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Publication
Automatic cognitive fatigue detection using wearable fNIRS and machine learning
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Advisor(s)
Abstract(s)
Wearable sensors have increasingly been applied in healthcare to generate data and monitor
patients unobtrusively. Their application for Brain–Computer Interfaces (BCI) allows for unobtru sively 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.
Description
Keywords
Cognitive fatigue Functional near-infrared spectroscopy Machine learning Brain-computer interface . Faculdade de Ciências Exatas e da Engenharia
Citation
Varandas, R., Lima, R., Badia, S. B. I., Silva, H., & Gamboa, H. (2022). Automatic cognitive fatigue detection using wearable fNIRS and machine learning. Sensors, 22(11), 4010.
Publisher
MDPI