Gotowe bibliografie tematyczne / Electrooculogram

Gotowa bibliografia na temat „Electrooculogram”

Autor: Grafiati
Data publikacji: 4 czerwca 2021
Data aktualizacji: 11 marca 2023

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Artykuły w czasopismach na temat "Electrooculogram"

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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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Rozprawy doktorskie na temat "Electrooculogram"

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Ma, Jiaxin. "Research on Human-Machine Interfaces of Vigilance Estimation and Robot Control based on Biomedical Signals". 京都大学 (Kyoto University), 2015. http://hdl.handle.net/2433/199268.

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Coughlin, Michael J., i n/a. "Calibration of Two Dimensional Saccadic Electro-Oculograms Using Artificial Neural Networks". Griffith University. School of Applied Psychology, 2003. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20030409.110949.

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The electro-oculogram (EOG) is the most widely used technique for recording eye movements in clinical settings. It is inexpensive, practical, and non-invasive. Use of EOG is usually restricted to horizontal recordings as vertical EOG contains eyelid artefact (Oster & Stern, 1980) and blinks. The ability to analyse two dimensional (2D) eye movements may provide additional diagnostic information on pathologies, and further insights into the nature of brain functioning. Simultaneous recording of both horizontal and vertical EOG also introduces other difficulties into calibration of the eye movements, such as different gains in the two signals, and misalignment of electrodes producing crosstalk. These transformations of the signals create problems in relating the two dimensional EOG to actual rotations of the eyes. The application of an artificial neural network (ANN) that could map 2D recordings into 2D eye positions would overcome this problem and improve the utility of EOG. To determine whether ANNs are capable of correctly calibrating the saccadic eye movement data from 2D EOG (i.e. performing the necessary inverse transformation), the ANNs were first tested on data generated from mathematical models of saccadic eye movements. Multi-layer perceptrons (MLPs) with non-linear activation functions and trained with back propagation proved to be capable of calibrating simulated EOG data to a mean accuracy of 0.33° of visual angle (SE = 0.01). Linear perceptrons (LPs) were only nearly half as accurate. For five subjects performing a saccadic eye movement task in the upper right quadrant of the visual field, the mean accuracy provided by the MLPs was 1.07° of visual angle (SE = 0.01) for EOG data, and 0.95° of visual angle (SE = 0.03) for infrared limbus reflection (IRIS®) data. MLPs enabled calibration of 2D saccadic EOG to an accuracy not significantly different to that obtained with the infrared limbus tracking data.
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Coughlin, Michael J. "Calibration of Two Dimensional Saccadic Electro-Oculograms Using Artificial Neural Networks". Thesis, Griffith University, 2003. http://hdl.handle.net/10072/365854.

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The electro-oculogram (EOG) is the most widely used technique for recording eye movements in clinical settings. It is inexpensive, practical, and non-invasive. Use of EOG is usually restricted to horizontal recordings as vertical EOG contains eyelid artefact (Oster & Stern, 1980) and blinks. The ability to analyse two dimensional (2D) eye movements may provide additional diagnostic information on pathologies, and further insights into the nature of brain functioning. Simultaneous recording of both horizontal and vertical EOG also introduces other difficulties into calibration of the eye movements, such as different gains in the two signals, and misalignment of electrodes producing crosstalk. These transformations of the signals create problems in relating the two dimensional EOG to actual rotations of the eyes. The application of an artificial neural network (ANN) that could map 2D recordings into 2D eye positions would overcome this problem and improve the utility of EOG. To determine whether ANNs are capable of correctly calibrating the saccadic eye movement data from 2D EOG (i.e. performing the necessary inverse transformation), the ANNs were first tested on data generated from mathematical models of saccadic eye movements. Multi-layer perceptrons (MLPs) with non-linear activation functions and trained with back propagation proved to be capable of calibrating simulated EOG data to a mean accuracy of 0.33° of visual angle (SE = 0.01). Linear perceptrons (LPs) were only nearly half as accurate. For five subjects performing a saccadic eye movement task in the upper right quadrant of the visual field, the mean accuracy provided by the MLPs was 1.07° of visual angle (SE = 0.01) for EOG data, and 0.95° of visual angle (SE = 0.03) for infrared limbus reflection (IRIS®) data. MLPs enabled calibration of 2D saccadic EOG to an accuracy not significantly different to that obtained with the infrared limbus tracking data.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Applied Psychology
Griffith Health
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DERCHI, CHIARA CAMILLA. "BEHIND AN EYE BLINK: A NEW EMPIRICAL PERSPECTIVE ON INTENTIONAL ACTION". Doctoral thesis, Università degli Studi di Milano, 2018. http://hdl.handle.net/2434/555411.

