Classification of People using Eye-Blink Based EOG Peak Analysis.

Transcription

1 Classification of People using Eye-Blink Based EOG Peak Analysis. Andrews Samraj, Thomas Abraham and Nikos Mastorakis Faculty of Computer Science and Engineering, VIT University, Chennai- 48, India. Technical University of Sofia, Bulgaria. Abstract:- While Recording Electroencephalography (EEG) the ongoing electrical activity along the scalp, it records brain s spontaneous electrical activity over a short period of time, along with the eye blinks which is one among the many biological artifacts that contaminates the EEG. The EEG signals recorded from alcoholic and control subjects using the presentation of visual stimulus are used in this experiment to find the impact of EOG on EEG of different people. The EEG signals recorded from a set of two experimental categories are analyzed to detect the EOG components like eye blinks buried in it. The detected eye blinks are further analyzed to prove that the alcoholics have lesser object identification power over the controls and there is lesser power in the EOG produced. In this paper, it is proved that using the experiments carried out, EOG based systems are reliable to controls rather than alcoholics if implemented for classifications on any paradigm based applications. Keywords: - Electrooculogram, Electroencephalogram, Picture paradigm.. Introduction Electroencephalography (EEG) is the recording of electrical activity along the scalp. It records brain s spontaneous electrical activity over a short period of time. Evoked potentials (EP) can be induced in EEG that reflects in brain activity temporally on a stimulus onset []. These electrical signals of EEG are usually contaminated by biological and technical artifacts. The biological artifacts include eye induced artifacts, muscle induced artifacts, and the cardiac artifacts. [6][7]. Eye induced artifacts includes eye blinks, eye movements and extra-ocular muscle activity. Electrooculography (EOG) is a field that measures the eye induced artifacts and the signal that results from the study is called the electro_oculogram [8][9]. The purpose of this paper is to show that the EOG based systems are reliable in identifying classes, and are the easy way to differentiate alcoholics from control subjects. In order to prove this, the EOG signals were extracted from an available EEG signal - set and further the differences in EOG between alcoholics and non-alcoholics were examined. Section of this paper explains the Methods followed in dataset creation and., the calculation of peaks on artificially generated signals and the same was done on real EEG signals, which is explained in section.. Section shows the results of the experiment, followed by the conclusion.. Methods The experiment was conducted on two groups of subjects who are categorized as alcoholics and controls. Their EEG signals were recorded in the experiments showing pictures of familiar and nonfamiliar categories. Secondly, the EOG signals alone were extracted from both the categories of EEG using butterworth filter and analyzed further [][4]. In this experiment, the peaks were counted in the extracted EOG signals and proved that power spectral density is weak in alcoholics than in the controls. ISBN:

2 First, it is set to prove the effectiveness of the experiment through a simulation study by deploying artificial EOG signals before the actual EOG signals captured along with the EEG.. Artificial EOG creation Twenty one artificial EOG signals were created using different combinations of Gaussian waveforms, each with different mean, variance and amplitude [] and resembles the actual EOG. These basic waveforms were created using the equation G(n)=(A/sqrt(πσ))exp(-((n-µ))/σ) () These EOGs were limited to reflect the amplitudes produced in the real EOG with a threshold of µv to 5 µv which is in the range of real EOG found in the data set. They were mixed with the real EEG signals at variable intervals, The EEG used to mix with this artificial EOG were obtained when the subjects were at rest. These EEG signals were whitened to remove their correlation, before adding to the artificial EOG signals,. Experiments using real EOG Twenty different subjects, ten alcoholics and ten controls, were exposed to two types of stimuli, which were pictures of objects chosen from different categories and the EEG signals were recorded [][4][5]. A set of randomly chosen pictures (first visual stimulus, S) was shown to the subjects. Another set of pictures (second visual stimulus, S) was selected according either to the matching (SM) or nonmatching (SN) rule, relative to the initial stimulus (S). The pictures in S were chosen in such a way that it is different from S not only in its appearance but also in terms of the category to avoid ambiguity. For example, if a picture in S is an animal, then S will not be a picture from the animal category. W(n) EOG+EEG =X(n) EOG +Y(n) EEG () The EOG mixed signal, W was then normalized to zero mean and unit variance. W = (W mean (W)) / Std (W) () For a given amplitude threshold, the number of EOG peaks could be counted precisely with the actual number of peaks. This proves that the calculation of peaks count above the given amplitude threshold is correct. The artificial EOG signals with different amplitude strengths are shown in Figure Figure : Artificial EOG signal. Figure : Electrode locations for the EEG recording system The eye blink artifact was extracted as odd peaks of EOG from the channel EEG signals based on amplitude discrimination and counted (the threshold value of µv was used since blinking typically produces potential of - µv lasting for 5 ms []). The extracted EEG signals from S stimuli were low pass filtered for smoothening the signal to have the EOG amplitudes predominant.. Results and Analysis The number of peaks above the threshold values of positive zero amplitude [] in EEG signals extracted from controls and alcoholics on all ISBN:

3 channels were counted separately and are given in table and table respectively. The number of EEG and smoothened EEG signals shown here are and average of signals for two different subjects is provided. It is observed that the total number of eligible EOG peaks above the set threshold per unit time in alcoholics is lesser than that in controls. In table the average number of peaks is shown from the EEG and extracted EOG signals of different subjects. Figure shows the randomly selected EEG signals and extracted EOG signals on an alcoholic subject. Figure4 shows the randomly selected EEG signals and EOG signals extracted from EEG of a different alcoholic subject. Figure5 and Figure6 shows the randomly selected EEG signals and EOG signals extracted from EEG on two different controls Figure 4: EEG and EOG signals recorded from alcoholics(sn) Figure 5: EEG and EOG signals recorded from controls(sm) Figure : EEG and EOG signals recorded from alcoholics(sm) ISBN:

4 [] S.Andrews, R.Palaniappan, Vijanth S.Asirvadam, Single trial source separation of VEP signals using selective principal components, IEE Medical Signal and Information Processing Conference, Malta GC, Sep Figure 6: EEG and EOG signals recorded from controls(sn).. 4. Conclusion The experimental results show that the extracted EOG signal peaks by filtering the recorded EEG of controls and alcoholics differ in numbers of strong EOG Peaks. Especially, the response of alcoholics to visual stimuli and the strength of their EOG are found inferior than the response and strength of controls. This is observed as all the amplitude heights in matching (SM) picture paradigm signals as weaker peaks for alcoholics. This simple experiment can replace the previous complex experiments [5] done to identify alcoholics from the controls. Moreover considering EOG as a feature component in temporal quantification for the identification is a novel idea suggested and proved in this work. This gives a possibility of developing, HCI systems that uses the EOG signals in future and too for controls. It is worth analyzing EOG for further similar experiments (for such task.) 5. Acknowledgement The authors wish to acknowledge the assistance of Prof. Henri Begleiter from the Neurodynamics Laboratory at the State University of New York, Health Center at Brooklyn, USA who recorded the raw VEP data and Mr. Paul Conlon, of Sasco Hill Research, USA for sharing the data. References [] Palaniappan.R., Anandan S., and Raveendran, P., Two Level PCA to Reduce Noise and EEG From Evoked Potential Signals, Proceedings of 7th International Conference on Control, Automation, Robotics and Vision, Singapore, pp , December -5. [] R. Barae, L. Boquete, M. Mazo, System for assited mobility using eye movements based on electrooculography, IEEE Transaction on Neural Systems and Rehabilitation Engineering, vol., 4, pp. 9-8, December. [4] S.Andrews, R.Palaniappan, and N.Kamel, 5. Single Trial VEP Source Separation by Selective Eigen Rate Principal Components, 5 th International Enformatika Conference, Prague, Czech Republic, pp.-,vol.7, 5 [5] P. Sharmila Kanna, Ramaswamy Palaniappan, K. V. R. Ravi, "Classification of Alcohol Abusers: An Intelligent Approach,", Third International Conference on Information Technology and Applications, (ICITA'5) vol., pp [6] S. Venkataramanan, P. Prabhat, S. R. Choudhury, H. B. Nemade, J. S. Sahambi, Biomedical instrumentation based on EOG signal processing and application to a hospital alarm system, Proc. of IEEE ICISI, Chennai, India, pp , 5 [7] S.Andrews, Nidal Kamel, and Ramasamy Palaniappan 5. Overcoming Accuracy Deficiency of Filterations in Source Separation of Visual Evoked Potentials by Adopting Principal Component Analysis. Proc.of International Science Congress (ISC), Malaysia, pp 44 [8] Z. Lv, X. Wu, M. Li, C. Zhang, Implementation of the EOG-based human computer interface system, The nd International Conference on Bioinformatics and Biomedical Engineering ICBBE 8, pp. 88-9,68 May 8. [9] D. Kumar, E. Poole, Classification of EOG for human computer interface, Proceedings of the Second Joint EMBS/BMES Conference Houston, vol, pp [] Polich, J, P in Clinical Applications: Meaning, Method and Measurement. American Journal of EEG Technology, vol., pp. -, 99. [] Y. Chen, W. S. Newman, A human- robot interface based on electrooculography, Robotics and Automation, 4. Proceedings. ICRA '4. 4 IEEE ISBN:

5 International Conference on, April 6 May 4, vol., pp [] Kriss, A, Recording Technique. In:Halliday, A.M. (ed) Evoked Potentials in Clinical Testing. Churchill Livingstone, 99. [] R. Palaniappan and P. Raveendran, Individual Identification Technique Using Visual Evoked Potential Signals, Electronics Letters, vol. 8, no. 5, pp. 6465,. [4] R. Palaniappan, Method of Identifying individuals Using VEP Signals and Neural Network, IEE Proc. Science, Measurement, and Technology, vol. 5, no., pp. 6-, 4. [5] R. Palaniappan and D.P. Mandic, Energy of Brain Potentials Evoked During Visual Stimulus: A New Biometric, Proc. Int l Conf. Artificial Neural Networks, W. Duch et al., eds., pp , 5. Table : Count of peaks in EEG signals on different subjects Channel# Subject X (alcoholic ) Subject Y (alcoholic) Peak Counts Subject W Subject Z Table : Comparison of the averages between alcoholics and controls Average EOG peak counts of subjects Controls EEG S.EEG (EOG) 5 Averag e peak counts of subjects Alcoholics EEG S. EEG (EOG) Total Average ISBN:

6 Table : Count of EOG peaks in Smoothened EEG signals on different subjects Channel # Subject X (alcoholic) Subject Y (alcoholic) Peak Counts Subject W Subject Z Total Average ISBN: