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Artykuły w czasopismach na temat „Electrooculograms”

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Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 20 lutego 2023

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1

Becerra-García, Roberto A., Rodolfo García-Bermúdez i Gonzalo Joya. "Differentiation of Saccadic Eye Movement Signals". Sensors 21, nr 15 (24.07.2021): 5021. http://dx.doi.org/10.3390/s21155021.

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Saccadic electrooculograms are discrete biosignals that contain the instantaneous angular position of the human eyes as a response to saccadic visual stimuli. These signals are essential to monitor and evaluate several neurological diseases, such as Spinocerebellar Ataxia type 2 (SCA2). For this, biomarkers such as peak velocity, latency and duration are computed. To compute these biomarkers, we need to obtain the velocity profile of the signals using numerical differentiation methods. These methods are affected by the noise present in the electrooculograms, specially in subjects that suffer neurological diseases. This noise complicates the comparison of the differentiation methods using real saccadic signals because of the impossibility of establishing exact saccadic onset and offset points. In this work, we evaluate 16 differentiation methods by the design of an experiment that uses synthetic saccadic electrooculograms generated from parametric models of both healthy subjects and subjects suffering from Spinocerebellar Ataxia type 2 (SCA2). For these synthetic electrooculograms the exact velocity profile is known, hence we can use them as a reference for comparison and error computing for the tasks of saccade identification and saccade biomarker computing. Finally, we identify the best fitting method or methods for each evaluated task.

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Chang, Won-Du. "Electrooculograms for Human–Computer Interaction: A Review". Sensors 19, nr 12 (14.06.2019): 2690. http://dx.doi.org/10.3390/s19122690.

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Eye movements generate electric signals, which a user can employ to control his/her environment and communicate with others. This paper presents a review of previous studies on such electric signals, that is, electrooculograms (EOGs), from the perspective of human–computer interaction (HCI). EOGs represent one of the easiest means to estimate eye movements by using a low-cost device, and have been often considered and utilized for HCI applications, such as to facilitate typing on a virtual keyboard, moving a mouse, or controlling a wheelchair. The objective of this study is to summarize the experimental procedures of previous studies and provide a guide for researchers interested in this field. In this work the basic characteristics of EOGs, associated measurements, and signal processing and pattern recognition algorithms are briefly reviewed, and various applications reported in the existing literature are listed. It is expected that EOGs will be a useful source of communication in virtual reality environments, and can act as a valuable communication tools for people with amyotrophic lateral sclerosis.

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3

Ishii, Chiharu, Shunsuke Murooka i Minato Tajima. "Navigation of an Electric Wheelchair Using Electromyograms, Electrooculograms, and Electroencephalograms". International Journal of Mechanical Engineering and Robotics Research 7, nr 2 (2016): 143–49. http://dx.doi.org/10.18178/ijmerr.7.2.143-149.

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Ianov, Alexsandr Igorevitch, Hiroaki Kawamoto i Yoshiyuki Sankai. "Development of Hybrid Resistive-Capacitive Electrodes for Electroencephalograms and Electrooculograms". IEEJ Transactions on Sensors and Micromachines 133, nr 3 (2013): 57–65. http://dx.doi.org/10.1541/ieejsmas.133.57.

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5

Dasgupta, Anirban, i Aurobinda Routray. "Piecewise empirical mode Bayesian estimation – A new method to denoise electrooculograms". Biomedical Signal Processing and Control 70 (wrzesień 2021): 102945. http://dx.doi.org/10.1016/j.bspc.2021.102945.

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Chang, Won-Du, Ho-Seung Cha i Chang-Hwan Im. "Removing the Interdependency between Horizontal and Vertical Eye-Movement Components in Electrooculograms". Sensors 16, nr 2 (14.02.2016): 227. http://dx.doi.org/10.3390/s16020227.

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Sasaki, Tatsuya, Kyouichi Suzuki, Masato Matsumoto, Taku Sato, Namio Kodama i Keiko Yago. "Origin of surface potentials evoked by electrical stimulation of oculomotor nerves: are they related to electrooculographic or electromyographic events?" Journal of Neurosurgery 97, nr 4 (październik 2002): 941–44. http://dx.doi.org/10.3171/jns.2002.97.4.0941.

