Mental workload classification via hierarchical latent dictionary learning: A functional near infrared spectroscopy study

Srinidhi Parshi, Rafiul Amin, Hamid Fekri Azgomi, Rose T. Faghih

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    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.

    Original languageEnglish (US)
    Title of host publication2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    ISBN (Electronic)9781728108483
    DOIs
    StatePublished - May 2019
    Event2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Chicago, United States
    Duration: May 19 2019May 22 2019

    Publication series

    Name2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings

    Conference

    Conference2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019
    Country/TerritoryUnited States
    CityChicago
    Period5/19/195/22/19

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Signal Processing
    • Information Systems and Management
    • Biomedical Engineering
    • Health Informatics
    • Radiology Nuclear Medicine and imaging

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