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Journal articles on the topic 'Electrooculogram'

To see the other types of publications on this topic, follow the link: Electrooculogram.

Author: Grafiati

Published: 4 June 2021

Last updated: 11 March 2023

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1

Liu, Jun, Guozheng Wang, Zhen-gang Cao, Fan Hang, Kangli Dong, and Delin Zhang. "The depth of anesthesia under sevoflurane can be monitored based on calling names language paradigm and Ag/AgCl electrooculogram electrode." Materials Express 12, no. 10 (October 1, 2022): 1315–22. http://dx.doi.org/10.1166/mex.2022.2269.

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In the depth of anesthesia monitoring, electrooculogram is usually treated as an artifact of EEG. However, the amplitude of the electrooculogram is much greater than that of electroencephalography. Its requirements for electrode materials and detection circuits are also far lower than electroencephalography. In this prospective cohort study, we use a new language paradigm that elicited rhythmic ocular muscle activity to explore whether the electrooculogram can be used for monitoring the depth of anesthesia. Thirty-three patients inhaled sevoflurane via a laryngeal mask for induction of anesthesia. According to the end-tidal sevoflurane concentration, patients were studied under three depth of sedation. Throughout all periods, the rhythmic calling of the patient’s own name script was sporadically played at a frequency of 0.5 Hz. We found that the electrooculogram was synchronized to the language paradigm during induction with sevoflurane, and the Ag/AgCl electrode has a significantly higher signal-to-noise ratio and 0.5 hz spectral peak value than other electrode. With the increase in the sevoflurane concentration, the synchronizing ability gradually weakened and eventually disappeared. Based on this evidence, we proposed a new method that can improve the performance of the anesthesia prediction monitoring model.

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2

Li, Shuai, Dongmei Hao, Bing Liu, Zhijie Yin, Lin Yang, and Jie Yu. "Evaluation of eyestrain with vertical electrooculogram." Computer Methods and Programs in Biomedicine 208 (September 2021): 106171. http://dx.doi.org/10.1016/j.cmpb.2021.106171.

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3

Ohn, Young-Hoon, Osamu Katsumi, Edilson Kruger-Leite, Elizabeth W. Larson, and Tatsuo Hirose. "Electrooculogram in Central Retinal Vein Obstruction." Ophthalmologica 203, no. 4 (1991): 189–95. http://dx.doi.org/10.1159/000310251.

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4

Lee, Kwang-Ryeol, Won-Du Chang, Sungkean Kim, and Chang-Hwan Im. "Real-Time “Eye-Writing” Recognition Using Electrooculogram." IEEE Transactions on Neural Systems and Rehabilitation Engineering 25, no. 1 (January 2017): 37–48. http://dx.doi.org/10.1109/tnsre.2016.2542524.

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5

Economou, Stratis G., and Costas N. Stefanis. "Changes of electrooculogram (EOG) in Parkinson's disease." Acta Neurologica Scandinavica 58, no. 1 (January 29, 2009): 44–52. http://dx.doi.org/10.1111/j.1600-0404.1978.tb02858.x.

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6

Nakanishi, Masaki, Yasue Mitsukura, Yijun Wang, Yu-Te Wang, and Tzyy-Ping Jung. "Online Voluntary Eye Blink Detection using Electrooculogram." IEICE Proceeding Series 1 (March 17, 2014): 114–17. http://dx.doi.org/10.15248/proc.1.114.

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7

Banerjee, Anwesha, Shreyasi Datta, Monalisa Pal, Amit Konar, D. N. Tibarewala, and R. Janarthanan. "Classifying Electrooculogram to Detect Directional Eye Movements." Procedia Technology 10 (2013): 67–75. http://dx.doi.org/10.1016/j.protcy.2013.12.338.

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8

Riemslag, F. C. C., H. F. E. Verduyn Lunel, and H. Spekreijse. "The electrooculogram: A refinement of the method." Documenta Ophthalmologica 73, no. 4 (December 1989): 369–75. http://dx.doi.org/10.1007/bf00154492.

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9

D’Souza, Sandra, and N. Sriraam. "Statistical Based Analysis of Electrooculogram (EOG) Signals." International Journal of Biomedical and Clinical Engineering 2, no. 1 (January 2013): 12–25. http://dx.doi.org/10.4018/ijbce.2013010102.

