Señales EOG: una revisión sobre procesamiento y aplicaciones
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adquisición de datos, discapacidad, electrooculografía, electrodos, tratamiento de señales
En este trabajo se presenta una revisión sobre procesamiento y aplicaciones de señales de electrooculografía. En primer lugar, se da a conocer el marco general sobre el uso de las señales mencionadas. Posteriormente se describen investigaciones de vanguardia sobre sistemas basados en señales electrooculografía. El objetivo de esta revisión es detectar los avances de los sistemas basados en dichas señales para futuros desarrollos tecnológicos.
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W. Du Chang, H. S. Cha, and C. H. Im, “Removing the interdependency between horizontal and vertical eye-movement components in electrooculograms,” Sensors (Switzerland), vol. 16, no. 2, 2016. DOI: https://doi.org/10.3390/s16020227
R. Stoean et al., “A hybrid unsupervised-Deep learning tandem for electrooculography time series analysis,” PLoS One, vol. 15, no. 7, 2020. DOI: https://doi.org/10.1371/journal.pone.0236401
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A. López, M. Fernández, H. Rodríguez, F. Ferrero, and O. Postolache, “Development of an EOG-based system to control a serious game,” Meas. J. Int. Meas. Confed., vol. 127, pp. 481–488, 2018. DOI: https://doi.org/10.1016/j.measurement.2018.06.017
C. T. Lin et al., “EOG-Based Eye Movement Classification and Application on HCI Baseball Game,” IEEE Access, vol. 7, pp. 96166–96176, 2019. DOI: https://doi.org/10.1109/ACCESS.2019.2927755
X. Ding, Z. Lv, C. Zhang, X. Gao, and B. Zhou, “A Robust Online Saccadic Eye Movement Recognition Method Combining Electrooculography and Video,” IEEE Access, vol. 5, pp. 17997–18003, 2017. DOI: https://doi.org/10.1109/ACCESS.2017.2750701
W. Tang, A. Wang, S. Ramkumar, and R. K. R. Nair, “Signal identification system for developing rehabilitative device using deep learning algorithms,” Artif. Intell. Med., vol. 102, p. 101755, 2020. DOI: https://doi.org/10.1016/j.artmed.2019.101755
S. Fang, A. F. Hussein, S. Ramkumar, K. S. Dhanalakshmi, and G. Emayavaramban, “Prospects of electrooculography in human-computer interface based neural rehabilitation for neural repair patients,” IEEE Access, vol. 7, pp. 25506–25515, 2019. DOI: https://doi.org/10.1109/ACCESS.2019.2899188
D. Y. Kim, C. H. Han, and C. H. Im, “Development of an electrooculogram-based human-computer interface using involuntary eye movement by spatially rotating sound for communication of locked-in patients,” Sci. Rep., vol. 8, no. 1, pp. 1–10, 2018. DOI: https://doi.org/10.1038/s41598-018-27865-5
J. Xiao, J. Qu, and Y. Li, “An Electrooculogram-Based Interaction Method and Its Music-on-Demand Application in a Virtual Reality Environment,” IEEE Access, vol. 7, pp. 22059–22070, 2019. DOI: https://doi.org/10.1109/ACCESS.2019.2898324
A. J. Molina-Cantero, C. Lebrato-Vazquez, M. Merino-Monge, R. Quesada-Tabares, J. A. Castro-Garcia, and I. M. Gomez-Gonzalez, “Communication technologies based on voluntary blinks: Assessment and design,” IEEE Access, vol. 7, pp. 70770–70798, 2019. DOI: https://doi.org/10.1109/ACCESS.2019.2919324
A. Tonin, A. Jaramillo-Gonzalez, A. Rana, M. Khalili-Ardali, N. Birbaumer, and U. Chaudhary, “Auditory Electrooculogram-based Communication System for ALS Patients in Transition from Locked-in to Complete Locked-in State,” Sci. Rep., vol. 10, no. 1, pp. 1–10, 2020. DOI: https://doi.org/10.1038/s41598-020-65333-1
P. Tian et al., “A hierarchical classification method for automatic sleep scoring using multiscale entropy features and proportion information of sleep architecture,” Biocybern. Biomed. Eng., vol. 37, no. 2, pp. 263–271, 2017. DOI: https://doi.org/10.1016/j.bbe.2017.01.005
D. JIANG, Y. MA, and Y. WANG, “Sleep stage classification using covariance features of multi-channel physiological signals on Riemannian manifolds,” Comput. Methods Programs Biomed., vol. 178, pp. 19–30, 2019. DOI: https://doi.org/10.1016/j.cmpb.2019.06.008
C. E. Kuo and G. T. Chen, “A Short-Time Insomnia Detection System Based on Sleep EOG with RCMSE Analysis,” IEEE Access, vol. 8, pp. 69763–69773, 2020. DOI: https://doi.org/10.1109/ACCESS.2020.2986397
H. Korkalainen et al., “Accurate Deep Learning-Based Sleep Staging in a Clinical Population with Suspected Obstructive Sleep Apnea,” IEEE J. Biomed. Heal. Informatics, vol. 24, no. 7, pp. 2073–2081, 2020.
