Análisis de registros multicanal en el comportamiento humano

Authors

María Consuelo Saiz Manzanares
Universidad de Burgos
https://orcid.org/0000-0002-1736-2089
Raúl Marticorena Sánchez
Universidad de Burgos
https://orcid.org/0000-0002-2633-635X
David García García
Universidad de Burgos
https://orcid.org/0000-0001-5224-3280

Keywords:

Biomedical engineering, eye tracking, biomedical signals, electroencephalography (EEG), galvanic skin response (GSR), multichannel recordings, artificial intelligence, human behaviour snal, Human behaviour analysis

Synopsis

This specialized manual, developed for the Master’s Degree in Biomedical Engineering at the University of Burgos, serves as a comprehensive guide for the rigorous study of human behavior through multichannel recordings. Using an interdisciplinary approach, this work merges engineering principles with health sciences to equip readers with the essential methodological tools for acquiring and analyzing complex physiological signals. The text provides an in-depth exploration of cutting-edge technologies, including eye trackingelectroencephalography (EEG), and galvanic skin response (GSR). Additionally, it covers advanced computational processing with MNE-Python and the application of Machine Learning for biomedical data interpretation. With a project-based practical focus and a critical discussion on ethics in generative AI, this book stands as the definitive reference for professionals and researchers aiming to lead technological innovation in clinical and industrial settings.  

Downloads

Download data is not yet available.

Abstract 0

References

Ajiboye, A.B., Willett, F.R., Young, D.R., Memberg, W.D., Murphy, B.A., Miller, J.P., Walter, B.L., Sweet, J.A., Hoyen, H.A., Keith, M.W., Peckham, P.H., Simeral, J.D., Donoghue, J.P., Hochberg, L.R., y Kirsch, R.F. (2017). Restoration of reaching and grasping movements through brain-controlled muscle stimulation in a person with tetraplegia: a proof-of-concept demonstration. Lancet., 6, 389(10081),1821-1830. https://doi.org/10.1016/S0140-6736(17)30601-3

Bach, D. R., Flandin, G., Friston, K. J., & Dolan, R. J. (2010). Modelling event-related skin conductance responses. International Journal of Psychophysiology, 75(3), 349-356.

Boonstra, L. (2025). Prompt Engineering. Google, Disponible en: https://d66z.short.gy/3ok7tY

BrainGate. (2025). About BrainGate: Advancing brain–computer interface technology. https://www.braingate.org/about-braingate/ . Recuperado el 27 de agosto de 2025.

Equipos de neurotecnología https://www.bitbrain.com/es/productos-neurotecnologia. Recuperado el 27 de agosto de 2025.

Escolano, C., López-Larraz, E., Minguez, J., y Montesano, L. (2022). Brain-Computer Interface-Based Neurorehabilitation: From the Lab to the Users’ Home. En D. Torricelli, M. Akay, y J.L. Pons. (Eds.), Converging Clinical and Engineering Research on Neurorehabilitation IV. ICNR 2020. Biosystems & Biorobotics, vol 28. Springer, Cham. https://doi.org/10.1007/978-3-030-70316-5_91

Gamboa, H. (2008). Multi-modal behavioral biometrics based on hci and electrophysiology. PhD ThesisUniversidade.

García Peñalvo, F.J. (2025). Enseñanza con IA Generativa: Desafíos en Salud e Ingeniería. Curso formativo UBU.

Gramfort, A., Luessi, M., Larson, E., Engemann, D.A., Strohmeier, D., Brodbeck, C., Goj, R., Jas, M., Brooks, T., Parkkonen, L., & Hämäläinen, M.S. MEG and EEG data analysis with MNE-Python. (2013). Frontiers in Neuroscience, 7(267):1–13, https://doi.org/10.3389/fnins.2013.00267

Gramfort, A., Luessi, M., Larson, E., Engemann, D.A., Strohmeier, D., Brodbeck, C., Goj, R., Jas, M., Brooks, T., Parkkonen, L., & Hämäläinen, M.S. (2014). MNE software for processing MEG and EEG data. NeuroImage, 86, 446–460. https://doi.org/10.1016/j.neuroimage.2013.10.027

Greco, A., Valenza, G., & Scilingo, E. P. (2016). Evaluation of CDA and CvxEDA Models. In Advances in Electrodermal Activity Processing with Applications for Mental Health (pp. 35-43). Springer International Publishing.

