2026-04-19T16:50:09Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/102732025-03-04T11:43:51Zcom_10259_5841com_10259_5086com_10259_2604col_10259_5842
Repositorio Institucional de la Universidad de Burgos
author
Sáez García, Javier
author
Sáiz Manzanares, María Consuelo
author
Marticorena Sánchez, Raúl
2025-03-03T08:19:37Z
2025-03-03T08:19:37Z
2024
http://hdl.handle.net/10259/10273
10.3390/computers13110289
2073-431X
The use of eye tracking technology, together with other physiological measurements such as psychogalvanic skin response (GSR) and electroencephalographic (EEG) recordings, provides researchers with information about users’ physiological behavioural responses during their learning process in different types of tasks. These devices produce a large volume of data. However, in order to analyse these records, researchers have to process and analyse them using complex statistical and/or machine learning techniques (supervised or unsupervised) that are usually not incorporated into the devices. The objectives of this study were (1) to propose a procedure for processing the extracted data; (2) to address the potential technical challenges and difficulties in processing logs in integrated multichannel technology; and (3) to offer solutions for automating data processing and analysis. A Notebook in Jupyter is proposed with the steps for importing and processing data, as well as for using supervised and unsupervised machine learning algorithms.
eng
Atribución 4.0 Internacional
Eye tracking
Galvanic skin response
Behavioural monitoring
Learning process
Data processing
A Proposed Method of Automating Data Processing for Analysing Data Produced from Eye Tracking and Galvanic Skin Response
info:eu-repo/semantics/article
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URL
https://riubu.ubu.es/bitstream/10259/10273/1/S%c3%a1ez-computers_2024.pdf
File
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application/pdf
Sáez-computers_2024.pdf