This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Open AccessArticle
Changes in Functional Connectivity of Electroencephalography While Learning to Touch-Type
by
David Gutiérrez
David Gutiérrez
David Gutiérrez (a.k.a. *Dania* Gutiérrez) received a B.Sc. degree in Electrical Engineering from [...]
David Gutiérrez (a.k.a. *Dania* Gutiérrez) received a B.Sc. degree in Electrical Engineering from the National Autonomous University of Mexico (UNAM) in 1997, an M.Sc. degree in Electrical Engineering and Computer Sciences, as well as a Ph.D. degree in Bioengineering from the University of Illinois at Chicago (UIC), in 2000 and 2005, respectively. From March 2005 to May 2006, she held a postdoctoral fellowship at the Department of Computer Systems Engineering and Automation, Institute of Research in Applied Mathematics and Systems (IIMAS), UNAM. In June 2006, she joined the Center for Research and Advanced Studies (Cinvestav) at Monterrey, Mexico. There, she is an associate professor in the area of medical sciences, and she was the academic secretary from 2015 to 2020. In 2015, Dr. Gutiérrez was a visiting professor at the Nicolaus Copernicus University at Toruń, Poland. Dr. Gutiérrez’s area of expertise is statistical signal processing and its applications to biomedicine. She is currently working on projects related to human–machine interfaces, neurocognition, and bioelectromagnetism. Dr. Gutiérrez is a former Fulbright fellow and a former student fellow of the Mexican Council for Science and Technology (Conacyt).
Monterrey’s Unit, Center for Research and Advanced Studies (Cinvestav), Apodaca 66628, Mexico
Appl. Sci. 2026, 16(1), 84; https://doi.org/10.3390/app16010084 (registering DOI)
Submission received: 2 October 2025
/
Revised: 16 December 2025
/
Accepted: 18 December 2025
/
Published: 21 December 2025
Abstract
The functional brain connectivity of electroencephalography (EEG) data that was acquired during the process of learning how to touch-type using the Colemak keyboard distribution is analyzed in this paper. The partial directed coherence (PDC) of the EEG in alpha, beta, and gamma rhythms was used to assess the functional brain connectivity at different learning stages. As a result, connectivity patterns common to the volunteers of the learning process are found to be representative of underlying brain processes. In particular, functional connectivity within the alpha brain rhythm in low-difficulty learning tasks exhibits the greatest desynchronization in the parietal lobes, which may be an indication of good performance during those tests. Widespread increase in fronto-central brain connectivity in the alpha band during the high-difficulty lesson is shown as a reflection of refined attention allocation and effective motor program processing. Beta modulation during motor planning is also reflected through an increase in frontal functional connectivity, as well as repetition suppression by a decrease in gamma connectivity. Metrics from complex network theory were used to associate channels P4, F4, Cz, and C4 as relevant in processes such as the execution of motor sequences, cognitive performance, and focused attention. These results add insight to previous analysis performed on the same database and further prove the feasibility of monitoring a learning process with EEG.
Share and Cite
MDPI and ACS Style
Gutiérrez, D.
Changes in Functional Connectivity of Electroencephalography While Learning to Touch-Type. Appl. Sci. 2026, 16, 84.
https://doi.org/10.3390/app16010084
AMA Style
Gutiérrez D.
Changes in Functional Connectivity of Electroencephalography While Learning to Touch-Type. Applied Sciences. 2026; 16(1):84.
https://doi.org/10.3390/app16010084
Chicago/Turabian Style
Gutiérrez, David.
2026. "Changes in Functional Connectivity of Electroencephalography While Learning to Touch-Type" Applied Sciences 16, no. 1: 84.
https://doi.org/10.3390/app16010084
APA Style
Gutiérrez, D.
(2026). Changes in Functional Connectivity of Electroencephalography While Learning to Touch-Type. Applied Sciences, 16(1), 84.
https://doi.org/10.3390/app16010084
Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details
here.
Article Metrics
Article metric data becomes available approximately 24 hours after publication online.