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Cited by 29EEG and Brain-Computer InterfacesOptical Imaging and Spectroscopy TechniquesEmotion and Mood Recognition

Classification of cognitive load based on neurophysiological features from functional near-infrared spectroscopy and electrocardiography signals on n-back task

Kesedzic, Ivan, Sarlija, Marko, Bozek, Jelena, Popovic, Sinisa, Cosic, Kresimir·2021·IEEE Sensors Journal
DOI 10.1109/jsen.2020.3038032
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Abstract

Cognitive load can be estimated using individuals' task performance, their subjective measures, and neurophysiological measures. Neurophysiological measures, which among others include brain activation signals obtained with various brain imaging techniques, such as the functional near-infrared spectroscopy (fNIRS), and signals from the peripheral physiology, such as the electrocardiography (ECG) signal, allow an objective and continuous estimation of cognitive load. In this article, the fNIRS and ECG signals were simultaneously collected from 32 participants and used to classify three levels of cognitive load on n-back task. A set of 30 fNIRS and ECG features proposed in this article enables the classification of different levels of cognitive load on n-back task using the support vector machine (SVM), k-nearest neighbors (KNN), and linear discriminant analysis (LDA) classification models. When combining the fNIRS and ECG features, three difficulties of the n-back task were classified with the mean accuracies ranging from 61% to 67%, while two difficulties were classified with the mean accuracy ranging from 70% to 84%. The most important features in the classification are discussed. The results presented in this article extend the existing empirical evidence that combining brain imaging and peripheral physiology features increases the accuracy of multi-level cognitive load classification, thus further underscoring the importance of multimodal approach to cognitive load classification.

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Authors
Kesedzic, I., Sarlija, M., Bozek, J., Popovic, S., & Cosic, K.
ORCID
Venue
IEEE Sensors Journal
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2021-07-01
Volume
21
Issue
13
Pages
14131-14140
ISSN
  • 1530-437X (ISSN-L)
  • 1558-1748
  • 2379-9153
DOI
10.1109/jsen.2020.3038032
OpenAlex
W3102376463
Cited by
29
References
56
Citations by year
2026: 7 · 2025: 9 · 2024: 6 · 2023: 3 · 2022: 2 · 2021: 2
FWCI
0.94
Type
journal article
Language
en
Open access
closed
Licence
Locations
IEEE Sensors Journal (publishedVersion)
Funders
  • Croatian Science Foundation through the Project “Young Researcher’s Career Development—Training of Doctoral Students.”
  • European Regional Development Fund (KK.01.1.1.01.0009 (DATACROSS))
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Optical Imaging and Spectroscopy Techniques
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
Keywords
cognitive load classification, n-back task, functional near-infrared spectroscopy, electrocardiogram, multimodal fusion, brain activation, peripheral physiological signals, support vector machine, k-nearest neighbors, linear discriminant analysis, task difficulty

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