Electrooculogram (EOG) based Mouse Cursor Controller Using the Continuous Wavelet Transform and Statistic Features

  • Triadi Triadi Telkom University
  • Inung Wijayanto Telkom University
  • Sugondo Hadiyoso Universitas Telkom

Abstract

This study design a system prototype to control a mouse cursor's movement on a computer using an electrooculogram (EOG) signal. The EOG signal generated from eye movement was processed utilizing a microcontroller with an analog to the digital conversion process, which communicates with the computer through a USB port. The signal was decomposed using continuous wavelet transform (CWT), followed by feature extraction processes using statistic calculation, and then classified using K-Nearest Neighbors (k-NN) to decide the movement and direction of the mouse cursor. The test was carried out with 110 EOG signals then separated, 0.5 as training data and 0.5 as test data with eight categories of directional movement patterns, including up, bottom, right, left, top right, top left, bottom right bottom left. The highest accuracy that can be achieved using CWT-bump and kurtosis is 100%, while the time needed to translate the eye movement to the cursor movement is 1.9792 seconds. It is hoped that the proposed system can help assistive devices, particularly for Amyotrophic Lateral Sclerosis (ALS) sufferers.


 

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References

[1] S. Chandra, G. Sharma, S. Malhotra, D. Jha, and A. P. Mittal, "Eye tracking based human computer interaction: Applications and their uses," in Proceedings - 2015 International Conference on Man and Machine Interfacing, MAMI 2015, 2016, no. December, pp. 1–5, doi: 10.1109/MAMI.2015.7456615.
[2] X. Zhang, X. Liu, S. M. Yuan, and S. F. Lin, "Eye Tracking Based Control System for Natural Human-Computer Interaction," Computational Intelligence and Neuroscience, vol. 2017, pp. 1–9, 2017, doi: 10.1155/2017/5739301.
[3] D. Y. Kim, C. H. Han, and C. H. Im, "Development of an electrooculogram-based human-computer interface using involuntary eye movement by spatially rotating sound for communication of locked-in patients," Scientific Reports, vol. 8, no. 1, pp. 1–10, 2018, doi: 10.1038/s41598-018-27865-5.
[4] C.-Y. Su and J.-J. Wong, "Connecting with Dysphonia: Human-Computer Interface for Amyotrophic Lateral Sclerosis Patients," 2011, pp. 453–457.
[5] H. Ka Hou and S. K.G., "Low-Cost Wireless Electrooculography Speller," in 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Oct. 2018, pp. 123–128, doi: 10.1109/SMC.2018.00032.
[6] G. Teng, Y. He, H. Zhao, D. Liu, J. Xiao, and S. Ramkumar, "Design And Development Of Human Computer Interface Using Electrooculogram With Deep Learning," Artificial Intelligence in Medicine, vol. 102, p. 101765, Jan. 2020, doi: 10.1016/j.artmed.2019.101765.
[7] O. Hardiman et al., "Amyotrophic lateral sclerosis," Nature Reviews Disease Primers, vol. 3, no. 1, p. 17071, Dec. 2017, doi: 10.1038/nrdp.2017.71.
[8] E. Zucchi et al., "Neurofilaments in motor neuron disorders: towards promising diagnostic and prognostic biomarkers," Molecular Neurodegeneration, vol. 15, no. 1, p. 58, Dec. 2020, doi: 10.1186/s13024-020-00406-3.
[9] D. Yuan et al., "A closed-loop electrical stimulation system triggered by EOG for acupuncture therapy," Systems Science & Control Engineering, vol. 8, no. 1, pp. 128–140, 2020, doi: 10.1080/21642583.2020.1733130.
[10] A. Bissoli, D. Lavino-Junior, M. Sime, L. Encarnação, and T. Bastos-Filho, "A human–machine interface based on eye tracking for controlling and monitoring a smart home using the internet of things," Sensors (Switzerland), vol. 19, no. 4, pp. 1–26, 2019, doi: 10.3390/s19040859.
[11] C.-I. Wu, "HCI and Eye Tracking Technology for Learning Effect," Procedia - Social and Behavioral Sciences, vol. 64, pp. 626–632, Nov. 2012, doi: 10.1016/j.sbspro.2012.11.073.
[12] A. Sahayadhas, K. Sundaraj, and M. Murugappan, "Detecting Driver Drowsiness Based on Sensors: A Review," Sensors, vol. 12, no. 12, pp. 16937–16953, Dec. 2012, doi: 10.3390/s121216937.
[13] J. Xu, J. Min, and J. Hu, "Real-time eye tracking for the assessment of driver fatigue," Healthcare Technology Letters, vol. 5, no. 2, pp. 54–58, 2018, doi: 10.1049/htl.2017.0020.
[14] W. S. Sanjaya, D. Anggraeni, R. Multajam, M. N. Subkhi, and I. Muttaqien, "Design and Experiment of Electrooculogram (EOG) System and Its Application to Control Mobile Robot," Journal of Physics: Conference Series, vol. 180, pp. 1–8, 2017, doi: 10.1088/1742-6596/755/1/011001.
[15] R. B. Navarro, L. B. Vázquez, and E. L. Guillén, EOG-based wheelchair control, Second Edition Elsevier B.V., 2018.
[16] N. Borkar, T. Dongare, P. Chahande, J. Bonsod, and A. B. Jirapure, "Microcontroller Based EOG and Accelerometer Guide Wheelchair," International Research Journal of Engineering and Technology (IRJET), vol. 5, no. 3, pp. 3803–3807, 2018.
Published
2021-03-31
How to Cite
TRIADI, Triadi; WIJAYANTO, Inung; HADIYOSO, Sugondo. Electrooculogram (EOG) based Mouse Cursor Controller Using the Continuous Wavelet Transform and Statistic Features. Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, [S.l.], v. 12, n. 1, p. 53-61, mar. 2021. ISSN 2541-5832. Available at: <https://ojs.unud.ac.id/index.php/lontar/article/view/70269>. Date accessed: 22 nov. 2024. doi: https://doi.org/10.24843/LKJITI.2021.v12.i01.p06.