Abstract
The level of attention in people is associated with the efficiency in their intellectual activities, in their level of understanding and in the development of their creative ability. It is essential to know the behavior of the physiological variables involved in this process, with these variables the states of attention of a person can be determined with greater precision. Using this information, a person can have feedback on their cognitive activity and thus raise attention on the activity performed and consequently improve their cognitive performance. A common problem is the complexity of recovering the data by means of sensors since they are usually invasive and difficult to calibrate, they are usually single-user. So the signals can contain noise and generate an error in the diagnosis. In this work we propose the implementation of a non-invasive multi-user system, for the identification of the level of attention in people, based on at least two physiological variables of the user to determine it, as well as obtaining a better performance in reading the physiological variables, in the delivery of the final diagnosis and in the control of the level of attention of the people to improve their cognitive performance. Currently there are several commercial headbands used as sensors of brain waves. The manufacturers of these devices provide a graphical interface limited to specific applications. In this work, is shown a description of the development of data acquisition of three commercial brainwave diadems: Mindwave, MUSE and Emotiv Epoc. The data obtained are processed independently of the manufacturer’s software to obtain the level of attention of the users, implementing a monitoring system for each commercial device.
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References
Ravichandran, S., Huang, J.: Motivating children with attention deficiency disorder using certain behavior modification strategies. In: Lim, C.T., Goh, J.C.H. (eds.) 13th International Conference on Biomedical Engineering. IFMBE Proceedings, vol. 23, pp. 1057–1062. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-540-92841-6_260
Huh, J., Ackerman, M.S.: Exploring social dimensions of personal information management with adults with AD/HD. In: CHI EA 2010 CHI 2010 Extended Abstracts on Human Factors in Computing Systems, Atlanta, Georgia, USA, vol. 1, pp. 3715–3720 (2010). https://doi.org/10.1145/1753846.1754044
Regan, L.M., et al.: Games as neurofeedback training for children with FASD. In: IDC 2013 Proceedings of the 12th International Conference on Interaction Design and Children, New York, USA, vol. 1, pp. 165–172 (2013). https://doi.org/10.1145/2485760.2485762
Pascual, M.F., Begoña, Z., Buldian, K.M.: Adaptive cognitive rehabilitation interventions based on serious games for children with ADHD using biofeedback techniques: assessment and evaluation. In: COMPUTE 2010 Proceedings of the Third Annual ACM Bangalore Conference, Article 29, Bilbao, España, pp. 1–4 (2014). https://doi.org/10.4108/icst.pervasivehealth.2014.255249
Divia, V.: A smartwatch application for individuals with ADHD and mental health challenges. In: ASSETS 2016 Proceedings of the 18th International ACM SIGACCESS Conference on Computers and Accessibility, Nevada, USA, vol. 1, pp. 311–312 (2016). https://doi.org/10.1145/2982142.2982207
García, A.E.: Análisis de ondas cerebrales para determinar emociones a partir de estímulos visuales. Tesis Universidad Veracruzana Facultad de Estadística e Informática, México (2015)
Girouard, A.: Adaptive brain-computer interface. In: Proceedings of the 27th International Conference on Human Factors in Computing Systems, CHI 2009, USA, vol. 1, pp. 3097–3100 (2009). https://doi.org/10.1145/1520340.1520436
Campazzo, E., Martínez, M., Guzmán, A., Agüero, A.: Desarrollo de interface de detección de emociones para su utilización en redes sociales y entornos virtuales de aprendizaje. In: XV Workshop de Investigadores en Ciencias de la Computación, Paraná (2013)
Hernández, A., Vásquez, R., Olivares, B.A., Cortes, G., López, I.: Sistema de detección de emociones para la recomendación de recursos educativos. In: Programación Matemática y Software, México, pp. 58–66 (2016). ISSN 2007-3283
Marín, E.J.: Detección de emociones del usuario. Tesis Pontificia Universidad, Chile (2014)
Torres, F., Sánchez, C., Palacio, B.: Adquisición y análisis de señales cerebrales utilizando el dispositivo MindWave. In: MASKANA, I+D+ingeniería 2014, vol. 5, pp. 83–93. (2014). ISSN 1390-6143
Rojas, S., Garzón, J., Martínez, D., Escobar, M., Robayo, C.: Lector de ondas cerebrales para implementar un sistema alternativo y aumentativo de comunicación. In: 10th Latin American and Caribbean Conference for Engineering and Technology, Panamá, vol. 10, pp. 1–9 (2012)
Perakakis, M., Potamianos, A.: An affective evaluation tool using brain signals. In: IUI 2013 Companion, USA, vol. 1, pp. 105–106 (2013). https://doi.org/10.1145/2451176.2451222
Pinto, R.D., Ferreira, H.A.: Development of a non-invasive brain computer interface for neurorehabilitation. In: REHAB 2015, Portugal, vol. 1, pp. 1–5 (2015). https://doi.org/10.1145/2838944.2838975
Zuckerman, O., et al.: KIP3: robotic companion as an external cue to students with ADHD. In: TEI 2016 Proceedings of the TEI 2016: Tenth International Conference on Tangible, Embedded, and Embodied Interaction, USA, vol. 1, pp. 621–626 (2016). https://doi.org/10.1145/2839462.2856535
Horii, T., Nagai, Y., Asada, M.: Active perception based on energy minimization in multimodal human-robot interaction. In: HAI 2017, Alemania, vol. 1, pp. 103–110 (2017). https://doi.org/10.1145/3125739.3125757
Alvarez, C.L., Hernández, M.A., Hernández, H.M.: Automatic evaluation of learning objects based on cross-entropy of eye fixations minimization. In: Interaccion 2017, México, vol. 1, pp. 1–4 (2017). https://doi.org/10.1145/3123818.3123872
Gomez, J.E., Marcé, A.M.: Brain Sensors Aplicats a la Tecnologia Mecánica. Tesis TFG Universitat Politécnica de Catalunya, Barcelona (2016)
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Garcia, A., Gonzalez, J.M., Palomino, A. (2019). Data Acquisition System for the Monitoring of Attention in People and Development of Interfaces for Commercial Devices. In: Agredo-Delgado, V., Ruiz, P. (eds) Human-Computer Interaction. HCI-COLLAB 2018. Communications in Computer and Information Science, vol 847. Springer, Cham. https://doi.org/10.1007/978-3-030-05270-6_7
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