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Il “blink” o “batter d’occhi” è un movimento di rapida chiusura e riapertura delle palpebre. Il “blink” può essere un movimento spontaneo, riflesso o volontario. “Blink” con identiche caratteristiche cinematiche possono avere differenti origini e significati. Per esempio, un blink può essere spontaneo quando ha la funzione fisiologica di creare un film lacrimale evitando la seccazione della cornea, può essere riflesso in riposta a stimoli esterni ed infine può essere volontario per comunicare un messaggio attraverso un canale comunicativo preservato, per esempio quando un paziente locked in cerca di comunicare gioia, accordo o disaccordo, frustrazione attraverso gli occhi (Laureys et al., 2005). Lo scopo principale di questo studio è stato quello di trovare una misura oggettiva relativa alla distinzione tra un blink spontaneo e uno volontario: il “potenziale di preparazione” (Readiness Potential). Il presente studio è quindi rilevante per due ragioni: 1. Nei soggetti sani, i blink spontanei appaiono con una frequenza di circa 1 ogni 5 secondi. Allo stesso tempo, i soggetti sani possono “controllare” il movimento spontaneo e riprodurlo in maniera intenzionale se opportunamente istruiti. In questo modo, il “blink” o “ammiccamento oculare” offre un contrasto ideale tra atto conscio e inconscio, a parità di cinematica. In questa prospettiva, l’analisi dell’attività cerebrale che precede un atto spontaneo o automatico messa a confronto con l’attività che precedere un “blink” volontario può offrire uno sguardo unico sui correlati neurali di un atto cosciente. 2. Nei pazienti con gravi cerebrolesioni, il “blink” è spesso l’unico atto motorio che può essere individuato. È infatti impossibile per molti pazienti effettuare movimenti più complessi. Per questo motivo, attraverso un condizionamento operante in cui ad uno specifico comportamento viene associato un rinforzo positivo, il nostro scopo è quello di indirizzare i pazienti ad associare un determinato tipo di “blink”, opportunamente selezionato, con un rinforzo positivo rappresentato da voci familiari/amiche che si suppone possano avere una valenza emotiva positiva per il paziente. Nella prima parte della tesi verranno introdotte le premesse teorico/sperimentali alla base dello studio e verranno presentati i materiali e metodi e i risultati relativi alla popolazione di controllo (soggetti sani). Nella seconda parte, verrà introdotto il “disturbo di coscienza” dal punto di vista clinico, il nuovo protocollo sperimentale applicato ai pazienti con disturbo di coscienza e i risultati preliminari. In conclusione, verranno valutate le potenzialità dello studio da un punto di vista teorico, da un punto di vista clinico/riabilitativo ed infine da un punto di vista etico.
Blinking is a rapid closing and opening of the eyelid. Eye blinks with identical kinematical features can have different origins and meanings. For example, one can blink automatically, due to a simple reflex arc – such as when moistening the cornea – or one can blink voluntarily to communicate a fundamental message – such as when a locked-in patient communicates that he/she is happy or frustrated (Laureys, et al., 2005) The main aim of the present project is to find a brain-based objective way to know whether a given blink is a meaningless automatic neural event or the endpoint of a complex conscious process. The proposal builds up on the empirical work by Kornhuber & Deecke and Benjamin Libet, who showed that the awareness of intention to move is preceded by a recordable cerebral activity called “Readiness Potential”. The present proposal is relevant for two reasons: 1. In healthy subjects, automatic blinking occurs spontaneously every 5 seconds, or so. At the same time, healthy subjects can be instructed to blink voluntarily in a controlled fashion. In this way, blinking offers the ideal contrast between unconscious and conscious acts – the physical, kinematic aspects of the movement being equal. In this perspective, analyzing brain activity prior to automatic and voluntary blinks may offer a unique insight on the neural correlates of a conscious act. 2. In patients with severe brain injuries blinking is often the only motor act that can be reliably detected. By employing operant conditioning, we aim at training patients on the association between a specific eyelid closure and a positive reinforcement. Specifically, Readiness Potential like activity will be computed on the cortical activity preceding eye blinking as a measure of “volition,” first in healthy controls and then in vegetative and minimally conscious state patients undergoing operant conditioning. In healthy controls, we will contrast spontaneous blinks against voluntary blinks. The results of this experiment are meant to explore the dynamic range of the changes in brain activity that underlies voluntary vs. spontaneous blinks in controlled conditions. In patients, detecting a progressive increase in the strength or complexity of brain activity (up to the levels obtained in healthy subjects during voluntary blinks) during the course of the conditioning sessions will indicate that their blinking might reflect a voluntary act. Ultimately, this project, if successful, will link operant conditioning to the long-standing topic of the neural substrates of a wilful decision to act, bearing important scientific/ethical implications. The novelty of this project rests on: a. Exploring, empirically, the relationships between brain activity and the will. The underlying hypothesis guiding this project is that a wilful act should be reflected, to some measurable degree, in high levels of anticipatory brain dynamics. b. Taking Libet’s work one-step forward, by using slow cortical potentials such as the “Readiness Potential” as a neural marker of volition. c. Using the “Readiness Potential” to distinguish between spontaneous and voluntary blinks. d. Answering the critical question of whether the blinks produced by vegetative patients after a conditioning protocol are voluntary or not.
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Young, Chieh-neng, i 楊傑能. "Electrooculogram Signals for the Detection of REM Sleep Via VQ Methods". Thesis, 2007. http://ndltd.ncl.edu.tw/handle/h372fr.

