Employing cardiac and respiratory features extracted from fNIRS signals for mental workload classification

Liucija Svinkunaite; Jörn M. Horschig; Marianne J. Floor-Westerdijk

doi:10.1117/12.2587155

5 March 2021 Employing cardiac and respiratory features extracted from fNIRS signals for mental workload classification

Liucija Svinkunaite, Jörn M. Horschig, Marianne J. Floor-Westerdijk

Proceedings Volume 11638, Biophotonics in Exercise Science, Sports Medicine, Health Monitoring Technologies, and Wearables II; 116380H (2021) https://doi.org/10.1117/12.2587155
Event: SPIE BiOS, 2021, Online Only

Abstract

Functional Near-Infrared Spectroscopy (fNIRS) is gaining popularity in detection and classification of cognitive and emotional states. In addition to hemodynamic responses arising from functional activity changes in the brain areas of interest, fNIRS signals contain components related to other physiological processes, such as respiration (frequency oscillations around 0.3 Hz) and cardiac pulsation (around 1 Hz). While heart rate and respiration measures have been successfully used as separate modalities to assess mental workload, these components are often discarded in fNIRS studies during the pre-processing. In this study, we examined whether including features related to heart and breathing rate improves the accuracy of mental workload level classification. Data collected with wearable fNIRS devices from 14 healthy participants performing mental workload task (n-back) were used to extract features for the classification. Machine learning classifiers were trained and tested using conventional features separately and in combination with the features derived from the oscillatory activity of respiration and heart pulsation. By comparing the performance, we demonstrated the effect of including proposed features on the classification accuracy of mental workload. In future studies, the examined features might be beneficial for other classification problems where modulations in heart and breathing rates are expected.

Conference Presentation

Citation Download Citation

Liucija Svinkunaite, Jörn M. Horschig, and Marianne J. Floor-Westerdijk "Employing cardiac and respiratory features extracted from fNIRS signals for mental workload classification", Proc. SPIE 11638, Biophotonics in Exercise Science, Sports Medicine, Health Monitoring Technologies, and Wearables II, 116380H (5 March 2021); https://doi.org/10.1117/12.2587155

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available