulated EEG/EOG dataset for the comparison of EOG artifact rejection techniques, Data in Brief 8, pp. 1004-1006, (2016).
18. Minguillon, J., Lopez-Gordo, M.A., Pelayo, F., Trends in EEG-BCI for daily-life: Requirements for artifact removal, Biomedical Signal Processing and Control, Volume 31, pp. 407-418, (2017).
19.Christensen, J. A. E., Kempfner, L., Leonthin, H. L., Hvidtfelt, M., Nikolic, M., Kornum, B. R., Jennum, P., Novel method for evaluation of eye movements in patients with narcolepsy, Sleep Medicine xxx, pp. 1-10, (2016), in press.
20. Picot, A., Charbonnier, S., Caplier, A., EOG-based drowsiness detection: Comparison between a fuzzy system and two supervised learning classifiers, Proceedings of the 18th World Congress - The International Federation of Automatic Control, (2011).
21. Gulyas, S., Electro-oculography (EOG) examination of eye movements, Neuro-Ophthalmology, pp. 287-293, (2016).
22. Gandhi, T., Trikha, M., Santhosh, J., Anand, S., Development of an expert multitask gadget controlled by voluntary eye movements, Expert Systems with Applications, Volume 37, Issue 6, pp. 4204-4211, (2010).
23. Harezlak, K., Rzeszutek, J., Kasprowski, P., The eye tracking methods in user interfaces assessment, Intelligent Decision Technologies, Systems and Technologies, Volume 39, pp. 325-335, (2015).
24. Rasch, C., Louviere, J. J., Teichert, T., Using facial EMG and eye tracking to study integral affect in discrete choice experiments, The Journal of Choice Modelling 14, pp. 32-47, (2015).
25. Lv, Z., Wu, X., Li, M., Zhang, D., A novel eye movement detection algorithm for EOG driven human computer interface, Pattern Recognition Letters 31, pp. 1041-1047, (2010).
26. Bissoli, A. L. C., Sime, M. M., Bastos-Filho, T. F., Using sEMG, EOG and VOG to control an intelligent environment, IFAC-PapersOnLine, pp. 210-215, (2016).
27. Deng, L. Y., Hsu, C.-L., Lin, T.-C., Tuan, J.-S., Chang, S.-M., EOG-based human-computer interface system development, Expert Systems with Applications 37, pp. 3337-3343, (2010).
28. Barea, R., Boquete, L., Ortega, S., López, E., Rodríguez-Ascariz, J.M., EOG-based eye movements codification for human computer interaction, Expert Systems with Applications 39, pp. 2677-2683, (2012).
29. Postelnicu, C.-C., Girbacia, F., Talaba, D., EOG-based visual navigation interface development, Expert Systems with Applications 39, pp. 10857-10866, (2012).
30. Alqudah, A.M., EOG-based mouse control for people with quadriplegia, XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016, Volume 57 of the series IFMBE Proceedings, pp. 145-150, (2016).
31. Guo, X., Pei, W., Wang, Y., Chen, Y., Zhang, H., Wu, X. et al., A human-machine interface based on single channel EOG and patchable sensor, Biomedical Signal Processing and Control 30, pp. 98-105, (2016).
32. Aungsakul, S., Phinyomark, A., Phukpattaranont, P., Limsakul, C., Evaluating feature extraction methods of electrooculography (EOG) signal for human-computer interface, Procedia Engineering 32, pp. 246-252, (2012).
33. Soltani, S., Mahnamn, A., A practical efficient human computer interface based on saccadic eye movements for people with disabilities, Computers in Biology and Medicine 70, pp. 163-173, (2016).
34. Nagy, R.B., Vesselenyi, T., Popențiu-Vlădicescu, F., An analysis of Electro-oculogram signals processing using Artificial Neural Networks, The 13th International Scientific Conference, eLearning and Software for Education, Bucharest, Volume 3, pp. 560-567, DOI: 10.12753/2066-026X-17-257, (2017).
35. Nagy, R.B., Vesselenyi T, Research regarding electro-oculogram based Human Computer Interface (HCI), Recent Innovations in Mechatronics (RIiM), Vol. 4., No. 1., ISSN 2064-9622, DOI 10.17667, (2017).
36. ***. MATLAB. – Mathworks, Users Manual, Neural Network Toolbox, Deep Learning, Autoencoders, 2016.
Published
2017-12-29
How to Cite
Nagy, R., & Vesselenyi, T. (2017). RESEARCH OF ELECTRO-OCULOGRAM (EOG) CONTROLLED MOUSE CURSOR. Nonconventional Technologies Review, 21(4). Retrieved from http://revtn.ro/index.php/revtn/article/view/208
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