Assessing the Driver's Current Level of Working Memory Load with High Density Functional Near-infrared Spectroscopy: A Realistic Driving Simulator Study.

Anirudh Unni,Jochem W Rieger,Meike Jipp,Klas Ihme

doi:10.3389/fnhum.2017.00167

Abstract

Cognitive overload or underload results in a decrease in human performance which may result in fatal incidents while driving. We envision that driver assistive systems which adapt their functionality to the driver’s cognitive state could be a promising approach to reduce road accidents due to human errors. This research attempts to predict variations of cognitive working memory load levels in a natural driving scenario with multiple parallel tasks and to reveal predictive brain areas. We used a modified version of the n-back task to induce five different working memory load levels (from 0-back up to 4-back) forcing the participants to continuously update, memorize, and recall the previous ‘n’ speed sequences and adjust their speed accordingly while they drove for approximately 60 min on a highway with concurrent traffic in a virtual reality driving simulator. We measured brain activation using multichannel whole head, high density functional near-infrared spectroscopy (fNIRS) and predicted working memory load level from the fNIRS data by combining multivariate lasso regression and cross-validation. This allowed us to predict variations in working memory load in a continuous time-resolved manner with mean Pearson correlations between induced and predicted working memory load over 15 participants of 0.61 [standard error (SE) 0.04] and a maximum of 0.8. Restricting the analysis to prefrontal sensors placed over the forehead reduced the mean correlation to 0.38 (SE 0.04), indicating additional information gained through whole head coverage. Moreover, working memory load predictions derived from peripheral heart rate parameters achieved much lower correlations (mean 0.21, SE 0.1). Importantly, whole head fNIRS sampling revealed increasing brain activation in bilateral inferior frontal and bilateral temporo-occipital brain areas with increasing working memory load levels suggesting that these areas are specifically involved in workload-related processing.

Highlights

Operating a car imposes high cognitive demands on the driver because information from the traffic signs, in-vehicle displays, and other traffic participants has to be integrated into a coherent situation representation
Data from fifteen participants are reported in the following functional near-infrared spectroscopy (fNIRS) analyses
Considering driving behavior, we find significant effects of the n-back condition on the time participants drove at the correct speed [χ2 = 12.02, p < 0.001, approximated r = −0.75, decrease per n-back level: 6.6%, standard error (SE) = 1.5%], the reaction time (χ2 = 4.25, p < 0.05, r = 0.47, increase per n-back level: 0.23 s, SE = 0.10) and the brake variance (χ2 = 7.44, p < 0.01, r = 0.58, increase per n-back level: 0.08, SE = 0.04)

Summary

Introduction

Operating a car imposes high cognitive demands on the driver because information from the traffic signs, in-vehicle displays, and other traffic participants has to be integrated into a coherent situation representation This representation needs to be dynamically updated with new information being especially challenging for the driver’s working memory (De Waard, 1996; da Silva et al, 2014). Parasuraman and colleagues (Parasuraman, 1987; Byrne and Parasuraman, 1996; Kaber et al, 2001) coined the term ‘adaptive automation’ for systems that aim to adapt the level of automation and support of assistance functions to the current state of the operator and to keep him or her at an optimal level of engagement and cognitive workload. Up to the present day, reliably detecting different levels of cognitive workload is challenging especially in realistic scenarios such as driving

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