International Journal of Advanced Technology and Engineering Exploration (IJATEE) ISSN (P): 2394-5443 ISSN (O): 2394-7454 Vol - 8, Issue - 81, August 2021
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Study of electrooculography signal acquisition sites for assistive device applications

Karthik Raj V

Abstract

Electrooculography (EOG) is a technique that involves the measurement of the corneo-retinal standing potential of the eye. The human eye acts as a dipole between the cornea (positive potential) and the retina (negative potential), creating an electric field around the eyeball. The resulting electric signal obtained from this field is called electrooculogram. These signals, generated by eye movements, could be measured by employing different electrode placement configurations for the acquisition. The properties of these signals change depending on the number and placement of the electrodes. The study conducted here describes the EOG signal acquisition using new electrode placement configurations that employ fewer facial electrodes placed on the patient. Three pre-gelled disposable electrodes were utilized for this purpose. Only one electrode was placed in a facial location, enhancing patient comfort during the acquisition procedure. To support this study, a low-cost signal acquisition hardware was developed. Using active filtering and amplification, appropriate signal processing techniques were executed upon the horizontal EOG signal acquired to reduce noise and interference due to external conditions. Hence, this paper presents the findings of new electrode placement sites for the acquisition of EOG signals which could be used for assistive device applications while restricting the number of facial electrodes to one. Most of the studies regarding the EOG signal acquisition had been using all electrodes in the face region. In contrast, we reduced the number of electrodes in the facial region, thereby providing patient comfort. The comparison was made mainly on the acquired data from these new locations to discover the configuration with the optimal signal response using data obtained from seven healthy subjects. The amplitude values were being compared from the new locations with the standard acquisition sites. The findings of this study were found to have a productive result. The total gain of the system required for new electrode placement configurations was two times more than the total gain required for standard acquisition sites, and also, the amplitude was less but can be helpful for assistive device applications. The average peak to peak amplitude value of the EOG signal for the new site was approximate 1.25 volts.

Keyword

Horizontal EOG, Facial electrodes, Pre-gelled disposable electrode, Signal processing.

Cite this article

Refference

[1][1]Webster JG. Medical instrumentation: application and design. John Wiley & Sons; 2009.

[2][2]Zhang J, Wang B, Zhang C, Hong J. Volitional and real-time control cursor based on eye movement decoding using a linear decoding model. Computational Intelligence and Neuroscience. 2016:1-11.

[3][3]Swami P, Gandhi TK. Assistive communication system for speech disabled patients based on electro-oculogram character recognition. In international conference on computing for sustainable global development 2014 (pp. 373-6). IEEE.

[4][4]López A, Ferrero FJ, Valledor M, Campo JC, Postolache O. A study on electrode placement in EOG systems for medical applications. In international symposium on medical measurements and applications 2016 (pp. 1-5). IEEE.

[5][5]Tarunkumar S, Raghul SS, Karthik RV, Pon V. An assistive device for quadriplegic patients using NI-MyRIO. Biomedical Engineering: Applications, Basis and Communications. 2017; 29(3).

[6][6]Aswin RV, Karthik RV. EOG based low cost device for controlling home appliances. International Journal of Innovative Research in Science, Engineering and Technology. 2014; 3(3):708-11.

[7][7]Reda R, Tantawi M, Shedeed H, Tolba MF. Eye movements recognition using electrooculography signals. In joint European-US workshop on applications of invariance in computer vision 2020 (pp. 490-500). Springer, Cham.

[8][8]Barea R, Boquete L, Mazo M, López E. System for assisted mobility using eye movements based on electrooculography. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2002; 10(4):209-18.

[9][9]Jambhulkar A, Wandhare S, Baraskar D, Barahate K. Wireless and portable EOG based interface for controlling wheelchair. International Journal of Science, Engineering and Technology. 2016; 5:189-91.

[10][10]Konwar P, Bordoloi H. A system design approach to control a wheelchair using EOG signal. Current Trends in Technology and Science. 2014; 3(3):155-8.

[11][11]Usakli AB, Gurkan S. Design of a novel efficient human–computer interface: an electrooculagram based virtual keyboard. IEEE Transactions on Instrumentation and Measurement. 2009; 59(8):2099-108.

[12][12]Gandhi T, Trikha M, Santhosh J, Anand S. Development of an expert multitask gadget controlled by voluntary eye movements. Expert Systems with Applications. 2010; 37(6):4204-11.

[13][13]Ang AM, Zhang ZG, Hung YS, Mak JN. A user-friendly wearable single-channel EOG-based human-computer interface for cursor control. In international IEEE/EMBS conference on neural engineering 2015 (pp. 565-8). IEEE.

[14][14]Mamatha KM, Sumalatha S, Nalini S. EOG based HMI for paralysed people to control electrical devices. International Journal of Engineering Research & Technology. 2013; 2(5):1319-24.

[15][15]Bhuyain MF, Shawon MA, Sakib N, Faruk T, Islam MK, Salim KM. Design and development of an EOG-based system to control electric wheelchair for people suffering from quadriplegia or quadriparesis. In international conference on robotics, electrical and signal processing techniques 2019 (pp. 460-5). IEEE.