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Object. Evoked potentials elicited by electrical stimulation of the oculomotor nerve and recorded from surface electrodes placed on the skin around the eyeball reportedly originate in the eye and are represented on electrooculograms. Because evoked potentials recorded from surface electrodes are extremely similar to those of extraocular muscles, which are represented on electromyograms, the authors investigated the true origin of these potentials. Methods. Evoked potentials elicited by electrical stimulation of the canine oculomotor nerve were recorded from surface electrodes placed on the skin around the eyeball. A thread sutured to the center of the cornea was pulled and the potentials that were evoked during the resultant eye movement were recorded. These potentials were confirmed to originate in the eye and to be represented on electrooculograms because their waveforms were unaffected by the administration of muscle relaxant. To eliminate the influence of this source, the retina, a main origin of standing potentials of the eyeball, was removed. This resulted in the disappearance of electrooculography (EOG) waves elicited by eye movement. Surface potentials elicited by oculomotor nerve stimulation were the same before and after removal of the retina. Again the oculomotor nerve was electrically stimulated and electromyography (EMG) response of the extraocular muscles was recorded at the same time that potentials were recorded from the surface electrodes. In their peak latencies, amplitudes, and waveforms, the evoked potentials obtained from surface electrodes were almost identical to EMG responses of extraocular muscles. Conclusions. Evoked potentials elicited by electrical stimulation of the oculomotor nerves and obtained from surface electrodes originated from EMG responses of extraocular muscles. These evoked potentials do not derive from the eye.

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8

Noguchi, Kazuhito, Koichi Haishi i Daisuke Sato. "An Illusion of Velocity in Motion Perception". Perceptual and Motor Skills 78, nr 1 (luty 1994): 112–14. http://dx.doi.org/10.2466/pms.1994.78.1.112.

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We report a phenomenon that seems to have potential to elucidate a role of eye movement in motion perception. When tracking a target controlled by a triangular wave, the viewer perceives movement of the target like a ball bouncing in between two walls. We measured eye movement with electrooculograms (EOGs) when the subject was tracking a target controlled by a triangular wave. Eye movement after passing the turning point and rapidly returning to the target with saccadic movement and then smoothly tracking the target was recorded for all 4 adults. It was considered that extraretinal information on eye position during saccade may mainly contribute to this illusion.

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9

Chakraborty, Suvodip, Anirban Dasgupta i Aurobinda Routray. "Localization of eye Saccadic signatures in Electrooculograms using sparse representations with data driven dictionaries". Pattern Recognition Letters 139 (listopad 2020): 104–11. http://dx.doi.org/10.1016/j.patrec.2017.11.001.

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10

Li, Hao, Xia Mao i Lijiang Chen. "An emotion classification method from electroencephalogram based on 1/f fluctuation theory". Measurement and Control 53, nr 5-6 (24.04.2020): 824–32. http://dx.doi.org/10.1177/0020294020913893.

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Electroencephalogram data are easily affected by artifacts, and a drift may occur during the signal acquisition process. At present, most research focuses on the automatic detection and elimination of artifacts in electrooculograms, electromyograms and electrocardiograms. However, electroencephalogram drift data, which affect the real-time performance, are mainly manually calibrated and abandoned. An emotion classification method based on 1/f fluctuation theory is proposed to classify electroencephalogram data without removing artifacts and drift data. The results show that the proposed method can still achieve a great classification accuracy of 75% in cases in which artifacts and drift data exist when using the support vector machine classifier. In addition, the real-time performance of the proposed method is guaranteed.

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11

Kim, Do, Jinuk Kwon, Joo-Young Kim, Ho-Seung Cha, Yong-Wook Kim, In Kim i Chang-Hwan Im. "New Method for Pure-Tone Audiometry Using Electrooculogram: A Proof-of-Concept Study". Sensors 18, nr 11 (28.10.2018): 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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12

Stoean, Catalin, Ruxandra Stoean, Miguel Atencia, Moloud Abdar, Luis Velázquez-Pérez, Abbas Khosravi, Saeid Nahavandi, U. Rajendra Acharya i Gonzalo Joya. "Automated Detection of Presymptomatic Conditions in Spinocerebellar Ataxia Type 2 Using Monte Carlo Dropout and Deep Neural Network Techniques with Electrooculogram Signals". Sensors 20, nr 11 (27.05.2020): 3032. http://dx.doi.org/10.3390/s20113032.