Greco, A., Valenza, G., Lanata, A., Scilingo, E. P., & Citi, L. (2016). cvxEDA: A convex optimization approach to electrodermal activity processing. IEEE Transactions on Biomedical Engineering, 63(4), 797-804.

Hernando-Gallego, F., Luengo, D., & Artés-Rodríguez, A. (2017). Feature extraction of galvanic skin responses by nonnegative sparse deconvolution. IEEE journal of biomedical and shealth informatics, 22(5), 1385-1394.

Historia olvidada de las ondas cerebrales alfa https://www.bitbrain.com/es/blog/ondas-cerebrales-alfa . Recuperado el 27 de agosto de 2025.

Kim, K. H., Bang, S. W., & Kim, S. R. (2004). Emotion recognition system using short-term monitoring of physiological signals. Medical and biological engineering and computing, 42(3), 419-427.

Kotha, A., Lee, J., y Zakariasson, E. (2025). GPT-5 prompting guide. En:

OpenAI Cookbook. Disponible en: https://d66z.short.gy/CaAOnG

Lebedev, M.A, y Nicolelis, M.A. (2006). Brain-machine interfaces: past, present and future. Trends Neurosci. 29(9), 536-46. https://doi.org/10.3390/10.1016/j.tins.2006.07.004

Makowski, D. (2021). Neurophysiological Data Analysis with NeuroKit2. NeuroKit. https://neuropsychology.github.io/NeuroKit/

Makowski, D., Pham, T., Lau, Z. J., Brammer, J. C., Lespinasse, F., Pham, H., Schölzel, C., & Chen, S. A. (2021). NeuroKit2: A Python toolbox for neurophysiological signal processing. Behavior Research Methods, 53(4), 1689-1696. https://doi.org/10.3758/s13428-020-01516-y

Manual iView XTM v.2.7. SMI.

Manual Sennslab. Bitbrain.

Manual Sennsmetrics. Bitbrain.

Manual Tobi Pro Lab versión v 25.7April 2025.

MNE-Python. (2025). MNE-Python: Open-source Python software for exploring, visualizing, and analyzing human neurophysiological data. Recuperado el 27 de agosto de 2025, de https://mne.tools/stable/index.html

Nabian, M., Yin, Y., Wormwood, J., Quigley, K. S., Barrett, L. F., & Ostadabbas, S. (2018). An Open-Source Feature Extraction Tool for the Analysis of Peripheral Physiological Data. IEEE journal of translational engineering in health and medicine, 6, 2800711.

Neuralink. (2025). Neuralink: Building safe and powerful brain–machine interfaces. https://neuralink.com . Recuperado el 27 de agosto de 2025.

Neurofeedback aplicación Medusa https://www.bitbrain.com/blog/bci-neurofeedback-eeg-medusa . Recuperado el 27 de agosto de 2025.

OpenAI. (2025). ChatGPT [Large language model]. Recuperado el 01/12/2025. https://chat.openai.com/

Pathaky, R., & Cheung, C. (2025). GPT-5 Prompt Migration and Improvement Using the New Optimizer. En: OpenAI Cookbook. Disponible en: https://d66z.short.gy/oiBKPH

Perez-Valero, E., Morillas, C., Lopez-Gordo, M.A., y Minguillon, J. (2023). Supporting the Detection of Early Alzheimer’s Disease with a Four-Channel EEG Analysis. International Journal of Neural Systems, 33(4), 2350021. https://doi.org/10.1142/S0129065723500211

Pham T, Lau ZJ, Chen SHA, Makowski D. (2021). Heart Rate Variability in Psychology: A Review of HRV Indices and an Analysis Tutorial. Sensors (Basel). 21(12), 3998. https://doi.org/10.3390/s21123998

Proyecto eEarlyCare-T https://www2.ubu.es/eearlycare_t/

Registro y análisis de datos https://www.bitbrain.com/es. Recuperado el 27 de agosto de 2025.