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博士
國立中山大學
機械與機電工程學系研究所
95
One primary topic of sleep studies is the depth of sleep. According to definitions of R&K rules, human sleep can be roughly divided into three different stages: Awake, Non-rapid-eye-movement (NREM) Sleep, and Rapid-eye-movement (REM) Sleep. Moreover, sleep stages are scored mainly by EEG signals and complementally by EOG and EMG signals. Many researchers have indicated that diseases or disorders occur during sleep will affect life quality of patients. For example, REM sleep-related dyssomnia is highly correlated with neurodegenerative or mental disorders such as major depression. Furthermore, sleep apnea is one of the most common sleep disorders at present. Untreated sleep apnea can increase the risk of mental and cardiovascular diseases. This research proposes a detection method of REM sleep. Take into account the environment of homecare, we just extract and analyze EOG signals for the sake of convenience in comparison with EEG channels. By analyzing elementary waveforms of EOG signals based on VQ method, the proposed method performs a classification accuracy of 67.71% in a group application. The corresponding sensitivity and specificity are 73.38% and 68.95% respectively. In contrast, the average classification accuracy is 82.02% in personalized applications. And the corresponding average sensitivity and specificity are 83.05% and 81.62% respectively. Experimental results demonstrate the feasibility of detecting REM sleep via the proposed method, especially in personalized applications. This will be propitious to a long term tracing and research of personal sleep status.
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Chen, Hsiaw-Shuw, i 陳孝壽. "THE STUDY OF RELATIONSHIP BETWEEN ELECTROOCULOGRAM AND THE FEATURES OF CLOSE EYE VIDEO IMAGES". Thesis, 2008. http://ndltd.ncl.edu.tw/handle/66898671229447341934.