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Application of deep learning (DL) to the field of healthcare is aiding clinicians to make an accurate diagnosis. DL provides reliable results for image processing and sensor interpretation problems most of the time. However, model uncertainty should also be thoroughly quantified. This paper therefore addresses the employment of Monte Carlo dropout within the DL structure to automatically discriminate presymptomatic signs of spinocerebellar ataxia type 2 in saccadic samples obtained from electrooculograms. The current work goes beyond the common incorporation of this special type of dropout into deep neural networks and uses the uncertainty derived from the validation samples to construct a decision tree at the register level of the patients. The decision tree built from the uncertainty estimates obtained a classification accuracy of 81.18% in automatically discriminating control, presymptomatic and sick classes. This paper proposes a novel method to address both uncertainty quantification and explainability to develop reliable healthcare support systems.

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13

Hollowell, D. E., i P. M. Suratt. "Mandible position and activation of submental and masseter muscles during sleep". Journal of Applied Physiology 71, nr 6 (1.12.1991): 2267–73. http://dx.doi.org/10.1152/jappl.1991.71.6.2267.

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Movement of the mandible could influence pharyngeal airway caliber because the mandible is attached to the tongue and to muscles that insert on the hyoid bone. In normal subjects and patients with obstructive sleep apnea (OSA) we measured jaw position during sleep with strain gauges, as well as masseter and submental electromyograms, airflow, esophageal pressure, oximetry, electroencephalograms, and electrooculograms. Jaws of patients with OSA were open more than those of normal subjects at end expiration and opened further at end inspiration, particularly at the termination of apneas when the masseter and submental muscles contracted. Masseter activation occurred only in patients with OSA and in a pattern similar to that of submental muscles. Jaw opening at end expiration could narrow the upper airway, whereas opening at end inspiration could reflect efforts to expand the airway with tracheal tug and with submental muscle activation and efforts to open the mouth to allow mouth breathing. Masseter contraction does not close the jaw but may serve to stabilize it.

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14

Ban, Seunghyeb, Yoon Jae Lee, Ka Ram Kim, Jong-Hoon Kim i Woon-Hong Yeo. "Advances in Materials, Sensors, and Integrated Systems for Monitoring Eye Movements". Biosensors 12, nr 11 (17.11.2022): 1039. http://dx.doi.org/10.3390/bios12111039.

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Eye movements show primary responses that reflect humans' voluntary intention and conscious selection. Because visual perception is one of the fundamental sensory interactions in the brain, eye movements contain critical information regarding physical/psychological health, perception, intention, and preference. With the advancement of wearable device technologies, the performance of monitoring eye tracking has been significantly improved. It also has led to myriad applications for assisting and augmenting human activities. Among them, electrooculograms, measured by skin-mounted electrodes, have been widely used to track eye motions accurately. In addition, eye trackers that detect reflected optical signals offer alternative ways without using wearable sensors. This paper outlines a systematic summary of the latest research on various materials, sensors, and integrated systems for monitoring eye movements and enabling human-machine interfaces. Specifically, we summarize recent developments in soft materials, biocompatible materials, manufacturing methods, sensor functions, systems' performances, and their applications in eye tracking. Finally, we discuss the remaining challenges and suggest research directions for future studies.

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15

Fu, Yulin, Jingjing Zhao, Ying Dong i Xiaohao Wang. "Dry Electrodes for Human Bioelectrical Signal Monitoring". Sensors 20, nr 13 (29.06.2020): 3651. http://dx.doi.org/10.3390/s20133651.

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Bioelectrical or electrophysiological signals generated by living cells or tissues during daily physiological activities are closely related to the state of the body and organ functions, and therefore are widely used in clinical diagnosis, health monitoring, intelligent control and human-computer interaction. Ag/AgCl electrodes with wet conductive gels are widely used to pick up these bioelectrical signals using electrodes and record them in the form of electroencephalograms, electrocardiograms, electromyography, electrooculograms, etc. However, the inconvenience, instability and infection problems resulting from the use of gel with Ag/AgCl wet electrodes can’t meet the needs of long-term signal acquisition, especially in wearable applications. Hence, focus has shifted toward the study of dry electrodes that can work without gels or adhesives. In this paper, a retrospective overview of the development of dry electrodes used for monitoring bioelectrical signals is provided, including the sensing principles, material selection, device preparation, and measurement performance. In addition, the challenges regarding the limitations of materials, fabrication technologies and wearable performance of dry electrodes are discussed. Finally, the development obstacles and application advantages of different dry electrodes are analyzed to make a comparison and reveal research directions for future studies.

