Revista ELECTRO
Vol. 40 – Año 2018
Artículo
TÍTULO
Reconocimiento de Movimiento Ocular Mediante el Análisis de Señales EEG
AUTORES
Rivera Calderón O.G., Chacón-Murguía M.I., Ramírez-Quintana J.A.
RESUMEN
En este trabajo se desarrolló un algoritmo para el reconocimiento de movimiento ocular en cuatro direcciones: mirada arriba, abajo, izquierda y derecha. Las señales de movimiento ocular provenía n de señales electroencefalograficas (EEG) y fueron registradas por el dispositivo Emotiv Epoc+. Posteriormente, se generaron imágenes en escala de grises que representaban la información de los movimientos oculares. Después, se extrajeron características estadísticas de las imágenes para desarrollar un c lasificador modular que emplea tres perceptrones simples. El resultado del reconocimiento indica que se logró un desempeño del 92%, resaltando que el reconocimiento entre movi miento ocular en dirección horizontal o vertical es de 100%.
Palabras Clave: reconocimiento de movimiento ocular, EEG, generación de imágenes, perceptrón simple.
ABSTRACT
In this work, it was develop ed an algorithm for eye movement recognition in four directions: look up, down, left and right. The eye movement signals came from electroencephalographic (EEG) signals and were recorded by the Emotiv Epoc+ device. Then, images in gray scales were generated representing information o n eye movements. After th at, St atistical characteristics were extracted from the images to develo p a modular classifier using three simple perceptrons. Recognition result indicates a 92% performance, highlighting that the recognition between horizontal and vertical eye movement is 100%.
Keywords: eye movement recognition, EEG, images generation, simple perceptron.
REFERENCIAS
[1] A. Nasreddine, D. Shin, and H. Kambara, “Online classification algorithm for eye-movement-based communication systems using two temporal EEG sensors,” Biomed. Signal Process. Control, vol. 16, no. Febrero 2015, pp. 40 –47, 2015.
[2] V. Gerla et al., “Automatic identification of artifacts and unwanted physiologic signals in EEG and EOG during wakefulness,” Biomed. Signal Process. Control, vol. 31, no. Enero 2017, pp. 381 –390, 2017.
[3] A.-Z. M, S. M. Ahmed, and S. N. Abbas, “A new multi-level approach to {EEG} based human authentication using eye blinking,” Pattern Recognit. Lett., vol. 82, no. Parte 2, pp. 216 –225, 2015.
[4] K. R. Lee, W. Du Chang, S. Kim, and C. H. Im, “Real-time eye-writing recognition using electrooculogram,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 25, no. 1, pp. 37 –48, 2017.
[5] A. L. C. Bissoli, M. M. Sime, and T. F. Bastos-Filho, “Using sEMG, EOG and VOG to Control an Intelligent Environment,” IFAC-PapersOnLine, vol. 49, no. 30, pp. 210 –215, 2016.
[6] J. Heo, H. Yoon, and K. S. Park, “A Novel Wearable Forehead EOG Measurement System for Human Computer Interfaces,” Sensors, vol. 17, no. 7, pp. 1 –14, 2017.
[7] A. Dasgupta, S. Chakraborty, and A. Routray, “A two-stage framework for denoising electrooculograp hy signals,” Biomed. Signal Process. Control, vol. 31, no. Enero 2017, pp. 231 –237, 2017.
[8] R. Patel, M. P. R. Janawadkar, S. Sengottuvel, K. Gireesan, and T. S. Radhakrishnan, “Suppression of Eye-Blink Associated Artifact Using Single Channel EEG Data b y Combining Cross-Correlation with Empirical Mode Decomposition,” IEEE Sens. J., vol. 16, no. 18, pp. 6947 –6954, 2016.
[9] S. M. Abdelfattah, K. E. Merrick, and H. A. Abbass, “Eye Movements as Information Markers in EEG Data,” in 2016 IEEE symposium Series on Computational Intelligence (SSCI), 2016.
[10] C. Hsieh, H. Chu, and Y. Huang, “An HMM-based Eye Movement Detection System Using EEG Brain-Computer Interface,” in 2014 IEEE International Symposium on Circuits and Systems, 2014, pp. 662–665.
[11] A. N. B. H. H. N. Y. Y. Koike, “Classification of Four Eye Directions from EEG Signals for Eye-Movement-Based Communication Systems,” J. Med. Biol. Eng., vol. 34, no. December, pp. 581 –588, 2014.
[12] S. N. A. A. A. M.-S. M. Mostafa, “Brain computer interfacing : Applications and challenges,” Egypt. Informatics J., vol. 16, no. 2, pp. 213 –230, 2015.
[13] E. Inc, “Emotiv Systems,” Emotiv Systems, 2014. [Online]. Available: https://www.emotiv.com/files/Emotiv-EPOC-Product-Sheet-2014.pdf. [Accessed: 16-May-2018].
[14] A. N. Belkacem et al., “Real-Time Control of a Video Game Using Eye Movements and Two Temporal EEG Sensors,” Comput. Intell. Neurosci., vol. 2015, no. 1, pp. 1 –10, 2015.
[15] A. U. E. L. J. M. Azorin, “Wireless and Portable EOG-Based Interface,” IEEE/ASM E Trans. Mechatronics, vol. 16, no. 5, pp. 870 –873, 2011.
[16] C. Hsieh and Y. Huang, “Low-Complexity EEG-Based Eye Movement Classification Using Extended Moving Difference Filter and Pulse Width Demodulation,” in 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2015, pp. 7238 –7241.
[17] C. H. Hsieh and Y. H. Huang, “Low-complexity EEG-based eye movement classification using extended moving difference filter and pulse width demodulation,” in Proceedings of t he Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2015, vol. 2015 –Novem, pp. 7238 –7241.
[18] A. Banerjee, M. Pal, S. Datta, D. N. Tibarewala, and A. Konar, “Eye movement sequence analysis using electrooculogr am to assist autistic children,” Biomed. Signal Process. Control, vol. 14, no. Noviembre, pp. 134 –140, 2014.
[19] H. E. M. O. I. M. Orak, “Detection of directional eye movements based on the electrooculogram signals through an artificial neural network,” Chaos, Solitons & Fractals, vol. 77, no. Agosto 2015, pp. 225–229, 2015.
CITAR COMO:
Rivera Calderón O.G., Chacón-Murguía M.I., Ramírez-Quintana J.A., "Reconocimiento de Movimiento Ocular Mediante el Análisis de Señales EEG", Revista ELECTRO, Vol. 40, 2018, pp. 256-2.
VERSIÓN PDF