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碩士
國立清華大學
產業研發碩士積體電路設計專班
96
The brain scientific research can be regarded as one of the contemporary popular studies in recent years. The integration of biology, medicine, physics, electrical and information engineering has resulted in a substantial development in brain related researches and applications. For example, there were breakthrough progresses in the researches on sleeping status, brain waves status, and excitatory zone of cortex, etc. In sleep studies, the majority of sleep measurements are conducted by using invasive sensing approaches which will more or less disturb the sleep. It’s natural to ask whether there exists a noninvasive approach that is not only cheaper and non-contact sensing, but also able to obtain the corresponding physiological signal. By looking at the physiological signals comprehensively, we discovered that most values of theirs strength are in μV or weaker if in a form of voltage signal; in addition, they are even weaker and difficult to measure if in the magnetic field signal form because of the difficulty of screening. However, the signal of Electrooculogram (EOG), with its stronger signal strength (in mV level), and related to the sleeping status, is frequently adopted along with other physiological measurements in the sleep study. If it is possible to use the remote sensing technique to acquire the EOG signal, a non-invasive and cheap approach of monitoring sleep may be obtained then. Therefore, this study is emphasized on the possibility of using the computer vision method to establish the function of EOG signal obtained from the traditional electrode. In order to develop the computer vision EOG, we have to seek out the correlation between the EOG and features of eye images obtained form computer vision. We thus utilized the digital image processing techniques to find out the image features of eye movement under close eye condition that related to the EOG. In pre-processing stage, we determined the position of eyelashes by examining the images from the video sequences taken of the close eye, and further to position the moveable range for eyes, named as the ROI (range of image). Then, we conducted the process of feature extraction to extract out 4 features: Spatial Domain Feature, Statistical Feature, Frequency Domain Feature, and Entropy Feature, respectively. Next, we investigate their correlations to the EOG by comparing these 4 features with the EOG signals obtained from the actual EOG measuring process. We then discovered a good correspondence between the Entropy Feature and EOG signal. As a result, the Entropy Feature may be a better approach of correspondence to develop the computer vision EOG.
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Książki na temat "Electrooculogram"

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Butkov, Nic. Polysomnography. Redaktorzy Sudhansu Chokroverty, Luigi Ferini-Strambi i Christopher Kennard. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199682003.003.0007.

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This chapter provides an overview of the sleep recording process, including the application of electrodes and sensors to the patient, instrumentation, signal processing, digital polysomnography (PSG), and artifact recognition. Topics discussed include indications for PSG, standard recording parameters, patient preparation, electrode placement for recording the electroencephalogram (EEG), electrooculogram (EOG), electromyogram (EMG), and electrocardiogram (ECG), the use of respiratory transducers, oximetry, signal processing, filters, digital data display, electrical safety, and patient monitoring. This chapter also includes record samples of the various types of recording artifacts commonly found in sleep studies, with a detailed description of their causes, preventative measures, and recommended corrective actions.
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Części książek na temat "Electrooculogram"

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Zayit-Soudry, Shiri, i Ido Perlman. "Electrooculogram". W Encyclopedia of Ophthalmology, 1–3. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-35951-4_1033-1.

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Zayit-Soudry, Shiri, i Ido Perlman. "Electrooculogram". W Encyclopedia of Ophthalmology, 705–7. Berlin, Heidelberg: Springer Berlin Heidelberg, 2018. http://dx.doi.org/10.1007/978-3-540-69000-9_1033.

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Yang, Fumeng, i Bin Xia. "Single Electrooculogram Channel-Based Sleep Stage Classification". W Advances in Cognitive Neurodynamics (V), 595–600. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0207-6_80.

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Banerjee, Anwesha, Shreyasi Datta, Amit Konar, D. N. Tibarewala i Janarthanan Ramadoss. "Cognitive Activity Recognition Based on Electrooculogram Analysis". W Smart Innovation, Systems and Technologies, 637–44. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07353-8_73.

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Sandra, D’Souza, i N. Sriraam. "Feature Based Reading Skill Analysis Using Electrooculogram Signals". W Advanced Computing and Communication Technologies, 233–44. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1023-1_24.

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Medeiros, Romeu, Ana Cláudia S. Souza i Gustavo F. Rodrigues. "Mouse Control Interface Using Electrooculogram and Genetic Programming". W XXVI Brazilian Congress on Biomedical Engineering, 335–39. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-2517-5_51.