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16

Zennifa, Fadilla, Sho Ageno, Shota Hatano i Keiji Iramina. "Hybrid System for Engagement Recognition During Cognitive Tasks Using a CFS + KNN Algorithm". Sensors 18, nr 11 (30.10.2018): 3691. http://dx.doi.org/10.3390/s18113691.

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Engagement is described as a state in which an individual involved in an activity can ignore other influences. The engagement level is important to obtaining good performance especially under study conditions. Numerous methods using electroencephalograph (EEG), electrocardiograph (ECG), and near-infrared spectroscopy (NIRS) for the recognition of engagement have been proposed. However, the results were either unsatisfactory or required many channels. In this study, we introduce the implementation of a low-density hybrid system for engagement recognition. We used a two-electrode wireless EEG, a wireless ECG, and two wireless channels NIRS to measure engagement recognition during cognitive tasks. We used electrooculograms (EOG) and eye tracking to record eye movements for data labeling. We calculated the recognition accuracy using the combination of correlation-based feature selection and k-nearest neighbor algorithm. Following that, we did a comparative study against a stand-alone system. The results show that the hybrid system had an acceptable accuracy for practical use (71.65 ± 0.16%). In comparison, the accuracy of a pure EEG system was (65.73 ± 0.17%), pure ECG (67.44 ± 0.19%), and pure NIRS (66.83 ± 0.17%). Overall, our results demonstrate that the proposed method can be used to improve performance in engagement recognition.

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17

Ogata, Shigeki. "Human Eeg Responses to Classical Music and Simulated White Noise: Effects of a Musical Loudness Component on Consciousness". Perceptual and Motor Skills 80, nr 3 (czerwiec 1995): 779–90. http://dx.doi.org/10.2466/pms.1995.80.3.779.

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The main purpose of the present study was to investigate the psychophysiological effects of music on human EEG. For this purpose, a sound modulator was developed which simulates the sound-pressure variations of a given piece of music by white noise (sim-music). Using this apparatus, the author tested the psychophysiological effects of music on human EEG. The electroencephalograms (EEG), electrocardiograms (ECG), and electrooculograms (EOG) of eight normal volunteers were recorded for a total of 21 min., 5 sec. per session for each subject under three sound conditions: silence for 5 min., two types of music (music) or two types of simulated noise (sim-music) for 11 min., 5 sec., followed by silence for another 5 min. Each subject was exposed to a total of 10 music and 10 sim-music conditions. At the low consciousness level (drowsiness, Stage S1), higher delta component power densities were observed with sim-music than with music. Thus, even in the same Stage S1, entire physiological consciousness levels may be higher when listening to music than to sim-music. While listening to music, many subjects reported that they felt pleasantly relaxed or comfortable. However, with the sim-music, they reported feeling unpleasantly weary and sleepy. It seems that the mental set toward two sound conditions differed greatly for many subjects. In Stage S1, the differences in EEG slow components showed that the differences in consciousness had a physiological aspect and indicated differences in mental set toward both sound conditions and mental activity during the listening conditions.

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Liu, Li, Yunfeng Ji, Yun Gao, Zhenyu Ping, Liang Kuang, Tao Li i Wei Xu. "A Novel Fatigue Driving State Recognition and Warning Method Based on EEG and EOG Signals". Journal of Healthcare Engineering 2021 (22.11.2021): 1–10. http://dx.doi.org/10.1155/2021/7799793.

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Traffic accidents are easily caused by tired driving. If the fatigue state of the driver can be identified in time and a corresponding early warning can be provided, then the occurrence of traffic accidents could be avoided to a large extent. At present, the recognition of fatigue driving states is mostly based on recognition accuracy. Fatigue state is currently recognized by combining different features, such as facial expressions, electroencephalogram (EEG) signals, yawning, and the percentage of eyelid closure over the pupil over time (PERCLoS). The combination of these features increases the recognition time and lacks real-time performance. In addition, some features will increase error in the recognition result, such as yawning frequently with the onset of a cold or frequent blinking with dry eyes. On the premise of ensuring the recognition accuracy and improving the realistic feasibility and real-time recognition performance of fatigue driving states, a fast support vector machine (FSVM) algorithm based on EEGs and electrooculograms (EOGs) is proposed to recognize fatigue driving states. First, the collected EEG and EOG modal data are preprocessed. Second, multiple features are extracted from the preprocessed EEGs and EOGs. Finally, FSVM is used to classify and recognize the data features to obtain the recognition result of the fatigue state. Based on the recognition results, this paper designs a fatigue driving early warning system based on Internet of Things (IoT) technology. When the driver shows symptoms of fatigue, the system not only sends a warning signal to the driver but also informs other nearby vehicles using this system through IoT technology and manages the operation background.

