Abstract
We studied the capability of a Hybrid functional neuroimaging technique to quantify human mental workload (MWL). We have used electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) as imaging modalities with 17 healthy subjects performing the letter n-back task, a standard experimental paradigm related to working memory (WM). The level of MWL was parametrically changed by variation of n from 0 to 3. Nineteen EEG channels were covering the whole-head and 19 fNIRS channels were located on the forehead to cover the most dominant brain region involved in WM. Grand block averaging of recorded signals revealed specific behaviors of oxygenated-hemoglobin level during changes in the level of MWL. A machine learning approach has been utilized for detection of the level of MWL. We extracted different features from EEG, fNIRS, and EEG+fNIRS signals as the biomarkers of MWL and fed them to a linear support vector machine (SVM) as train and test sets. These features were selected based on their sensitivity to the changes in the level of MWL according to the literature. We introduced a new category of features within fNIRS and EEG+fNIRS systems. In addition, the performance level of each feature category was systematically assessed. We also assessed the effect of number of features and window size in classification performance. SVM classifier used in order to discriminate between different combinations of cognitive states from binary- and multi-class states. In addition to the cross-validated performance level of the classifier other metrics such as sensitivity, specificity, and predictive values were calculated for a comprehensive assessment of the classification system. The Hybrid (EEG+fNIRS) system had an accuracy that was significantly higher than that of either EEG or fNIRS. Our results suggest that EEG+fNIRS features combined with a classifier are capable of robustly discriminating among various levels of MWL. Results suggest that EEG+fNIRS should be preferred to only EEG or fNIRS, in developing passive BCIs and other applications which need to monitor users' MWL.
Highlights
Mental workload (MWL) affects people who are interacting with computers and other devices
We have previously shown that aspects of neurovascular coupling (NVC) are characterizable by EEG+functional near-infrared spectroscopy (fNIRS), by taking advantage of the synergistic interaction between the modalities (Keles et al, 2016)
In (Figures 8f,g) we show the grand block average of all tasks v rest state for one specific fNIRS channel
Summary
Mental workload (MWL) affects people who are interacting with computers and other devices. According to the prevalent Multiple Resources theory (Navon and Gopher, 1979; Wickens, 2002), performing different tasks requires a subject to tap into a set of separate resources, which are limited in capacity and distributable among tasks (Horrey and Wickens, 2003). In the case of driving while having a phone conversation, in addition to the interference of resources the “engagement phenomenon” controls the outcome of multitasking scenario This happens when one of the tasks attracts so much attention that the advantage of separate resource demand would be eliminated (Strayer and Johnston, 2001; Strayer and Drews, 2007)
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