Author(s):
Varandas, Rui ; Lima, Rodrigo ; Badia, Sergi Bermúdez I. ; Silva, Hugo ; Gamboa, Hugo
Date: 2022
Persistent ID: http://hdl.handle.net/10362/142798
Origin: Repositório Institucional da UNL
Project/scholarship:
info:eu-repo/grantAgreement/FCT/OE/PD%2FBDE%2F150304%2F2019/PT;
info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UID%2FCEC%2F04516%2F2019/PT;
info:eu-repo/grantAgreement/FCT/OE/PD%2FBDE%2F150304%2F2019/PT;
info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UID%2FCEC%2F04516%2F2019/PT;
Subject(s): brain–computer interface; cognitive fatigue; functional near-infrared spectroscopy; machine learning; Analytical Chemistry; Information Systems; Atomic and Molecular Physics, and Optics; Biochemistry; Instrumentation; Electrical and Electronic Engineering; SDG 3 - Good Health and Well-being
Description
Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
Wearable sensors have increasingly been applied in healthcare to generate data and monitor patients unobtrusively. Their application for Brain–Computer Interfaces (BCI) allows for unobtrusively monitoring one’s cognitive state over time. A particular state relevant in multiple domains is cognitive fatigue, which may impact performance and attention, among other capabilities. The monitoring of this state will be applied in real learning settings to detect and advise on effective break periods. In this study, two functional near-infrared spectroscopy (fNIRS) wearable devices were employed to build a BCI to automatically detect the state of cognitive fatigue using machine learning algorithms. An experimental procedure was developed to effectively induce cognitive fatigue that included a close-to-real digital lesson and two standard cognitive tasks: Corsi-Block task and a concentration task. Machine learning models were user-tuned to account for the individual dynamics of each participant, reaching classification accuracy scores of around 70.91 ± 13.67%. We concluded that, although effective for some subjects, the methodology needs to be individually validated before being applied. Moreover, time on task was not a particularly determining factor for classification, i.e., to induce cognitive fatigue. Further research will include other physiological signals and human–computer interaction variables.