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Liu, Jun, Guozheng Wang, Zhen-gang Cao, Fan Hang, Kangli Dong i 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, nr 10 (1.10.2022): 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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Li, Shuai, Dongmei Hao, Bing Liu, Zhijie Yin, Lin Yang i Jie Yu. "Evaluation of eyestrain with vertical electrooculogram". Computer Methods and Programs in Biomedicine 208 (wrzesień 2021): 106171. http://dx.doi.org/10.1016/j.cmpb.2021.106171.

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Ohn, Young-Hoon, Osamu Katsumi, Edilson Kruger-Leite, Elizabeth W. Larson i Tatsuo Hirose. "Electrooculogram in Central Retinal Vein Obstruction". Ophthalmologica 203, nr 4 (1991): 189–95. http://dx.doi.org/10.1159/000310251.

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Lee, Kwang-Ryeol, Won-Du Chang, Sungkean Kim i Chang-Hwan Im. "Real-Time “Eye-Writing” Recognition Using Electrooculogram". IEEE Transactions on Neural Systems and Rehabilitation Engineering 25, nr 1 (styczeń 2017): 37–48. http://dx.doi.org/10.1109/tnsre.2016.2542524.

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Economou, Stratis G., i Costas N. Stefanis. "Changes of electrooculogram (EOG) in Parkinson's disease". Acta Neurologica Scandinavica 58, nr 1 (29.01.2009): 44–52. http://dx.doi.org/10.1111/j.1600-0404.1978.tb02858.x.

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Nakanishi, Masaki, Yasue Mitsukura, Yijun Wang, Yu-Te Wang i Tzyy-Ping Jung. "Online Voluntary Eye Blink Detection using Electrooculogram". IEICE Proceeding Series 1 (17.03.2014): 114–17. http://dx.doi.org/10.15248/proc.1.114.

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Banerjee, Anwesha, Shreyasi Datta, Monalisa Pal, Amit Konar, D. N. Tibarewala i 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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Riemslag, F. C. C., H. F. E. Verduyn Lunel i H. Spekreijse. "The electrooculogram: A refinement of the method". Documenta Ophthalmologica 73, nr 4 (grudzień 1989): 369–75. http://dx.doi.org/10.1007/bf00154492.

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D’Souza, Sandra, i N. Sriraam. "Statistical Based Analysis of Electrooculogram (EOG) Signals". International Journal of Biomedical and Clinical Engineering 2, nr 1 (styczeń 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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Fricke, Kyle, Robert Sobot i Anestis Dounavis. "Analogue portable electrooculogram real-time signal processor". International Journal of Circuit Theory and Applications 42, nr 2 (21.09.2012): 195–208. http://dx.doi.org/10.1002/cta.1848.

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Uchitomi, Hirotaka, i Junichi Hori. "Development of Pointing Device Using DC-Coupled Electrooculogram". IEEJ Transactions on Electronics, Information and Systems 129, nr 10 (2009): 1792–800. http://dx.doi.org/10.1541/ieejeiss.129.1792.

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Shirao, Y. "Differentiation of neovascular maculopathies by nonphotic electrooculogram responses". Japanese Journal of Ophthalmology 41, nr 3 (czerwiec 1997): 174–79. http://dx.doi.org/10.1016/s0021-5155(97)00026-9.

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

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S., Dr Ramkumar, Dr Sathesh Kumar K. i Emayavaramban G. "Nine States HCI using Electrooculogram and Neural Networks". International Journal of Engineering and Technology 8, nr 6 (31.12.2016): 3056–64. http://dx.doi.org/10.21817/ijet/2016/v8i6/160806249.