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The use of Electrooculogram (EOG) signals for developing Human-Computer Interfaces is increasing in the recent times. Several advantages including ease and flexibility in acquiring EOG signals have encouraged insight into EOG based research. In order to identify optimal features for EOG signals for rehabilitation applications, it is necessary to apply the statistical basis to decide the selection of best feature. This paper suggests a pilot study on non-parametric statistical based approach for analyzing EOG signals. This paper considers the detailed statistical analysis of Electrooculogram (EOG) signals. The EOG signals are acquired by considering the horizontal and vertical movements of the eye. The recording includes subjects with identified age groups with different activities. Power spectral densities based on Welch, Yule-Walker, Burg methods are estimated from the acquired EOG signals. Then non-parametric based statistical analysis is performed to show whether the gender or age of the subject influences the EOG signal obtained for different activities. The experimental results based on statistical analysis show that the raw data did not hold any significance to categorize male-female or age wise grouping. However, some features extracted set from the raw data provides useful statistical information which will be of great importance when used for selective rehabilitation.

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10

Fricke, Kyle, Robert Sobot, and Anestis Dounavis. "Analogue portable electrooculogram real-time signal processor." International Journal of Circuit Theory and Applications 42, no. 2 (September 21, 2012): 195–208. http://dx.doi.org/10.1002/cta.1848.

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11

Uchitomi, Hirotaka, and Junichi Hori. "Development of Pointing Device Using DC-Coupled Electrooculogram." IEEJ Transactions on Electronics, Information and Systems 129, no. 10 (2009): 1792–800. http://dx.doi.org/10.1541/ieejeiss.129.1792.

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12

Shirao, Y. "Differentiation of neovascular maculopathies by nonphotic electrooculogram responses." Japanese Journal of Ophthalmology 41, no. 3 (June 1997): 174–79. http://dx.doi.org/10.1016/s0021-5155(97)00026-9.

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13

Saravanakumar, D., and M. Ramasubba Reddy. "A virtual speller system using SSVEP and electrooculogram." Advanced Engineering Informatics 44 (April 2020): 101059. http://dx.doi.org/10.1016/j.aei.2020.101059.

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14

S., Dr Ramkumar, Dr Sathesh Kumar K., and Emayavaramban G. "Nine States HCI using Electrooculogram and Neural Networks." International Journal of Engineering and Technology 8, no. 6 (December 31, 2016): 3056–64. http://dx.doi.org/10.21817/ijet/2016/v8i6/160806249.

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15

Belov, D. P., S. Yu Eram, S. F. Kolodyazhnyi, I. E. Kanunikov, and O. V. Getmanenko. "Electrooculogram Detection of Eye Movements on Gaze Displacement." Neuroscience and Behavioral Physiology 40, no. 5 (May 14, 2010): 583–91. http://dx.doi.org/10.1007/s11055-010-9299-z.

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16

Thaler, Arnulf, Manfred R. Lessel, and Peter Heilig. "The slow electrooculogram oscillation in malignant choroidal melanoma." Documenta Ophthalmologica 71, no. 4 (April 1989): 403–7. http://dx.doi.org/10.1007/bf00152767.

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17

Korotenko, YA S., YU I. Yakymenko, A. O. Popov, YE S. Karplyuk, and M. A. Tsyparsʹkyy. "Electrooculographic brain-computer interface system." Electronics and Communications 16, no. 3 (March 28, 2011): 162–66. http://dx.doi.org/10.20535/2312-1807.2011.16.3.266312.

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Problems of brain-computer interface (BCI) systems construction are considered. The fundamentals of constructing BCI systems using the electrooculogram are presented. A method and software for determining eye rotation angle by a frequency-modulated electrooculographic signal processing are given

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18

., Vandhana. "A NOVEL EFFICIENT HUMAN COMPUTER INTERFACE USING AN ELECTROOCULOGRAM." International Journal of Research in Engineering and Technology 03, no. 04 (April 25, 2014): 799–803. http://dx.doi.org/10.15623/ijret.2014.0304140.

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19

Wajima, Ryohei, Salim B. Chater, Osamu Katsumi, Mehul C. Mehta, and Tatsuo Hirose. "Correlating Visual Acuity and Electrooculogram Recordings in Best’s Disease." Ophthalmologica 207, no. 4 (1993): 174–81. http://dx.doi.org/10.1159/000310429.

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20

Kim, Youngmin, Nakju Doh, Youngil Youm, and Wan Kyun Chung. "Development of Human-Mobile Communication System Using Electrooculogram Signals." IFAC Proceedings Volumes 34, no. 4 (May 2001): 245–50. http://dx.doi.org/10.1016/s1474-6670(17)34303-3.

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21

., Saravanan A. "LOW COST LOW POWER ELECTROOCULOGRAM BASED HUMAN MACHINE INTERFACE." International Journal of Research in Engineering and Technology 04, no. 26 (December 25, 2015): 47–50. http://dx.doi.org/10.15623/ijret.2015.0426010.

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22

Moon, Kee S., Sung Q. Lee, John S. Kang, Andrew Hnat, and Deepa B. Karen. "A Wireless Electrooculogram (EOG) Wearable Using Conductive Fiber Electrode." Electronics 12, no. 3 (January 23, 2023): 571. http://dx.doi.org/10.3390/electronics12030571.

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Electrooculography (EOG) is a technique for detecting electrical signals from the extra-ocular muscles. The EOG is a precise method for quantifying eye movements, including drowsiness-induced eye closure, and is also a promising technology for its potential use as a contributing mechanism for brain–computer interface applications. Despite the fact that EOG signals change as humans move their eyes, it is still difficult to monitor eye movement patterns in natural behaviors, such as everyday activity. Wearable convenience is essential for obtaining EOG signals while moving freely. This paper proposes the development and use of semi-dry electrodes with low impedance and excellent wearability, as well as a small, portable device with wireless communication capabilities, to increase the likelihood of use in real-life scenarios. The semi-dry electrode produced by the electrospinning technique had an impedance that was 3.5 times lower than that of the existing dry electrode and demonstrated low impedance drift even after long-term use. Furthermore, three steps of eye motion separation were performed using a signal obtained from the wearable device. It was confirmed that the classification of eye movements was at a meaningful level.

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23

Popov, A. A., A. M. Kanajkin, K. A. Roshchina, O. R. CHertov, and V. A. SHashkov. "Detection of artifacts in the EEG signal with using wavelet transform." Electronics and Communications 16, no. 4 (March 31, 2011): 126–30. http://dx.doi.org/10.20535/2312-1807.2011.16.4.245547.

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The paper considers the task of cleaning up the EEG signal from artifacts. Method for identifying electrooculogram and signal recovery after its removal using discrete wavelet transform of the electroencephalogram is proposed. The developed method showed nice results on examined examples of real signals at localization and removal of artifacts

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24

Jasim, Sarah Saadoon, and Alia Karim Abdul Hassan. "Driving sleepiness detection using electrooculogram analysis and grey wolf optimizer." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 6 (December 1, 2022): 6034. http://dx.doi.org/10.11591/ijece.v12i6.pp6034-6044.

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<span lang="EN-US">In modern society, providing safe and collision-free travel is essential. Therefore, detecting the drowsiness state of the driver before its ability to drive is compromised. For this purpose, an automated hybrid sleepiness classification system that combines the artificial neural network and gray wolf optimizer is proposed to distinguish human Sleepiness and fatigue. The proposed system is tested on data collected from 15 drivers (male and female) in alert and sleep-deprived conditions where physiological signals are used as sleep markers. To evaluate the performance of the proposed algorithm, k-nearest neighbors (k-NN), support vector machines (SVM), and artificial neural networks (ANN) classifiers have been used. The results show that the proposed hybrid method provides 99.6% accuracy, while the SVM classifier provides 93.0% accuracy when the kernel is (RBF) and outlier (0.1). Furthermore, the k-NN classifier provides 96.7% accuracy, whereas the standalone ANN algorithm provides 97.7% accuracy.</span>

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25

Banerjee, Anwesha, Monalisa Pal, Shreyasi Datta, D. N. Tibarewala, and Amit Konar. "Voluntary eye movement controlled electrooculogram based multitasking graphical user interface." International Journal of Biomedical Engineering and Technology 18, no. 3 (2015): 254. http://dx.doi.org/10.1504/ijbet.2015.070574.

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26

Lin, Chun-Ju, Chang-Hao Yang, Chung-May Yang, and Kuo-Pin Chang. "Abnormal electroretinogram and abnormal electrooculogram after lightning-induced ocular injury." American Journal of Ophthalmology 133, no. 4 (April 2002): 578–79. http://dx.doi.org/10.1016/s0002-9394(01)01400-3.

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27

Banerjee, Anwesha, Monalisa Pal, Shreyasi Datta, D. N. Tibarewala, and Amit Konar. "Eye movement sequence analysis using electrooculogram to assist autistic children." Biomedical Signal Processing and Control 14 (November 2014): 134–40. http://dx.doi.org/10.1016/j.bspc.2014.07.010.

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28

Song, Mengchuang, Lina Li, Jintao Guo, Tian Liu, Shuyin Li, Yingtuo Wang, Qurat ul ain, and Jue Wang. "A new method for muscular visual fatigue detection using electrooculogram." Biomedical Signal Processing and Control 58 (April 2020): 101865. http://dx.doi.org/10.1016/j.bspc.2020.101865.

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29

Usakli, A. B., S. Gurkan, F. Aloise, G. Vecchiato, and F. Babiloni. "On the Use of Electrooculogram for Efficient Human Computer Interfaces." Computational Intelligence and Neuroscience 2010 (2010): 1–5. http://dx.doi.org/10.1155/2010/135629.

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The aim of this study is to present electrooculogram signals that can be used for human computer interface efficiently. Establishing an efficient alternative channel for communication without overt speech and hand movements is important to increase the quality of life for patients suffering from Amyotrophic Lateral Sclerosis or other illnesses that prevent correct limb and facial muscular responses. We have made several experiments to compare the P300-based BCI speller and EOG-based new system. A five-letter word can be written on average in 25 seconds and in 105 seconds with the EEG-based device. Giving message such as “clean-up” could be performed in 3 seconds with the new system. The new system is more efficient than P300-based BCI system in terms of accuracy, speed, applicability, and cost efficiency. Using EOG signals, it is possible to improve the communication abilities of those patients who can move their eyes.

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30

Kellner, U., Claudia Jandeck, Hannelore Kraus, and Michael H. Foerster. "Autosomal dominant vitreoretinochoroidopathy with normal electrooculogram in a German family." Graefe's Archive for Clinical and Experimental Ophthalmology 236, no. 2 (January 23, 1998): 109–14. http://dx.doi.org/10.1007/s004170050050.

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31

Ishikawa, M., N. Kiryu, M. Ikoda, T. Yamaguro, A. Ebihara, G. Kusaka, and Y. Tanaka. "P490: Electrooculogram and trigeminal nerve evoked potentials during neurosurgical operations." Clinical Neurophysiology 125 (June 2014): S180—S181. http://dx.doi.org/10.1016/s1388-2457(14)50587-1.

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32

Thilagaraj, M., B. Dwarakanath, S. Ramkumar, K. Karthikeyan, A. Prabhu, Gurusamy Saravanakumar, M. Pallikonda Rajasekaran, and N. Arunkumar. "Eye Movement Signal Classification for Developing Human-Computer Interface Using Electrooculogram." Journal of Healthcare Engineering 2021 (December 8, 2021): 1–11. http://dx.doi.org/10.1155/2021/7901310.

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Human-computer interfaces (HCI) allow people to control electronic devices, such as computers, mouses, wheelchairs, and keyboards, by bypassing the biochannel without using motor nervous system signals. These signals permit communication between people and electronic-controllable devices. This communication is due to HCI, which facilitates lives of paralyzed patients who do not have any problems with their cognitive functioning. The major plan of this study is to test out the feasibility of nine states of HCI by using modern techniques to overcome the problem faced by the paralyzed. Analog Digital Instrument T26 with a five-electrode system was used in this method. Voluntarily twenty subjects participated in this study. The extracted signals were preprocessed by applying notch filter with a range of 50 Hz to remove the external interferences; the features were extracted by applying convolution theorem. Afterwards, extracted features were classified using Elman and distributed time delay neural network. Average classification accuracy with 90.82% and 90.56% was achieved using two network models. The accuracy of the classifier was analyzed by single-trial analysis and performances of the classifier were observed using bit transfer rate (BTR) for twenty subjects to check the feasibility of designing the HCI. The achieved results showed that the ERNN model has a greater potential to classify, identify, and recognize the EOG signal compared with distributed time delay network for most of the subjects. The control signal generated by classifiers was applied as control signals to navigate the assistive devices such as mouse, keyboard, and wheelchair activities for disabled people.

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33

Debbarma, Shibam, and Sharmistha Bhadra. "A Lightweight Flexible Wireless Electrooculogram Monitoring System With Printed Gold Electrodes." IEEE Sensors Journal 21, no. 18 (September 15, 2021): 20931–42. http://dx.doi.org/10.1109/jsen.2021.3095423.

