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Multimodal Cognitive Workload Assessment Using EEG, fNIRS, ECG, EOG, PPG, and Eye-tracking
Abstract   Open access

Multimodal Cognitive Workload Assessment Using EEG, fNIRS, ECG, EOG, PPG, and Eye-tracking

Jesse Mark, Adrian Curtin, Amanda Kraft, Amanda Sargent, Alison Perez, Leah Friedman, Amanda Barkan, Trevor Sands, William Casebeer, Matthias Ziegler, …
Frontiers in human neuroscience, v 12
2018
url
https://doi.org/10.3389/conf.fnhum.2018.227.00106View
Published, Version of Record (VoR) Open CC BY V4.0

Abstract

Mental workload is a measure of the cognitive effort required to successfully perform a task, and is a function of task difficulty and individual expertise. Understanding the mental workload which is disassociated from the behavioral performance can be used to improve interface design of complex systems and efficiency of human-machine teaming (Ayaz et al 2012). Functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) are portable neuroimaging modalities that can have wearable, wireless, and battery-operated sensors to capture neural correlates with complex and natural tasks, aligned with the neuroergonomic approach (Parasuraman and Wilson, 2008; Gramann et al 2017). Recent studies we have conducted have demonstrated the capability of fNIRS and EEG to measure workload in working memory tasks (Ayaz et al 2012; Choe et al 2016; Liu et al., 2017). In this study, we look at fNIRS, EEG, heart rate (ECG), photoplethysmograph (PPG), and several eye movement-related metrics (EOG and eye-tracking) to capture multi-dimensional biomarkers of workload in brain and body measures.

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