Proceedings of the 6th WSEAS International Conference on Applied Computer Science, Tenerife, Canary Islands, Spain, December 16-18,

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1 Proceedings of e 6 WSEAS International Conference on Applied Computer Science, Tenerife, Canary Islands, Spain, December 16-18, Identification of saccades in Electrooculograms and eir use as a control tool MARCELINO MARTINEZ, EMILIO SORIA, RAFAEL MAGDALENA, ANTONIO JOSÉ SERRANO LÓPEZ Department of Electronics Engineering University of Valencia C/. Doctor Moliner, Burjassot (Valencia) SPAIN Abstract: - This paper describes e tasks carried out to develop a control tool using e changes detected in gaze, which are captured in e electrooculogram signal. The objective is to use ese changes to control a user interface such as Dasher. A software tool for generating visual stimuli and acquiring e eye signal has been developed. These signals were later processed wi a first derivative-based algorim in order to detect e changes. The optimal parameters for e algorim have been determined, and also e sensitivity (S>97%) and e predictive positive value (+PV>9%) of e detector have also been calculated. The preliminary results are promising, but a study wi a greater number of individuals should be made to check e on-line performance wi longer registers. Key-Words: - electrooculogram, saccadic movements, dasher, acquisition, control 1 Introduction The use of gaze-tracking techniques as control systems is a work at has been increasing in recent years [1],[2]. The main idea is to control devices wi eye (or gaze) movements. These movements are extremely fast, so is greater an e response speed achieved wi a pointer, e.g., a mouse. This work presents an acquisition, processing and electrooculogram (EOG) analysis, as an initial stage to use e EOG signal in an open source, free tool called Dasher [3]. This program enables optimal writing in a text editor at is controlled by on-off signals. The auors propose e use of EOG and a modified version of e Dasher software as e base system to develop control device systems for severely handicapped people. This paper is aimed at e acquisition of EOG signals, and e generation of visual stimuli at will generate e saccadic movements, e auors also propose an algorim to characterize movements of is kind. This control signal could be used in e future as a pointer control in e Dasher software, leading to a fast writing tool for e handicapped, or ey can also be used in several applications, such as alarm systems[4], wheel chair guidance[5], etc. 2 Problem Formulation 2.1 Dasher Dasher ( [3] is a text-entry system at has a novel interface incorporating a language model. Text typing is driven by continuous two-dimensional search and navigation wi a device such as a mouse, touchscreen, rollerball, breaing device, or eye-tracker. Figure 1. Main screen of Dasher. The language model has been trained on example documents, or training corpus, which allows Dasher to predict e probability of each character s occurrence in a given context. The size of e space is allocated for each setter and successive characters according to e predicted probability. A screen shot of Dasher is shown in Figure 1. Dasher is licensed

2 Proceedings of e 6 WSEAS International Conference on Applied Computer Science, Tenerife, Canary Islands, Spain, December 16-18, under GPL and it is available for several operating systems, including Windows, Linux and Pocket PC. 2.2 Electrooculogram acquisition Several practical devices have used eye movement as a communication support. The videooculogram, which detects eye movements from pictorial images of e eyeball, requires a video camera to film eye movements in real time. Eye movement detection using infrared reflectance of e cornea is difficult to use over a long period of time because eyes tend to become dry and fatigued [1]. All of ese different techniques have advantages and drawbacks, and eir use is intrinsically related to e target of research or e specific purpose desired. Eyes control eir movements by e use of six muscles at enable em to perform different kinds of movements: saccades, fixation, and smoo pursuits [6]. In basal conditions, e retina has a bioelectrical potential at is negative wi respect to e cornea. The registering of is potential, called EOG, can be done by using surface electrodes placed around e eye. It is a non-invasive technique and can be used as a marker of eye movements. This technique measures eye movements relative to e head position and is not generally suitable for point of regard measurements unless e head position is also measured [7]. This potential is corrupted wi several sources of noise which makes e amplification stage a critical component. The typical spatial resolution when using surface electrodes is ±1.5º-2º[5]. There are some problems associated wi EOG measurement. Eye blinks and eye-muscle electrical activity contaminate e signal; moreover, ere is a considerable wandering of basal line due to electrode drift. All ese factors are considered as noise overlapping to e target signal and must be eliminated using digital processing techniques as a previous step before signal interpretation. Wi is meod, e final signal can be used as a control signal. Electrooculogram signals have bandwid between DC and 1 Hz. The amplitude ranges from 15 μv to 2 μv and is linearly related to e eye displacement, wi nominal sensitivities of about 2μV/deg. An important problem of is bioelectrical potential is e DC component which may saturate e amplifier. A set of five electrodes Ag/AgCl has been used in e acquisition of e signal, located at e positions shown in Figure 2. These potentials have been amplified using e g.bsamp module from Guger Technologies. This module is an AC-coupled amplifier wi a cut-off frequency of.1 Hz; wi is meod, e typical saturation of DC coupling is avoided. In order to reduce skin impedance, e skin was cleaned wi an abrasive gel, Parker Redux Paste. Every register consists of a horizontal channel (channel H) and a vertical channel (channel V), which should enable e detection of changes in bo directions. Figure 2. Layout of electrodes used in e acquisition of EOG registers. Since we are solely interested in e measurement of abrupt eye movement (saccadic), an analysis of e obtained waveform was made in order to be able to determine which sampling frequency was most suitable and us to determine e antialiasing filter to be used. Finally, we decided to use a sampling rate of 2 Hertz per channel and to limit bandwid by means of a 2nd order analogue Butterwor filter wi a cut-off frequency of about 4 Hertz. The entire amplifying system is powered by rechargeable batteries, and also has galvanic isolation to guarantee e safety of e patient. The amplified signal is e input of a data acquisition card (PCL 711B of Advantech) wi 12 bits of resolution. A program has been developed at enables e user to simultaneously control, e acquisition of e EOG signals and e generation of visual stimuli; e position and e duration of e stimuli are selectable parameters. Figure 3 shows e positions in which visual stimuli appear. Each stimulus lasts 2 seconds. Only one stimulus is visible at a time. 1 7 Stimulus position Figure 3. Locations of e visual stimuli (only one will be visible at a time). The visual stimuli are yellow circles on a black background. The distance of e subject to e monitor has been fixed to 5 cm. and a 17-inches screen has been used. The height of e chair has also 3 9

