TY - GEN
T1 - Mental workload classification via hierarchical latent dictionary learning
T2 - 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019
AU - Parshi, Srinidhi
AU - Amin, Rafiul
AU - Azgomi, Hamid Fekri
AU - Faghih, Rose T.
N1 - Funding Information:
Srinidhi Parshi, Md. Rafiul Amin, Hamid Fekri Azgomi and Rose T. Faghih are with the Department of Electrical and Computer Engineering at the University of Houston, Houston, TX 77004 USA (e-mail:{sparshi, mamin, hfekriazgomi, rtfaghih}@uh.edu). This work was supported in part by NSF Grant 1755780. Correspondence should be addressed to senior author Rose T. Faghih.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/5
Y1 - 2019/5
N2 - Variations in the brain's blood oxygenation and deoxygenation reflect neuronal activation patterns, and can be measured using functional near infrared spectroscopy (fNIRS). We aim to utilize fNIRS to obtain insights into the dynamic functional connectivity of the brain as a function of the mental workload. Interpreting connectivity in the brain using noisy fNIRS data with low signal to noise ratio is challenging. To overcome the challenges with fNIRS data, we use a hierarchical latent dictionary learning approach. This approach provides covariance matrices to obtain the dynamic functional connectivity and neuronal activation patterns that change over time. We use features from the dynamic functional connectivity of the brain reflected in fNIRS data collected from the prefrontal cortex to investigate mental workload. In particular, we study three types of mental workload tasks called n-back tasks and perform binary classification for each n-back task compared to the other n-back tasks and rest condition using support vector machines. The results of our binary classification of various n-back tasks compared to the rest condition outperforms binary classification results reported previously.
AB - Variations in the brain's blood oxygenation and deoxygenation reflect neuronal activation patterns, and can be measured using functional near infrared spectroscopy (fNIRS). We aim to utilize fNIRS to obtain insights into the dynamic functional connectivity of the brain as a function of the mental workload. Interpreting connectivity in the brain using noisy fNIRS data with low signal to noise ratio is challenging. To overcome the challenges with fNIRS data, we use a hierarchical latent dictionary learning approach. This approach provides covariance matrices to obtain the dynamic functional connectivity and neuronal activation patterns that change over time. We use features from the dynamic functional connectivity of the brain reflected in fNIRS data collected from the prefrontal cortex to investigate mental workload. In particular, we study three types of mental workload tasks called n-back tasks and perform binary classification for each n-back task compared to the other n-back tasks and rest condition using support vector machines. The results of our binary classification of various n-back tasks compared to the rest condition outperforms binary classification results reported previously.
UR - http://www.scopus.com/inward/record.url?scp=85073026185&partnerID=8YFLogxK
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U2 - 10.1109/BHI.2019.8834636
DO - 10.1109/BHI.2019.8834636
M3 - Conference contribution
AN - SCOPUS:85073026185
T3 - 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings
BT - 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 19 May 2019 through 22 May 2019
ER -