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F. Latifoǧlu, M. Y. Esas, and E. Demirci, “Diagnosis of attention-deficit hyperactivity disorder using EOG signals: A new approach,” Biomed. Tech., vol. 65, no. 2, pp. 149–164, 2020. DOI: https://doi.org/10.1515/bmt-2019-0027
H. Iwaki et al., “Using spontaneous eye-blink rates to predict the motor status of patients with parkinson’s disease,” Intern. Med., vol. 58, no. 10, pp. 1417–1421, 2019. DOI: https://doi.org/10.2169/internalmedicine.1960-18
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En este trabajo se presenta una revisión sobre procesamiento y aplicaciones de señales de electrooculografía. En primer lugar, se da a conocer el marco general sobre el uso de las señales mencionadas. Posteriormente se describen investigaciones de vanguardia sobre sistemas basados en señales electrooculografía. El objetivo de esta revisión es detectar los avances de los sistemas basados en dichas señales para futuros desarrollos tecnológicos.
A. Bertomeu-Motos et al., “User activity recognition system to improve the performance of environmental control interfaces: A pilot study with patients,” J. Neuroeng. Rehabil., vol. 16, no. 1, pp. 1–9, 2019. DOI: https://doi.org/10.1186/s12984-018-0477-5
N. Barbara, T. A. Camilleri, and K. P. Camilleri, “A comparison of EOG baseline drift mitigation techniques,” Biomed. Signal Process. Control, vol. 57, p. 101738, 2020. DOI: https://doi.org/10.1016/j.bspc.2019.101738
J. Ryu, M. Lee, and D. H. Kim, “EOG-based eye tracking protocol using baseline drift removal algorithm for long-term eye movement detection,” Expert Syst. Appl., vol. 131, pp. 275–287, 2019. DOI: https://doi.org/10.1016/j.eswa.2019.04.039
Z. Lv, Y. Wang, C. Zhang, X. Gao, and X. Wu, “An ICA-based spatial filtering approach to saccadic EOG signal recognition,” Biomed. Signal Process. Control, vol. 43, pp. 9–17, 2018. DOI: https://doi.org/10.1016/j.bspc.2018.01.003
B. Zhang, N. Bi, C. Zhang, X. Gao, and Z. Lv, “Robust EOG-based saccade recognition using multi-channel blind source deconvolution,” Biomed. Eng. (NY)., vol. 64, no. 3, pp. 309–324, 2019. DOI: https://doi.org/10.1515/bmt-2018-0018
V. P. Brahmaiah, Y. P. Sai, and M. N. Giriprasad, “A new framework for recognizing normal and epileptic seizure from eye movement signals using genetic based convolutional neural network,” Trait. du Signal, vol. 37, no. 3, pp. 493–501, 2020. DOI: https://doi.org/10.18280/ts.370317
W. Du Chang, H. S. Cha, and C. H. Im, “Removing the interdependency between horizontal and vertical eye-movement components in electrooculograms,” Sensors (Switzerland), vol. 16, no. 2, 2016. DOI: https://doi.org/10.3390/s16020227
R. Stoean et al., “A hybrid unsupervised-Deep learning tandem for electrooculography time series analysis,” PLoS One, vol. 15, no. 7, 2020. DOI: https://doi.org/10.1371/journal.pone.0236401