3 Proceedings of e 6 WSEAS International Conference on Applied Computer Science, Tenerife, Canary Islands, Spain, December 16-18, been fixed, so at e line of e eyes is approximately in e centre of e screen. Thus, watching e central point does not imply displacement of e eyeball. Additionally, e patient are asked not to move eir heads during e acquisition. 2.3 Saccadic movements detection The problem, (extremely simplified), is shown in Figure 4; is figure shows an ideal EOG signal, wi saccades and fixations. This signal represents e movement of e eye in only one direction; eier horizontal or vertical. It is important to note at we are not interested in e exact point of e eye gaze but in an estimation of e direction (left, right, up and down) Blinks Figure 5. Example of a type I register which shows e effect of eye blinks, mainly in channel V Figure 4. Representation of an ideal EOG signal along wi e desired detection marks. Figure 5 shows a register in which transitions have taken place up-down and later left-right. This figure shows at ey are independent movements. Figure 5 also clearly shows e presence of blinks in channel V, which we can consider to be impulsive noise EOG registers Type I registers. In is type of register, ere are transitions at only affect one channel. They are composed of repeated patterns up-down or left-right. Thus, e EOG signal only reflects changes in one channel. Figure 5 shows an example of a type I register. The number of type I registers is 16. Type II registers. In is case, e registers are generated using a sequence wi simultaneous vertical and horizontal transitions. According to e numbering in Figure 3, e sequence follows e locations: [5,1,3,9,7,1,5,1,5,3,5,9,5,7,5,2,6,8,4,2,5, 2,5,6,5,8,5,4,5,2,8,5,6,4,5,1,9,5,3,7,5]. Figure 6 shows an example of a type II register. The number of type II registers is Figure 6. Example of a type II register. In is case, ere are simultaneous transitions in bo directions. 3 Problem Solution 3.1 Saccadic eye movement detection We have used a variation of e algorim based on e point-to-point derivative of e signal in order to determine e position of e gaze, among e different techniques described in [6]. The algorim operation is e same for bo H and V channels. A simplified pseudo code is shown in Table I. The main difference wi e originally proposed algorim is at a median filter is first applied to e signal. Since e leng of e filter is similar to a blink, blinks are eliminated. In order to estimate resholds on e derivative, we distinguish between positive and negative slopes because e eye line is not always in e middle of e screen. In order to determine e optimal performance of e algorim, e slope reshold ( slope ) and e median filter leng ( MF leng ) must be selected. The parameter slope is a factor at multiplies e average of e signal derivatives in e transition