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Belov, D. P., S. Yu Eram, S. F. Kolodyazhnyi, I. E. Kanunikov i O. V. Getmanenko. "Electrooculogram Detection of Eye Movements on Gaze Displacement". Neuroscience and Behavioral Physiology 40, nr 5 (14.05.2010): 583–91. http://dx.doi.org/10.1007/s11055-010-9299-z.

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Thaler, Arnulf, Manfred R. Lessel i Peter Heilig. "The slow electrooculogram oscillation in malignant choroidal melanoma". Documenta Ophthalmologica 71, nr 4 (kwiecień 1989): 403–7. http://dx.doi.org/10.1007/bf00152767.

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Korotenko, YA S., YU I. Yakymenko, A. O. Popov, YE S. Karplyuk i M. A. Tsyparsʹkyy. "Electrooculographic brain-computer interface system". Electronics and Communications 16, nr 3 (28.03.2011): 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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., Vandhana. "A NOVEL EFFICIENT HUMAN COMPUTER INTERFACE USING AN ELECTROOCULOGRAM". International Journal of Research in Engineering and Technology 03, nr 04 (25.04.2014): 799–803. http://dx.doi.org/10.15623/ijret.2014.0304140.

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Wajima, Ryohei, Salim B. Chater, Osamu Katsumi, Mehul C. Mehta i Tatsuo Hirose. "Correlating Visual Acuity and Electrooculogram Recordings in Best’s Disease". Ophthalmologica 207, nr 4 (1993): 174–81. http://dx.doi.org/10.1159/000310429.

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Kim, Youngmin, Nakju Doh, Youngil Youm i Wan Kyun Chung. "Development of Human-Mobile Communication System Using Electrooculogram Signals". IFAC Proceedings Volumes 34, nr 4 (maj 2001): 245–50. http://dx.doi.org/10.1016/s1474-6670(17)34303-3.

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., Saravanan A. "LOW COST LOW POWER ELECTROOCULOGRAM BASED HUMAN MACHINE INTERFACE". International Journal of Research in Engineering and Technology 04, nr 26 (25.12.2015): 47–50. http://dx.doi.org/10.15623/ijret.2015.0426010.

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Moon, Kee S., Sung Q. Lee, John S. Kang, Andrew Hnat i Deepa B. Karen. "A Wireless Electrooculogram (EOG) Wearable Using Conductive Fiber Electrode". Electronics 12, nr 3 (23.01.2023): 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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Popov, A. A., A. M. Kanajkin, K. A. Roshchina, O. R. CHertov i V. A. SHashkov. "Detection of artifacts in the EEG signal with using wavelet transform". Electronics and Communications 16, nr 4 (31.03.2011): 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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Jasim, Sarah Saadoon, i Alia Karim Abdul Hassan. "Driving sleepiness detection using electrooculogram analysis and grey wolf optimizer". International Journal of Electrical and Computer Engineering (IJECE) 12, nr 6 (1.12.2022): 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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Banerjee, Anwesha, Monalisa Pal, Shreyasi Datta, D. N. Tibarewala i Amit Konar. "Voluntary eye movement controlled electrooculogram based multitasking graphical user interface". International Journal of Biomedical Engineering and Technology 18, nr 3 (2015): 254. http://dx.doi.org/10.1504/ijbet.2015.070574.

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Lin, Chun-Ju, Chang-Hao Yang, Chung-May Yang i Kuo-Pin Chang. "Abnormal electroretinogram and abnormal electrooculogram after lightning-induced ocular injury". American Journal of Ophthalmology 133, nr 4 (kwiecień 2002): 578–79. http://dx.doi.org/10.1016/s0002-9394(01)01400-3.

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

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

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Usakli, A. B., S. Gurkan, F. Aloise, G. Vecchiato i 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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Kellner, U., Claudia Jandeck, Hannelore Kraus i Michael H. Foerster. "Autosomal dominant vitreoretinochoroidopathy with normal electrooculogram in a German family". Graefe's Archive for Clinical and Experimental Ophthalmology 236, nr 2 (23.01.1998): 109–14. http://dx.doi.org/10.1007/s004170050050.

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

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Thilagaraj, M., B. Dwarakanath, S. Ramkumar, K. Karthikeyan, A. Prabhu, Gurusamy Saravanakumar, M. Pallikonda Rajasekaran i N. Arunkumar. "Eye Movement Signal Classification for Developing Human-Computer Interface Using Electrooculogram". Journal of Healthcare Engineering 2021 (8.12.2021): 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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