Sáez-García, J., Sáiz-Manzanares, M. C., Marticorena-Sánchez, R. (2024). Challenges in data processing analysis by monitoring the learning process with Eye Tracking and Galvanic Skin Response. Computers, 13, 289. https://doi.org/10.3390/computers13110289

Sáiz-Manzanares, M. C. (2024). Módulo VII.3. Intervención temprana y aplicación de recursos inteligentes: utilización de la tecnología eye tracking y de la aplicación web eearlycare. En M.C. Sáiz-Manzanares y M. Santamaría Vázquez (Eds.), Formación y especialización en atención temprana: uso de recursos tecnológicos y de inteligencia artificial (257-280). Burgos: Servicio de Publicaciones de la Universidad de Burgos. https://doi.org/10.36443/9788418465802 (versión en español). https://doi.org/10.36443/9788418465819 (versión en inglés)

Sáiz-Manzanares, M. C., Marticorena-Sánchez, R., Sáez-García, J., & González-Díez, I. (2024). Analysing Virtual Labs Through Integrated Multi-Channel Eye-Tracking Technology: A Proposal for an Explanatory Fit Model. Applied Sciences, 14(21), 9831. https://doi.org/10.3390/app14219831

Sáiz-Manzanares, M.C. (2019). Metacognición e inteligencia artificial: más allá del paralelismo de funcionamiento. Tesis Doctoral. Burgos: Servicio de Publicaciones de la Universidad de Burgos. http://hdl.handle.net/10259/5357 acceso 25/09/2024

Sáiz-Manzanares, M.C., & Santamaria Vázquez, M. (2024). Formación y Especialización en Atención Temprana: uso de Recursos Tecnológicos y de Inteligencia Artificial. Burgos: Servicio de Publicaciones de la Universidad de Burgos. ISBN: 978-84-18465-80-2 https://doi.org/10.36443/9788418465802

Sáiz-Manzanares, M.C., & Santamaria Vázquez, M. (2024). Training and Specialisation in Early Intervention: use of Technological Resources and Artificial Intelligence. Burgos: Servicio de Publicaciones de la Universidad de Burgos. ISBN: 978-84-18465-81-9. https://doi.org/10.36443/9788418465819

Sáiz-Manzanares, M.C., Almeida, L.S., Martín-Antón, L.J., 3, Carbonero, M.Á., & Valdivieso-Burón, J.A. (2022a). Teacher Training Effectiveness in Self-Regulation in Virtual Environments. Frontiers in Psychology, 13, 776806. https://doi.org/10.3389/fpsyg.2022.776806

Sáiz-Manzanares, M.C., Alonso-Martínez, L., Calvo-Rodríguez, A., & Martin, C. (2022b). Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques. Journal of Visualized Experiments, e63601. https://doi.org/10.3791/63601

Sáiz-Manzanares, M.C., Marticorena-Sánchez, R., Escolar-Llamazares, M.C., Martín-Antón, L.J., & Velasco-Saiz, R. (2025). Monitoring university students’ learning processes: application of Advanced Learning Technologies and integrated multichannel techniques. Thinking Skills and Creativity, 58, 101938. https://doi.org/10.1016/j.tsc.2025.101938

Sáiz-Manzanares, M.C., Marticorena-Sánchez, R., Martín-Antón, L.J., González-Diez, I., & Carbonero-Martín, I. (2023). Using eye tracking technology to analyse cognitive load in multichannel activities in university students. International Journal of Human–Computer Interaction, 40(12), 3263–328. https://doi.org/10.1080/10447318.2023.2188532

Sáiz-Manzanares, M.C., Marticorena-Sánchez, R., Martín-Antón, L.J., Almeida, L., & Carbonero-Martín, I. (2023). Application and challenges of eye tracking technology in Higher Education. Comunicar, 76, 1-12. https://doi.org/10.3916/C76-2023-03

Sáiz-Manzanares, M.C., Marticorena-Sánchez, R.; Escolar-Llamazares, M.C., González-Díez, I., Martín Antón, L.J. (2024). Using integrated multimodal technology: a way to personalised learning in Health Sciences and Biomedical engineering Students. Applied Sciences, 14(16), 7017. https://doi.org/10.3390/app14167017

Sáiz-Manzanares, M.C., Ortega-Renuncio, R., y Marticorena-Sánchez, R. (2026). Processing and Analysis of Portable EEG Data for Cognitive Load Assessment in Neurotypical University Students. Frontiers in Human Neuroscience.

