ISBN
Formato digital
979-13-87837-54-9
Fecha de publicación
06-10-2025
Licencia
D. R. © Copyright 2025. Alma Y. Alanis, Jorge Galvez, Omar Avalos, Eduardo Méndez-Palos, Jorge D. Rios, Adriana Peña Perez-Negron & Gabriel Martínez Soltero
Todos los contenidos de esta obra se comparten bajo la licencia Creative Commons Atri-bución/Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0). Esto implica que no está autorizado el uso comercial de la obra original ni de las eventuales obras derivadas, las cuales deberán distribuirse bajo la misma licencia que rige la obra original. No obstante, se permite a terceros compartir el contenido siempre y cuando se reconozca debidamente la autoría y la publicación original en esta editorial.
Maria Cristina Padilla Becerra
Universidad de Guadalajara
0009-0000-1530-0450
Emilio Barajas González
Universidad de Guadalajara
0000-0002-0468-6244
Eduardo Méndez Palos
Universidad de Guadalajara
0000-0002-3267-024X
Alma Yolanda Alanis García
Universidad de Guadalajara
0000-0001-9600-779X
Acerca de
People with speech disabilities face significant communication challenges, especially those with motor impairments that prevent them from using sign language. To address this issue, eye movement-based communication systems like Blink to Speak (BTS) have been developed, allowing users to convey messages through eight eye movement alphabets. This study evaluates the clas- sification of BTS alphabets using electrooculography (EOG) signals, comparing two approaches: within-subject (WS) and between-subject (BS). A dataset of EOG signals from 38 subjects was used to train and test a Random Forest model, selected for its superior performance in BS classification. Results show that WS classification generally outperforms BS, with F1-score improvements in 33 out of 38 subjects and across all alphabets. Alphabets with lower inter-subject similarity, such as “Wink”, “Roll” and “Left” achieved the most significant improvements in WS, while those with high similarity, like «Down» and «Blink,» showed less enhancement. Despite the superior performance of the WS approach, it requires an extensive data collection and calibration process, which may hinder practical implementation. Future work should consider balancing quick adaptability (favoring BS classification) and model accuracy (favoring WS classifica- tion), depending on user needs and application context.
Referencias
Hernández Rosado, M., Lluesma Rojas, M. de la C., De Veras Olivera, B.: Hacia una comunicación eficaz. Revista Cubana de Educación Superior. 38, (2019).
INEGI: Estadísticas a propósito del día internacional de las personas con discapacidad (datos nacionales). (2021).
Rico Ivorra, A.: Comunicación Aumentativa y Alternativa en personas con Esclerosis Lateral Amiotrófica y su impacto en la calidad de vida. (2021).
Zhang, J., Wang, B., Zhang, C., Xiao, Y., Wang, M.Y.: An EEG/EMG/EOG-based mul- timodal human-machine interface to real-time control of a soft robot hand. Front Neu- rorobot. 13, 420002 (2019).
Gutiérrez-Zermeño, M., Aguilera-Rodríguez, E., Barajas-González, E., Román- Godínez, I., Torres-Ramos, S., Salido-Ruiz, R.A.: Decoding Imagined Speech of Daily Use Words from EEG Signals Using Binary Classification. IFMBE Proc. 86, 293–301 (2023).
Silva-Acosta, V., Alfaro-Cortés, H.H., García-Manzo, R.E., Ruiz, R.A.S., Godínez, I.R., Ramos, S.T.: Clasificación inter-sujeto e intra-sujeto de señales SVEMG utilizando re- des neuronales para la intención del habla. Miscelánea Científica en México. 575 (2020).
Belkhiria, C., Boudir, A., Hurter, C., Peysakhovich, V.: EOG-Based Human–Computer Interface: 2000–2020 Review. Sensors. 22, 4914 (2022).
Ezzat, M., Maged, M., Gamal, Y., Adel, M., Alrahmawy, M., El-Metwally, S.: Blink- To-Live eye-based communication system for users with speech impairments. Sci Rep. 13, 7961 (2023).
Sane, H., Nivins, S.: Blink to speak, https://www.blinktospeak.com/blink-to-speak- book, last accessed 2023/06/19.
Pinto, S., Quintarelli, S., Silani, V.: New technologies and Amyotrophic Lateral Sclero- sis – Which step forward rushed by the COVID-19 pandemic? J Neurol Sci. 418, 117081 (2020).
Pugliese, R., Sala, R., Regondi, S., Beltrami, B., Lunetta, C.: Emerging technologies for management of patients with amyotrophic lateral sclerosis: from telehealth to assistive robotics and neural interfaces. J Neurol. 269, 2910–2921 (2022).
Jebakani, S., Divya, J., Megha, S., Santhosh, H.: Eye blink to voice for paralyzed pa- tients. International Journal of Information Technology (IJIT). 6, (2020).
Tyagi, S., Gupta, A., Maurya, A., Garg, A., Vats, K., Kumar, A.: Design of Blink to Speak System for Paralysed People. i-Manager’s Journal on Embedded Systems. 8, 14 (2020).
Tyagi, S., Gupta, A., Maurya, A., Garg, A., Vats, K., Kumar, A.: Design of Blink to Speak System for Paralysed People. i-Manager’s Journal on Embedded Systems. 8, 14– 19 (2020).
Malini, V., Jeevanandham, S., Gokul, P., Prathibanandhi, K., Kurinjimalar, L.: Blink To Speak an assistive device for Paralyzed people. In: 2022 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS). pp. 1–3 (2022).
Buitrago Jiménez, R.: Sistema de medida de movimientos oculares basado en reflecto- metría infrarroja, (2017).
Gila, L., Villanueva, A., Cabeza, R.: Fisiopatología y técnicas de registro de los movi- mientos oculares. In: Anales del sistema sanitario de Navarra. pp. 9–26. SciELO Espana (2009).
Nathan, D.S., Vinod, A.P., Thomas, K.P.: An electrooculogram based assistive commu- nication system with improved speed and accuracy using multi-directional eye move- ments. 2012 35th International Conference on Telecommunications and Signal Pro- cessing, TSP 2012 – Proceedings. 554–558 (2012).
Pérez-Reynoso, F.D., Rodríguez-Guerrero, L., Salgado-Ramírez, J.C., Ortega-Palacios, R.: Human–Machine Interface: Multiclass Classification by Machine Learning on 1D EOG Signals for the Control of an Omnidirectional Robot. Sensors (Basel). 21, 5882 (2021).
Hernández Pérez, S.N., Pérez Reynoso, F.D., Gutiérrez, C.A.G., Cosío León, M.D. los Á., Ortega Palacios, R.: EOG Signal Classification with Wavelet and Supervised Learn- ing Algorithms KNN, SVM and DT. Sensors 2023, Vol. 23, Page 4553. 23, 4553 (2023).
Padilla-Becerra, M.C., Macias-Castro, D.K., Salido-Ruiz, R.A., Torres-Ramos, S., Román-Godínez, I.: EOG Signal Classification Based on Blink-to-Speak Language. IFMBE Proc. 96, 249–257 (2024).
Padilla-Becerra, M.C., Salido-Ruiz, R.A., Román-Godínez, I., Santos-Arce, S.: Carac- terización de patrones electrooculográficos relacionados con el lenguaje “Blink to Speak”, (2024).
Sun, W., Lv, Z., Wu, M., Bi, N., Zhang, S.: A Comparative Experimental Study between Instantaneous and Convolutional BSS Models for Saccadic EOG Signal Separation. IEEE Trans Instrum Meas. 70, (2021).
Merino, M., Rivera, O., Gómez, I., Molina, A., Dorronzoro, E.: A method of EOG signal processing to detect the direction of eye movements. In: 2010 First International Con- ference on Sensor Device Technologies and Applications. pp. 100–105. IEEE (2010).
Aungsakul, S., Phinyomark, A., Phukpattaranont, P., Limsakul, C.: Evaluating Feature Extraction Methods of Electrooculography (EOG) Signal for Human-Computer Inter- face. Procedia Eng. 32, 246–252 (2012).
Jiang, J., Zhou, Z., Yin, E., Yu, Y., Hu, D.: Hybrid Brain-Computer Interface (BCI) based on the EEG and EOG signals. Biomed Mater Eng. 24, 2919–2925 (2014).
Phukpattaranont, P., Aungsakul, S., Phinyomark, A., Limsakul, C.: Efficient feature for classification of eye movements using electrooculography signals. Thermal Science. 20, 563–572 (2016).
Kumar, D., Sharma, A.: Electrooculogram-based virtual reality game control using blink detection and gaze calibration. 2016 International Conference on Advances in Compu- ting, Communications and Informatics, ICACCI 2016. 2358–2362 (2016).
Babita, Syal, P., Kumari, P.: Comparative Analysis of KNN, SVM, DT for EOG based Human Computer Interface. International Conference on Current Trends in Computer, Electrical, Electronics and Communication, CTCEEC 2017. 1023–1028 (2018).