[16][16]Kuntal K, Banerjee I, Lakshmi PP. Design of wheelchair based on electrooculography. In international conference on communication and signal processing 2020 (pp. 632-6). IEEE.

[17][17]Huang Q, He S, Wang Q, Gu Z, Peng N, Li K, et al. An EOG-based human–machine interface for wheelchair control. IEEE Transactions on Biomedical Engineering. 2017; 65(9):2023-32.

[18][18]Sucres MA, Pérez SS, Zavala IV, Ramírez DL, Hernández NR. EOG-based interface and speech recognition for wheelchair control. In international conference on engineering Veracruz 2019 (pp. 1-4). IEEE.

[19][19]López A, Fernández D, Ferrero FJ, Valledor M, Postolache O. EOG signal processing module for medical assistive systems. In international symposium on medical measurements and applications 2016 (pp. 1-5). IEEE.

[20][20]Aziz S, Ibraheem S, Malik A, Aamir F, Khan MU, Shehzad U. Electrooculugram based communication system for people with locked-in-syndrome. In international conference on electrical, communication, and computer engineering 2020 (pp. 1-6). IEEE.

[21][21]Kołodziej M, Tarnowski P, Sawicki DJ, Majkowski A, Rak RJ, Bala A, et al. Fatigue detection caused by office work with the use of EOG signal. IEEE Sensors Journal. 2020; 20(24):15213-23.

[22][22]Milanizadeh S, Safaie J. EOG-based HCI system for quadcopter navigation. IEEE Transactions on Instrumentation and Measurement. 2020; 69(11):8992-9.

[23][23]De LSE, Conzelmann P. Computer USB-mouse emulation using EOG. In international symposium on instrumentation systems, circuits and transducers 2019 (pp. 1-5). IEEE.

[24][24]Hayawi AA, Waleed J. Drivers drowsiness monitoring and alarming auto-system based on EOG signals. In international conference on engineering technology and its applications 2019 (pp. 214-8). IEEE.

[25][25]Hou HK, Smitha KG. Low-cost wireless electrooculography speller. In international conference on systems, man, and cybernetics 2018 (pp. 123-8). IEEE.

[26][26]Latifoğlu F, İleri R, Demirci E, Altıntop ÇG. Detection of reading movement from EOG signals. In international symposium on medical measurements and applications 2020 (pp. 1-5). IEEE.

[27][27]Lin CT, King JT, Bharadwaj P, Chen CH, Gupta A, Ding W, et al. EOG-based eye movement classification and application on HCI baseball game. IEEE Access. 2019; 7:96166-76.

[28][28]Lu YY, Huang YT. A method of personal computer operation using electrooculography signal. In eurasia conference on biomedical engineering, healthcare and sustainability 2019 (pp. 76-8). IEEE.

[29][29]Banerjee A, Chakraborty S, Das P, Datta S, Konar A, Tibarewala DN, et al. Single channel electrooculogram (EOG) based interface for mobility aid. In international conference on intelligent human computer interaction 2012 (pp. 1-6). IEEE.

[30][30]Mala S, Latha K. Feature selection in classification of eye movements using electrooculography for activity recognition. Computational and Mathematical Methods in Medicine. 2014:1-9.

[31][31]Vidal M, Bulling A, Gellersen H. Analysing EOG signal features for the discrimination of eye movements with wearable devices. In proceedings of the international workshop on pervasive eye tracking & mobile eye-based interaction 2011 (pp. 15-20).

[32][32]Bulling A, Ward JA, Gellersen H, Tröster G. Eye movement analysis for activity recognition using electrooculography. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2010; 33(4):741-53.

[33][33]Mala S, Latha K. Classification of electrooculograph signals: comparing conventional classifiers using CBFS feature selection algorithm. In fourth international conference on computing, communications and networking technologies 2013 (pp. 1-7). IEEE.

[34][34]Yamagishi K, Hori J, Miyakawa M. Development of EOG-based communication system controlled by eight-directional eye movements. In international conference of engineering in medicine and biology society 2006 (pp. 2574-7). IEEE.

[35][35]Nann M, Cordella F, Trigili E, Lauretti C, Bravi M, Miccinilli S, et al. Restoring activities of daily living using an EEG/EOG-controlled semiautonomous and mobile whole-arm exoskeleton in chronic stroke. IEEE Systems Journal. 2020; 15(2):2314-21.

[36][36]Golparvar AJ, Yapici MK. Graphene-coated wearable textiles for EOG-based human-computer interaction. In international conference on wearable and implantable body sensor networks 2018 (pp. 189-92). IEEE.

[37][37]Xiao J, Qu J, Li Y. An electrooculogram-based interaction method and its music-on-demand application in a virtual reality environment. IEEE Access. 2019; 7:22059-70.

[38][38]Zhang R, He S, Yang X, Wang X, Li K, Huang Q, et al. An EOG-based human–machine interface to control a smart home environment for patients with severe spinal cord injuries. IEEE Transactions on Biomedical Engineering. 2018; 66(1):89-100.

[39][39]Zheng WL, Gao K, Li G, Liu W, Liu C, Liu JQ, et al. Vigilance estimation using a wearable EOG device in real driving environment. IEEE Transactions on Intelligent Transportation Systems. 2019; 21(1):170-84.