A Novel Method for Classifying Driver Mental Workload Under Naturalistic Conditions With Information From Near-Infrared Spectroscopy

Abstract

Driver cognitive distraction is a critical factor in road safety, and its evaluation, especially under real conditions, presents challenges to researchers and engineers. In this study, we considered mental workload from a secondary task as a potential source of cognitive distraction and aimed to estimate the increased cognitive load on the driver with a four-channel near-infrared spectroscopy (NIRS) device by introducing a machine-learning method for hemodynamic data. To produce added cognitive workload in a driver beyond just driving, two levels of an auditory presentation n-back task were used. A total of 60 experimental data sets from the NIRS device during two driving tasks were obtained and analyzed by machine-learning algorithms. We used two techniques to prevent overfitting of the classification models: (1) k-fold cross-validation and principal-component analysis, and (2) retaining 25% of the data (testing data) for testing of the model after classification. Six types of classifier were trained and tested: decision tree, discriminant analysis, logistic regression, the support vector machine, the nearest neighbor classifier, and the ensemble classifier. Cognitive workload levels were well classified from the NIRS data in the cases of subject-dependent classification (the accuracy of classification increased from 81.30 to 95.40%, and the accuracy of prediction of the testing data was 82.18 to 96.08%), subject 26 independent classification (the accuracy of classification increased from 84.90 to 89.50%, and the accuracy of prediction of the testing data increased from 84.08 to 89.91%), and channel-independent classification (classification 82.90%, prediction 82.74%). NIRS data in conjunction with an artificial intelligence method can therefore be used to classify mental workload as a source of potential cognitive distraction in real time under naturalistic conditions; this information may be utilized in driver assistance systems to prevent road accidents.

A Review of Psychophysiological Measures to Assess Cognitive States in Real-World Driving

“…Therefore, imaging of activity from deep cortical and subcortical sources (beyond the outer cortical mantle) is limited. Recent work has also employed wearable fNIRS systems (Piper et al, 2014 ; McKendrick et al, 2016 ; Le et al, 2018 ) and simultaneous collection of fNIRS and EEG (Kassab et al, 2018 ), which can enable real-world monitoring in ecologically valid settings.…”

“…As the development of ultra-portable systems grows (e.g., battery powered mobile systems, McKendrick et al, 2016 ), fNIRS will likely form a novel complement to the many other physiological measures discussed here, in part because of its unique capability to image neural hemodynamics and reveal changes in brain activity with improved spatial resolution compared to other portable and non-invasive neurophysiological methods (e.g., EEG; Ahn and Jun, 2017 ). For instance, a recent study adopted a wearable fNIRS system (with sensors placed on a baseball cap making it less intrusive) to measure cognitive distraction while driving (Le et al, 2018 ). Thus, while these methods are still in their infancy compared to many of the other methods discussed here, the ability to reveal neural mechanisms of cognitive states in real-world domains such as driving is promising.…”

Classification of Drivers' Workload Using Physiological Signals in Conditional Automation

“…Finally, Le et al ( 2018 ) recently considered using near-infrared spectroscopy to similarly classify drivers workload being cognitively distracted by a NDRT. Again, the n-back task was chosen for manipulating workload and 6 features were computed from the sensor data.…”

Demonstrating Brain-Level Interactions Between Visuospatial Attentional Demands and Working Memory Load While Driving Using Functional Near-Infrared Spectroscopy

“…Due to its robustness against motion artifacts and external electrical noise, fNIRS is suitable for applied settings (Masataka et al, 2015; Balardin et al, 2017) and has been used in actual driving (Yoshino et al, 2013a,b). FNIRS has shown to be sensitive toward changes in mental workload in the applied fields of simulated flight operation (Ayaz et al, 2012; Durantin et al, 2014), simulated urban rail driving (Li et al, 2018), as well as simulated (Unni et al, 2017; Xu et al, 2017) and actual car driving (Ahn Son et al, 2018). Further, fNIRS could detect elevated visual attention in curve driving, as indicated by increased activity in right premotor cortex, right frontal eye field, and bilateral prefrontal cortex (Oka et al, 2015).…”