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34

Sakurai, Keiko, Mingmin Yan, Koichi Tanno, and Hiroki Tamura. "Gaze Estimation Method Using Analysis of Electrooculogram Signals and Kinect Sensor." Computational Intelligence and Neuroscience 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/2074752.

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A gaze estimation system is one of the communication methods for severely disabled people who cannot perform gestures and speech. We previously developed an eye tracking method using a compact and light electrooculogram (EOG) signal, but its accuracy is not very high. In the present study, we conducted experiments to investigate the EOG component strongly correlated with the change of eye movements. The experiments in this study are of two types: experiments to see objects only by eye movements and experiments to see objects by face and eye movements. The experimental results show the possibility of an eye tracking method using EOG signals and a Kinect sensor.

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35

Sakurai, Keiko, Mingmin Yan, Hiroki Tamura, and Koichi Tanno. "A Study on Eyes Tracking Method using Analysis of Electrooculogram Signals." Proceedings of International Conference on Artificial Life and Robotics 22 (January 19, 2017): 524–27. http://dx.doi.org/10.5954/icarob.2017.os10-2.

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36

Mulam, Harikrishna, and Malini Mudigonda. "Optimised multi-wavelet domain for decomposed electrooculogram-based eye movement classification." IET Image Processing 14, no. 9 (July 20, 2020): 1862–69. http://dx.doi.org/10.1049/iet-ipr.2019.0277.

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37

Mollajafar, Mahyar, Seyed Mohammad Masoud Shushtarian, and Sogand Sahari. "Study of Probable Retinal Change in the Epileptic Patients using Electrooculogram." Biophysical Journal 104, no. 2 (January 2013): 330a. http://dx.doi.org/10.1016/j.bpj.2012.11.1831.

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38

Yu, Minzhong, Lezheng Wu, and Dezheng Wu. "Model parameters of the smooth pursuit eye movement system with electrooculogram." Documenta Ophthalmologica 76, no. 1 (November 1990): 37–46. http://dx.doi.org/10.1007/bf00140496.

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39

R�ver, J., and M. Bach. "C-wave versus electrooculogram in diseases of the retinal pigment epithelium." Documenta Ophthalmologica 65, no. 3 (March 1987): 385–91. http://dx.doi.org/10.1007/bf00149945.

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40

Garden, A. L. "Myogenic and scalp signals evoked by midinspiratory airway occlusion." Journal of Applied Physiology 89, no. 2 (August 1, 2000): 422–29. http://dx.doi.org/10.1152/jappl.2000.89.2.422.

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A somatosensory potential that is evoked by transient added inspiratory load has previously been described (Davenport PW, Friedman WA, Thompson FJ, and Franzen O. J Appl Physiol60: 1843–1848, 1986). This evoked potential is novel because it arises in response to a stimulus that also evokes a muscle response, and so this potential could contain myogenic components. The present study was undertaken to define the relationship between the scalp response and other physiological responses that are evoked by airway occlusion. Evoked signals were recorded from the scalp, scalenus anterior, masseter, and electrooculogram. Responses to a 200-ms midinspiratory occlusion were recorded in 12 healthy volunteers. Evoked responses were reliably recorded at C3-CZ and C4-CZ and from the skin overlying the scalenus anterior in 11 of these subjects. The onset latencies were 15.7 ± 3.1 at C3-CZ, 15.9 ± 2.1 at C4-CZ, and 17.6 ± 5.5 ms at scalenus anterior. In nine subjects, the masseter response appeared to coincide with the mouth pressure trace, and this was interpreted as movement artifact. No consistent electrooculogram or frontal electroencephalogram response was recorded. Because of the similarity in onset latency at C3-CZ, C4-CZ, and scalenus anterior, it was concluded that the myogenic signal may contribute to the scalp response and should be viewed as a potential source of artifact in experiments of this nature.

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41

Kuo, Terry B. J., and Cheryl C. H. Yang. "Frequency domain analysis of electrooculogram and its correlation with cardiac sympathetic function." Experimental Neurology 217, no. 1 (May 2009): 38–45. http://dx.doi.org/10.1016/j.expneurol.2009.01.012.

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42

Steinberg, R. H., R. A. Linsenmeier, and E. R. Griff. "Chapter 2 Retinal pigment epithelial cell contributions to the electroretinogram and electrooculogram." Progress in Retinal Research 4 (January 1985): 33–66. http://dx.doi.org/10.1016/0278-4327(85)90004-5.

