Eye Blink Classification for Assisting Disability to Communicate Using Bagging and Boosting

Luthfi Ardi; Noor Akhmad Setiawan; Sunu Wibirama

doi:10.22146/ijitee.63515

Eye Blink Classification for Assisting Disability to Communicate Using Bagging and Boosting

https://doi.org/10.22146/ijitee.63515

Luthfi Ardi^(1*), Noor Akhmad Setiawan⁽²⁾, Sunu Wibirama⁽³⁾

(1) Universitas Gadjah Mada
(2) Universitas Gadjah Mada
(3) Universitas Gadjah Mada
(*) Corresponding Author

Abstract

Disability is a physical or mental impairment. People with disability have more barriers to do certain activity than those without disability. Moreover, several conditions make them having difficulty to communicate with other people. Currently, researchers have helped people with disabilities by developing brain-computer interface (BCI) technology, which uses artifact on electroencephalograph (EEG) as a communication tool using blinks. Research on eye blinks has only focused on the threshold and peak amplitude, while the difference in how many blinks can be detected using peak amplitude has not been the focus yet. This study used primary data taken using a Muse headband on 15 subjects. This data was used as a dataset classified using bagging (random forest) and boosting (XGBoost) methods with python; 80% of the data was allocated for learning and 20% was for testing. The classified data was divided into ten times of testing, which were then averaged. The number of eye blinks’ classification results showed that the accuracy value using random forest was 77.55%, and the accuracy result with the XGBoost method was 90.39%. The result suggests that the experimental model is successful and can be used as a reference for making applications that help people to communicate by differentiating the number of eye blinks. This research focused on developing the number of eye blinks. However, in this study, only three blinking were used so that further research could increase these number.

Keywords

Brain-Computer Interface (BCI);Electroencephalograph (EEG);Eye Blinks;Disability

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References

P. Masrori and P. Van Damme, “Amyotrophic Lateral Sclerosis: A Clinical Review,” European Journal of Neurology, Vol. 27, No. 10, pp. 1918-1929, Oct. 2020.

F. Cincotti, D. Mattia, F. Aloise, S. Bufalari, G. Schalk, G. Oriolo, A. Cherubini, M.G. Marciani, and F. Babiloni, “Non-Invasive Brain-Computer Interface System: Towards Its Application as Assistive Technology,” Brain Research Bulletin, Vol. 75, No. 6, pp. 796-803, Apr. 2008.

M.M. Fouad, K.M. Amin, N. El-Bendary, and A.E. Hassanien, “Brain Computer Interface: A Review,” in Brain-Computer Interfaces, A.E. Hassanien and A.T. Azar, Eds. Cham, Switzerland: Springer International Publishing, 2015, pp. 3-30.

S. Soman and B.K. Murthy, “Using Brain Computer Interface for Synthesized Speech Communication for the Physically Disabled,” Procedia Computer Science, Vol. 46, pp. 292-298, Dec. 2015.

K.S. Kabir, C.M.A. Rahman, A. Farayez, and M. Ferdous, “An Experimental Model of an Indigenous BCI Based System to Help Disabled People to Communicate,” AIP Conference Proceedings, Vol. 1919, No. 1, 2017, pp. 1-7.

I.A.E. Zaeni, A.P. Wibawa, and M. Ashar, “The Design of the Patient’s Room Facilities Controller Using an Eye Blink,” 2017 5th International Conference on Electrical, Electronics and Information Engineering (ICEEIE), 2017, pp. 88-91.

W. Zhi-Hao, Hendrick, K. Yu-Fan, C. Chuan-Te, L. Shi-Hao, and J. Gwo-Jia, “Controlling DC Motor Using Eye Blink Signals Based on LabVIEW,” 2017 5th International Conference on Electrical, Electronics and Information Engineering (ICEEIE), 2017, pp. 61-65.

J. Immanuel, A. Joshua, and S.T. George, “A Study on Using Blink Parameters from EEG Data for Lie Detection,” 2018 International Conference on Computer Communication and Informatics (ICCCI), 2018, pp. 1-5.

A.K. Singh, Y. Wang, J. King, C. Lin, and L. Ko “A Simple Communication System Based on Brain Computer Interface,” 2015 Conference on Technologies and Applications of Artificial Intelligence (TAAI), 2015, pp. 363-366.

B. Chambayil, R. Singla, and R. Jha, “Virtual Keyboard BCI Using Eye Blinks in EEG,” 2010 IEEE 6th International Conference on Wireless and Mobile Computing, Networking and Communications, 2010, pp. 466-470.

M.S.B.A. Rani and B.T.M. Wahidah, “Detection of Eye Blinks from EEG Signals for Home Lighting System Activation,” 2009 6th International Symposium on Mechatronics and Its Applications, 2009, pp. 1-4.

J.J. Bird, L.J. Manso, E.P. Ribeiro, A. Ekart, and D.R. Faria, “A Study on Mental State Classification Using EEG-Based Brain-Machine Interface,” 2018 International Conference on Intelligent Systems (IS), 2018, pp. 795-800.

A. Przegalinska, L. Ciechanowski, M. Magnuski, and P. Gloor, “Muse Headband: Measuring Tool or a Collaborative Gadget?” in Collaborative Innovation Networks, Studies on Entrepreneurship, Structural Change and Industrial Dynamics, F. Grippa, J. Leitão, J. Gluesing, K. Riopelle, and P. Gloor, Eds., Cham, Switzerland: Springer International Publishing, 2018, pp. 93-101.

M.K. Delimayanti, B. Purnama, N.G. Nguyen, M.R.Faisal, K.R. Mahmudah, F. Indriani, M. Kubo, and K. Satou, “Classification of Brainwaves for Sleep Stages by High-Dimensional FFT Features from EEG Signals,” Applied Sciences, Vol. 10, No. 5, pp. 1-12, Mar. 2020.

A.S. Al-Fahoum and A.A. Al-Fraihat, “Methods of EEG Signal Features Extraction Using Linear Analysis in Frequency and Time-Frequency Domains,” ISRN Neuroscience, Vol. 2014, pp. 1-7, Feb. 2014.

E. Goel and E. Abhilasha, “Random Forest: A Review,” International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 7, No. 1, pp. 251-257, 2017.

T. Chen and C. Guestrin, “XGBoost: A Scalable Tree Boosting System,” Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, pp. 785-794.

S. Kabiraj, M. Raihan, N. Alvi, M. Afrin, L. Akter, S.A. Sohagi, and E. Podder, “Breast Cancer Risk Prediction Using XGBoost and Random Forest Algorithm,” 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT), 2020, pp. 1-4.

DOI: https://doi.org/10.22146/ijitee.63515