4 Proceedings of e 6 WSEAS International Conference on Applied Computer Science, Tenerife, Canary Islands, Spain, December 16-18, points determined by e stimulus pattern. We have used e first 25% of e known transitions for parameter estimation. Filter e original signal wi a median filter of leng MF leng Calculate e point to point velocity of e filtered signal. Estimate resholds of e previous signal in e first transition points, for positive and negative slopes. Check e complete signal looking for slopes at exceed e resholds (is way saccadic movements and fixations can be distinguished. The sign of e slope gives information about e gaze direction). Table I. Pseudocode of e proposed algorim. 3.2 Results Many tests were performed on e free parameters and useful information about e optimal values was obtained. The values given to e free parameters were slope =.2:.2:.9, and MF leng =2:2:14, where MATLAB TM notation was used (InitialValue:Step:FinalValue). The parameters at were used for optimal value selection were e sensitivity and positive predictive value value of e saccadic eye movement detector. The optimum values were: Type I registers: Type II registers: slope =.6; slope =.4; MF leng =.5s; MF leng =.5s; Figures 7 and 8 show e result obtained when applying e algorim to a type I and a type II register, respectively. A dot marks e points wi negative slope, and an asterisk marks e points wi positive slope. The determination of e direction of e movement depends on e channel being considered.. Table II shows e results of algorim sensitivity and positive predictive value (+PV) when applied to all e registers. We have separated H and V channels, and positive (Hp, Vp) and negative (Hn,Vn) transitions. The number of transitions was N I =256 for type I registers and N II =27 for type II registers. 4 Conclusions The first conclusion at we observed from e acquired registers is at e changes in e direction of e gazes are appraised clearly in bo channels, wi e vertical channel being e noisier one due to e presence of impulsive noise generated by involuntary blinks. Significant base line wander is observed in e registers, is is due to e extremely low cut-off frequency of e HP filter at sometimes can saturate e amplifier and present signal values at are different from zero even ough an individual is watching e centre of e screen. This effect is also generated due to an inappropriate positioning of e patient. Register Hp Hn Vp Vn Sensitivity I (N I =256) +PV Sensitivity II (N II =27) +PV Table II. Results of e saccadic eye movement detection algorim. S: sensitivity(%). +PV: Positive Prediction Value Figure 7. Results of e saccade detection algorim in a type I register. The proposed algorim is quite simple because it only uses a median filter of leng similar to e duration of a blink, and it uses e derivative for e detection of transitions. Derivative resholds are determined in an initial training stage of e system. The application of e algorim on bo types of register have yielded good detection results in bo channels, alough e horizontal channel always yields better results due to e lower amount of noise. The auors are working on combining e output of e algorim wi e Dasher software, which is

5 Proceedings of e 6 WSEAS International Conference on Applied Computer Science, Tenerife, Canary Islands, Spain, December 16-18, currently limited to text typing software but whose operation can be extended for oer purposes. The final goal is to use e EOG signal as a control signal for a Human-Machine Interface to detect transitions raer an to detect e absolute position of e gaze. Anoer possible use of is signal could be as an alarm signal for individuals wi severe handicaps Sample (a) Sample (b) The auors wish to ank Prof. Francisco Alcantud, (Faculty of Psychology at e University of Valencia), for his help during e register acquisition, as well as e volunteers who unselfishly collaborated in e register acquisition. References [1] Hori J., Sakano K, Saitoh Y. Development of a Communication Support Device Controlled by Eye Movements and Voluntary Eye Blink. IEICE Trans Inf Syst 26; E89-D(6): [2]Tecce JJ, Pok LJ, Consiglio MR, O'Neil JL. Attention impairment in electrooculographic control of computer functions. Int J Psychophysiol 25; 55(2): [3]Ward DJ. Adaptive Computer Interfaces UK: University of Cambridge, 21. [4]Venkataramanan S., Praanay Prabhat, Shubhodeep Roy Choudhury, Harshal B., Sahambi J.S. Biomedical Instrumentation Based on Electrooculogram (EOG) Signal Processing and Application to a Hospital Alarm System. IEEE Proceedings of e Second International Conference on Intelligent Sensing and Information Processing (ICISIP). 25 [5]Barea R, Boquete L, Mazo M, Lopez E. System for assisted mobility using eye movements based on electrooculography. IEEE Trans Neural Syst Rehabil Eng 22; 1(4): [6] Salvucci DD, Goldberg JH. Identifying fixations and saccades in eye-tracking protocols. Proceedings of e 2 Symposium on Eye Tracking Research & Applications.ETRA '. New York: ACM Press. [7]Duchowsky A.T. Eye Tracking Meodology: Theory and Practice. Springer-Verlag, 23. Figure 8. Results of e saccade detection algorim in a type II register: (a) Horizontal channel, (b) Vertical channel. Even ough e preliminary results are promising, a study wi a greater number of individuals should be made in order to check e online performance wi longer registers. In is study, resholds should be updated on-line Acknowledgments This study has been partially supported by project GV6/248 of Conselleria d Empresa Universitat i Ciencia de la Generalitat Valencia, and project UV- AE from e University of Valencia.