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43

Takahashi, Yohji, Shoken Onoe, Namiko Asamizu, Toshiro Mori, Yuzuru Yoshimura, and Yutaka Tazawa. "Incomplete congenital stationary night blindness: Electroretinogram c-wave and electrooculogram light rise." Documenta Ophthalmologica 70, no. 1 (September 1988): 67–75. http://dx.doi.org/10.1007/bf00154737.

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44

Constable, Paul A. "A Perspective on the Mechanism of the Light-Rise of the Electrooculogram." Investigative Opthalmology & Visual Science 55, no. 4 (April 21, 2014): 2669. http://dx.doi.org/10.1167/iovs.14-13979.

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45

Teng, Geer, Yue He, Hengjun Zhao, Dunhu Liu, Jin Xiao, and S. Ramkumar. "DESIGN AND DEVELOPMENT OF HUMAN COMPUTER INTERFACE USING ELECTROOCULOGRAM WITH DEEP LEARNING." Artificial Intelligence in Medicine 102 (January 2020): 101765. http://dx.doi.org/10.1016/j.artmed.2019.101765.

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46

Zhang, Xiu, Takenao Sugi, Xingyu Wang, and Masatoshi Nakamura. "Real-time estimation system of the gaze position based on an electrooculogram." Artificial Life and Robotics 14, no. 2 (November 2009): 182–85. http://dx.doi.org/10.1007/s10015-009-0649-2.

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47

Boghez, Floriana, and Ioana Mandruta. "SCORING SLEEP: THE RULES FOR LOOKING INSIDE." Romanian Journal of Neurology 14, no. 3 (September 30, 2015): 119–24. http://dx.doi.org/10.37897/rjn.2015.3.2.

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Polysomnography is the most comprehensive sleep study, a multi-parametric recording test (electroencephalography, electrooculogram, chin and limbs electromyogram, respiratory and cardiac functions and permanent video-recording), used as an important diagnostic tool for the sleep disorders. Sleep architecture or the sleep macrostructure is a term used to describe the divisions of sleep into specific sleep stages using electro encephalographic (EEG), electrooculographic (EOG) and electromyographic (EMG) criteria: NREM (non-rapid eye movements) stages – N1, N2 and N3, and REM (rapid eye movements) stage.

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48

YAN, Mingmin, Hiroki TAMURA, and Koichi TANNO. "A Study on Gaze Estimation System of the Horizontal Angle Using Electrooculogram Signals." IEICE Transactions on Information and Systems E97.D, no. 9 (2014): 2330–37. http://dx.doi.org/10.1587/transinf.2013lop0018.

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49

Kim, Do, Jinuk Kwon, Joo-Young Kim, Ho-Seung Cha, Yong-Wook Kim, In Kim, and Chang-Hwan Im. "New Method for Pure-Tone Audiometry Using Electrooculogram: A Proof-of-Concept Study." Sensors 18, no. 11 (October 28, 2018): 3651. http://dx.doi.org/10.3390/s18113651.

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Precise and timely evaluation of an individual’s hearing loss plays an important role in determining appropriate treatment strategies, including medication and aural rehabilitation. However, currently available hearing assessment systems do not satisfy the need for an objective assessment tool with a simple and non-invasive procedure. In this paper, we propose a new method for pure-tone audiometry, which may potentially be used to assess an individual’s hearing ability objectively and quantitatively, without need for the user’s active response. The proposed method is based on the auditory oculogyric reflex, where the eyes involuntary rotate towards the source of a sound, in response to spatially moving pure-tone audio stimuli modulated at specific frequencies and intensities. We quantitatively analyzed horizontal electrooculograms (EOG) recorded with a pair of electrodes under two conditions—when pure-tone stimuli were (1) “inaudible” or (2) “audible” to a participant. Preliminary experimental results showed significantly increased EOG amplitude in the audible condition compared to the inaudible condition for all ten healthy participants. This demonstrates potential use of the proposed method as a new non-invasive hearing assessment tool.

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50

Nakanishi, Masaki, Kyohei Okugawa, Masaki Takahashi, and Yasue Mitsukura. "Voluntary Eye Blink Detection Using Electrooculogram for Controlling Powered Wheelchairs Considering Environmental Information." IEEJ Transactions on Electronics, Information and Systems 133, no. 10 (2013): 1969–75. http://dx.doi.org/10.1541/ieejeiss.133.1969.

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