Influence of Task Combination on EEG Spectrum Modulation for Driver Workload Estimation

Lei, Shengguang; Roetting, Matthias

doi:10.1177/0018720811400601

Cited by 98 publications

(73 citation statements)

References 41 publications

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“…Lei and Roetting (2011) estimated driver workload from EEG spectrum modulation while participants performed a combination of lane-change and a working memory tasks in a driving simulator. They found an 8–12 Hz (alpha) oscillatory power decrease and a 4–8 Hz (theta) power increase with increasing workload.…”

Section: Introductionmentioning

confidence: 99%

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

Unni

Ihme

Jipp

et al. 2017

Front. Hum. Neurosci.

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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.

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Section: Introductionmentioning

confidence: 99%

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

Unni

Ihme

Jipp

et al. 2017

Front. Hum. Neurosci.

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“…For example, Lei et al [15] used EEG to assess driver's workload. They used a simulated driving task and a secondary n-back memory task.…”

Section: Related Workmentioning

confidence: 99%

Investigating Intrusiveness of Workload Adaptation

Putze¹,

Schultz²

2014

Proceedings of the 16th International Conference on Multimodal Interaction

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In this paper, we investigate how an automatic task assistant which can detect and react to a user's workload level is able to support the user in a complex, dynamic task. In a user study, we design a dispatcher scenario with low and high workload conditions and compare the effect of four support strategies with different levels of intrusiveness using objective and subjective metrics. We see that a more intrusive strategy results in higher efficiency and effectiveness, but is also less accepted by the participants. We also show that the benefit of supportive behavior depends on the user's workload level, i.e. adaptation to its changes are necessary. We describe and evaluate a Brain Computer Interface that is able to provide the necessary user state detection.

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“…The EEG signal is a representation of the brain's electrical activity recorded from electrodes placed on the scalp which can includes abundant information of the cognitive states such as alertness and arousal. Numerous studies suggest that delta (1-3 Hz), theta (4)(5)(6)(7), and alpha activities are highly correlated with fatigue, drowsiness, and poor task performance [6][7][8]. It has been used to assess the driver's performance for many years.…”

Section: Introductionmentioning

confidence: 99%

EEG-based Safety Driving Performance Estimation and Alertness Using Support Vector Machine

Sun¹,

Bi²,

Chen³

et al. 2015

IJSIA

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Influence of Task Combination on EEG Spectrum Modulation for Driver Workload Estimation

Abstract: One potential future application of this study is to establish a general driver workload estimator that uses EEG signals.

Cited by 98 publications

References 41 publications

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

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

Investigating Intrusiveness of Workload Adaptation

EEG-based Safety Driving Performance Estimation and Alertness Using Support Vector Machine

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