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Goswami, Laxmi. "Human Computer Interface Using Electrooculogram as a Substitute". W International Conference on Intelligent Emerging Methods of Artificial Intelligence & Cloud Computing, 170–77. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-92905-3_21.

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Borchardt, A. R., L. S. Schiavon, L. G. L. Silva, A. A. Souza Junior i M. G. Lucas. "Acquisition and Comparison of Classification Algorithms in Electrooculogram Signals". W XXVII Brazilian Congress on Biomedical Engineering, 1999–2003. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-70601-2_292.

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Gondou, Kazuya, Hiroki Tamura i Koichi Tanno. "A Study on Human Interface for Communication Using Electrooculogram Signals". W Advances in Intelligent Systems and Computing, 311–20. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23207-2_31.

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Ning, Bo, Ming-jie Li, Tong Liu, Hui-min Shen, Liang Hu i Xin Fu. "Human Brain Control of Electric Wheelchair with Eye-Blink Electrooculogram Signal". W Intelligent Robotics and Applications, 579–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33509-9_58.

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Streszczenia konferencji na temat "Electrooculogram"

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Datta, Shreyasi, Anwesha Banerjee, Amit Konar i D. N. Tibarewala. "Electrooculogram based cognitive context recognition". W 2014 International Conference on Electronics, Communication and Instrumentation (ICECI). IEEE, 2014. http://dx.doi.org/10.1109/iceci.2014.6767362.

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Brahmaiah, V. Priyanka, Y. Padma Sai i M. N. Giri Prasad. "Data Acquisition System of Electrooculogram". W 2017 IEEE 7th International Advance Computing Conference (IACC). IEEE, 2017. http://dx.doi.org/10.1109/iacc.2017.0149.

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Atique, Md Moin Uddin, Sakhawat Hossen Rakib i Khondkar Siddique-e-Rabbani. "An electrooculogram based control system". W 2016 International Conference on Informatics, Electronics and Vision (ICIEV). IEEE, 2016. http://dx.doi.org/10.1109/iciev.2016.7760113.

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Alquran, Hiam, Ali Mohammad Alqudah, Isam Abu Qasmieh i Sami Almashaqbeh. "Gaussian Model of Electrooculogram Signals". W 2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT). IEEE, 2019. http://dx.doi.org/10.1109/jeeit.2019.8717499.

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Trikha, Mrinal, Tapan Gandhi, Ayush Bhandari i Vijay Khare. "Multiple Channel Electrooculogram Classification using Automata". W 2007 IEEE International Workshop on Medical Measurement and Applications. IEEE, 2007. http://dx.doi.org/10.1109/memea.2007.4285158.

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Malaekah, Emad, Chanakya Reddy Patti i Dean Cvetkovic. "Automatic sleep-wake detection using electrooculogram signals". W 2014 IEEE Conference on Biomedical Engineering and Sciences (IECBES). IEEE, 2014. http://dx.doi.org/10.1109/iecbes.2014.7047603.

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Rosa, Andrei, Virgı́nia Bordignon, Carla Becker i Sergio Almeida. "A New Approach for Electrooculogram Recognition Algorithms". W XXXV Simpósio Brasileiro de Telecomunicações e Processamento de Sinais. Sociedade Brasileira de Telecomunicações, 2017. http://dx.doi.org/10.14209/sbrt.2017.40.

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Kim-Tien, Nguyen, i Nguyen Truong-Thinh. "Using Electrooculogram and Electromyogram for powered wheelchair". W 2011 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE, 2011. http://dx.doi.org/10.1109/robio.2011.6181515.

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Bardhan, Jayetri, P. Suma i M. Jyothirmayi. "Motorized wheelchair control using electrooculogram and head gear". W 2016 International Conference on Inventive Computation Technologies (ICICT). IEEE, 2016. http://dx.doi.org/10.1109/inventive.2016.7830190.

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Galiana-Merino, Juan Jose, Daniel Ruiz-Fernandez i Agustin Jarones-Gonzalez. "Electrooculogram filtering using wavelet and wavelet packet transforms". W 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2013. http://dx.doi.org/10.1109/embc.2013.6609680.

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