Sáiz-Manzanares, M.C., Payo Hernanz, R.J., Zaparaín Yáñez, M.J., Andrés López, G., Marticorena Sánchez, R., Calvo Rodríguez, A., Martin, C., y Rodríguez Arribas, S. (2021). Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes. Journal of Visualized Experiments. https://doi.org/10.3390/10.3791/62103

Sáiz-Manzanares, M.C., Queiruga-Dios, M.Á., García-Osorio, C.I., Montero, E., & Rodríguez, J. (2019). Observation of Metacognitive Skills in Natural Environments: A Longitudinal Study With Mixed Methods. Frontiers in Psychology, 10(2398), 1-13. https://doi.org/10.3389/fpsyg.2019.02398

Sáiz-Manzanares, M.C., Ramos Pérez, I., Arnaiz-Rodríguez, Á., Rodríguez-Arribas, S., Almeida, L., & Martin, C.F. (2021). Analysis of the learning process through eye tracking technology and feature selection techniques. Applied Sciences, 11, 6157, 1-24. https://doi.org/10.3390/app11136157

Sáiz-Manzanares, M.C., Rodríguez-Díez, J.J., Marticorena, R., Zaparaín, M.J., & Cerezo, R. (2020). Lifelong Learning from Sustainable Education: An Analysis with Eye Tracking and Data Mining Techniques. Sustainability, 12(5), 1-18. https://doi.org/10.3390/su12051970

Schwarz, A., Escolano, C., Montesano, L., y Müller-Putz, G.R. (2020) Analyzing and Decoding Natural Reach-and-Grasp Actions Using Gel, Water and Dry EEG Systems. Front. Neurosci., 14, 849. https://doi.org/10.3389/fnins.2020.00849

Sterman, M. B., y Chartier, D.R. (2023). Chapter 7 - Origins of electroencephalogram rhythms and implications for neurofeedback En D.R. Chartier., et al. (Eds)., Introduction to Quantitative EEG and Neurofeedback. Third Edition (pp. 103-120). https://doi.org/10.1016/B978-0-323-89827-0.00030-9

Synchron. (2025). Synchron: Brain-computer interface technology. https://synchronbci.com. Recuperado el 27 de agosto de 2025.

Tobii análisis del comportamiento humano https://www.tobii.com/learn-and-support/scientific-publications?utm_medium=email&utm_source=pardot&utm_campaign=msr-nurturing

Tobii Nexus for healthcare https://www.tobii.com/resource-center/reports-and-papers/tobii-nexus-for-healthcare

Van Halem, S., Van Roekel, E., Kroencke, L., Kuper, N., & Denissen, J. (2020). Moments That Matter? On the Complexity of Using Triggers Based on Skin Conductance to Sample Arousing Events Within an Experience Sampling Framework. European Journal of Personality.

Vidal, J.J. (1973). Toward Direct Brain-Computer Communication. Annu Rev Biophys Bioeng. 1973(2), 157-180. https://doi.org/10.1146/annurev.bb.02.060173.001105

Webs Tobii Pro Lab sample projects https://connect.tobii.com/s/demo-projects-landing-page/shopper-research?language=en_US

Webster, J.G & Nimunkar, A. (2020). Medical Instrumentation: Application and Design. Hoboken, USA: Wiley.

World Medical Association. (2013). World Medical Association Declaration of Helsinki: Ethical principles for medical research involving human subjects. JAMA, 310(20), 2191–2194. https://doi.org/10.1001/jama.2013.281053

Cubierta "Análisis de registros multicanal en el comportamiento humano"

Published

June 3, 2026

Details about the available publication format: Análisis de registros multicanal en el comportamiento humano (PDF)

Análisis de registros multicanal en el comportamiento humano (PDF)

ISBN-13 (15)

979-13-87585-40-2

Date of first publication (11)

2026-06-02

Details about the available publication format: Análisis de registros multicanal en el comportamiento humano (Libro en papel 32,50€)

Análisis de registros multicanal en el comportamiento humano (Libro en papel 32,50€)

ISBN-13 (15)

979-13-87585-39-6

Date of first publication (11)

2026-06-02

Physical Dimensions

160mm x 230mm x 11mm

How to Cite

Saiz Manzanares, M. C., Marticorena Sánchez, R., & García García, D. (2026). Análisis de registros multicanal en el comportamiento humano. UNIVERSIDAD DE BURGOS. Servicio de Publicaciones e Imagen Institucional - Libros en acceso abierto. https://doi.org/10.36443/9791387585402