A. López, F. Ferrero, and O. Postolache, “An affordable method for evaluation of ataxic disorders based on electrooculography,” Sensors (Switzerland), vol. 19, no. 17, pp. 1–16, 2019. DOI: https://doi.org/10.3390/s19173756
S. K. Ameri et al., “Imperceptible electrooculography graphene sensor system for human–robot interface,” npj 2D Mater. Appl., vol. 2, no. 1, pp. 1–7, 2018. DOI: https://doi.org/10.1038/s41699-018-0064-4
A. J. Golparvar and M. K. Yapici, “Electrooculography by Wearable Graphene Textiles,” IEEE Sens. J., vol. 18, no. 21, pp. 8971–8978, 2018. DOI: https://doi.org/10.1109/JSEN.2018.2868879
A. J. Golparvar and M. K. Yapici, “Graphene Smart Textile-Based Wearable Eye Movement Sensor for Electro-Ocular Control and Interaction with Objects,” J. Electrochem. Soc., vol. 166, no. 9, 2019. DOI: https://doi.org/10.1149/2.0241907jes
V. Toral et al., “Cost-Effective Printed Electrodes Based on Emerging Materials Applied to Biosignal Acquisition,” IEEE Access, vol. 8, pp. 127789–127800, 2020. DOI: https://doi.org/10.1109/ACCESS.2020.3008945
A. G. Robson et al., “ISCEV guide to visual electrodiagnostic procedures,” Doc. Ophthalmol., vol. 136, no. 1, pp. 1–26, 2018. DOI: https://doi.org/10.1007/s10633-017-9621-y
Y. Jia and C. W. Tyler, “Measurement of saccadic eye movements by electrooculography for simultaneous EEG recording,” Behav. Res. Methods, vol. 51, no. 5, pp. 2139–2151, 2019. DOI: https://doi.org/10.3758/s13428-019-01280-8
A. Pal, A. K. Gautam, and Y. N. Singh, “Evaluation of bioelectric signals for human recognition,” Procedia Comput. Sci., vol. 48, no. C, pp. 746–752, 2015. DOI: https://doi.org/10.1016/j.procs.2015.04.211
S. Arslan Tuncer and T. Kaya, “True Random Number Generation from Bioelectrical and Physical Signals,” Comput. Math. Methods Med., vol. 2018, pp. 1–11, 2018. DOI: https://doi.org/10.1155/2018/3579275
P. Wangmo, G. Ramyavani, K. S. Iyer, and V. Karthik Raj, “Two electrode ECG and EOG system for monitoring applications,” Int. J. Innov. Technol. Explor. Eng., vol. 8, no. 11, pp. 262–265, 2019. DOI: https://doi.org/10.35940/ijitee.K1315.0981119
A. López, M. Fernández, H. Rodríguez, F. Ferrero, and O. Postolache, “Development of an EOG-based system to control a serious game,” Meas. J. Int. Meas. Confed., vol. 127, pp. 481–488, 2018. DOI: https://doi.org/10.1016/j.measurement.2018.06.017
C. T. Lin et al., “EOG-Based Eye Movement Classification and Application on HCI Baseball Game,” IEEE Access, vol. 7, pp. 96166–96176, 2019. DOI: https://doi.org/10.1109/ACCESS.2019.2927755
X. Ding, Z. Lv, C. Zhang, X. Gao, and B. Zhou, “A Robust Online Saccadic Eye Movement Recognition Method Combining Electrooculography and Video,” IEEE Access, vol. 5, pp. 17997–18003, 2017. DOI: https://doi.org/10.1109/ACCESS.2017.2750701
W. Tang, A. Wang, S. Ramkumar, and R. K. R. Nair, “Signal identification system for developing rehabilitative device using deep learning algorithms,” Artif. Intell. Med., vol. 102, p. 101755, 2020. DOI: https://doi.org/10.1016/j.artmed.2019.101755
