Predicting the Degree of Distracted Driving Based on fNIRS Functional Connectivity: A Pilot Study

Figures

• 

  Figure 1. Experimental driving course and design. (A) The experimental course was oval, flat and 40 m long. The red triangles in the figure indicate the safety cones placed along the course. (B) Beeping sounds were generated at random intervals during the 15-min experiment (inter-stimulus interval: 20–40 s). Participants were required to quickly brake and decelerate to 10 km/h speed if they recognized the beeping sound while traveling at 20 km/h. After decelerating, they accelerated again and were allowed to continue driving while maintaining a speed of 20 km/h.

• 

  Figure 2. Monte-Carlo simulation results over frontal cortex (A), temporal cortex (B), and parietal cortex (C). Red dots represent emitters, blue dots represent detectors, and yellow lines represent measurement channels, respectively (created using Homer 2 AtlasViewer; v2.8, p2.1: https://www.nitrc.org/frs/shownotes.php?release_id=3956). The color bar represents the spatial sensitivity of fNIRS measurements. The two-dimensional fNIRS montage using the International 10–20 measurement system as reference is presented in (D). Blue circles represent the short-range photosensitive area. The sensitivity value has mm−1 units and is displayed in log10 units. It yields the estimated value of optical density change by multiplying the sensitivity value by the absorption change in the measurement channel (in mm−1) and the area where the change occurred (in mm2).

• 

  Figure 3. The overview of our proposed method to build a predictive model of distracted driving. Cross-validation and feature selection were incorporated into the model-building process. The model was constructed for each participant using the dataset from multiple car braking trials. NB, number of resampling; BP, proportion of sampling; FP, stability of the features.

• 

  Figure 4. The distribution of BRT measured for each participant. The red dots indicate the outliers excluded from the regression modeling. The mean (μ) and standard deviation (σ) are also indicated for each participant.

• 

  Figure 5. Correlation between the predicted and measured BRTs for each participant. Note that the hyperparameters of these models were chosen based on the held-out test samples accuracy. Each connectome plot indicates the functional connectivity edges chosen via feature selection and used for the regression model. The bar graph summarizes Spearman's correlation as an accuracy measure for each individual model.

• 

  Figure 6. Eleven individual models with sufficient prediction accuracy were classified using hierarchical clustering based on the functional connectivity chosen via feature selection. The horizontal axis indicates the Jaccard distance among the models. The red line was drawn to analyze the five clusters. The circle plot and connectome indicate the common functional connectivity edges within each cluster. Each node channel was associated with the brain region determined by automated anatomical labeling (AAL) atlas. The nodes are colored based on which well-known functional network they belonged to: DAN, dorsal attention network; DMN, default mode network; SMN, Sensorimotor network; VAN, ventral attention network; FPN, frontoparietal network; SN, salience network.