S. Fang, A. F. Hussein, S. Ramkumar, K. S. Dhanalakshmi, and G. Emayavaramban, “Prospects of electrooculography in human-computer interface based neural rehabilitation for neural repair patients,” IEEE Access, vol. 7, pp. 25506–25515, 2019. DOI: https://doi.org/10.1109/ACCESS.2019.2899188
D. Y. Kim, C. H. Han, and C. H. Im, “Development of an electrooculogram-based human-computer interface using involuntary eye movement by spatially rotating sound for communication of locked-in patients,” Sci. Rep., vol. 8, no. 1, pp. 1–10, 2018. DOI: https://doi.org/10.1038/s41598-018-27865-5
J. Xiao, J. Qu, and Y. Li, “An Electrooculogram-Based Interaction Method and Its Music-on-Demand Application in a Virtual Reality Environment,” IEEE Access, vol. 7, pp. 22059–22070, 2019. DOI: https://doi.org/10.1109/ACCESS.2019.2898324
A. J. Molina-Cantero, C. Lebrato-Vazquez, M. Merino-Monge, R. Quesada-Tabares, J. A. Castro-Garcia, and I. M. Gomez-Gonzalez, “Communication technologies based on voluntary blinks: Assessment and design,” IEEE Access, vol. 7, pp. 70770–70798, 2019. DOI: https://doi.org/10.1109/ACCESS.2019.2919324
A. Tonin, A. Jaramillo-Gonzalez, A. Rana, M. Khalili-Ardali, N. Birbaumer, and U. Chaudhary, “Auditory Electrooculogram-based Communication System for ALS Patients in Transition from Locked-in to Complete Locked-in State,” Sci. Rep., vol. 10, no. 1, pp. 1–10, 2020. DOI: https://doi.org/10.1038/s41598-020-65333-1
P. Tian et al., “A hierarchical classification method for automatic sleep scoring using multiscale entropy features and proportion information of sleep architecture,” Biocybern. Biomed. Eng., vol. 37, no. 2, pp. 263–271, 2017. DOI: https://doi.org/10.1016/j.bbe.2017.01.005
D. JIANG, Y. MA, and Y. WANG, “Sleep stage classification using covariance features of multi-channel physiological signals on Riemannian manifolds,” Comput. Methods Programs Biomed., vol. 178, pp. 19–30, 2019. DOI: https://doi.org/10.1016/j.cmpb.2019.06.008
C. E. Kuo and G. T. Chen, “A Short-Time Insomnia Detection System Based on Sleep EOG with RCMSE Analysis,” IEEE Access, vol. 8, pp. 69763–69773, 2020. DOI: https://doi.org/10.1109/ACCESS.2020.2986397
H. Korkalainen et al., “Accurate Deep Learning-Based Sleep Staging in a Clinical Population with Suspected Obstructive Sleep Apnea,” IEEE J. Biomed. Heal. Informatics, vol. 24, no. 7, pp. 2073–2081, 2020.
S. I. Dimitriadis, C. Salis, and D. Linden, “A novel, fast and efficient single-sensor automatic sleep-stage classification based on complementary cross-frequency coupling estimates,” Clin. Neurophysiol., vol. 129, no. 4, pp. 815–828, 2018. DOI: https://doi.org/10.1016/j.clinph.2017.12.039
F. Latifoǧlu, M. Y. Esas, and E. Demirci, “Diagnosis of attention-deficit hyperactivity disorder using EOG signals: A new approach,” Biomed. Tech., vol. 65, no. 2, pp. 149–164, 2020. DOI: https://doi.org/10.1515/bmt-2019-0027
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