Detection of Driver Vigilance Level Using EEG Signals and Driving Contexts

Guo, Zizheng; Pan, Yufan; Zhao, Guozhen; Cao, Shi; Zhang, Jun

doi:10.1109/tr.2017.2778754

Cited by 69 publications

(29 citation statements)

References 47 publications

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“…PSD determines the distribution of the signal power in a frequency range, facilitating the extraction of the most popular features in the context of the cognitive workload [ 55 ]. These features are defined as frequency bands and are Delta (0.5–4 Hz), Theta (4–8 Hz), Alpha (8–12 Hz), Beta (12–30 Hz), and Gamma (30–100 Hz) [ 23 , 56 , 57 ].…”

Section: Experimentation and Materialsmentioning

confidence: 99%

“…In these systems, the strategy employed to extract the information can cause a loss of vital data. In [ 23 ], the authors propose a system for detecting vigilance levels using EEG signals and combine SVM algorithms with multi-particle optimization, obtaining an 84.1% accuracy. The model displays a low prediction performance in some predictions due to the complexity of the data.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Feature Selection Model based on EEG Signals for Assessing the Cognitive Workload in Drivers

Becerra-Sánchez

Reyes-Muñoz

Ibáñez

2020

Sensors

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In recent years, research has focused on generating mechanisms to assess the levels of subjects’ cognitive workload when performing various activities that demand high concentration levels, such as driving a vehicle. These mechanisms have implemented several tools for analyzing the cognitive workload, and electroencephalographic (EEG) signals have been most frequently used due to their high precision. However, one of the main challenges in implementing the EEG signals is finding appropriate information for identifying cognitive states. Here, we present a new feature selection model for pattern recognition using information from EEG signals based on machine learning techniques called GALoRIS. GALoRIS combines Genetic Algorithms and Logistic Regression to create a new fitness function that identifies and selects the critical EEG features that contribute to recognizing high and low cognitive workloads and structures a new dataset capable of optimizing the model’s predictive process. We found that GALoRIS identifies data related to high and low cognitive workloads of subjects while driving a vehicle using information extracted from multiple EEG signals, reducing the original dataset by more than 50% and maximizing the model’s predictive capacity, achieving a precision rate greater than 90%.

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Section: Experimentation and Materialsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Feature Selection Model based on EEG Signals for Assessing the Cognitive Workload in Drivers

Becerra-Sánchez

Reyes-Muñoz

Ibáñez

2020

Sensors

View full text Add to dashboard Cite

show abstract

“…This makes it imperative to understand from the driver's perspective about when the situation becomes critical. While computer vision continues to be the preferred sensing modality for achieving the goal of assessing driver awareness, the use of bio-sensing systems in this context has received wide attention in recent times [1][2][3]. Most of these studies have used electroencephalogram (EEG) as the preferred bio-sensing modality.…”

Section: Introductionmentioning

confidence: 99%

On Assessing Driver Awareness of Situational Criticalities: Multi-modal Bio-Sensing and Vision-Based Analysis, Evaluations, and Insights

Siddharth

Trivedi

2020

Brain Sciences

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Automobiles for our roadways are increasingly utilizing advanced driver assistance systems. These changes require us to develop novel perception systems not only for accurately understanding the situations and context of these vehicles, but also to understand the awareness of the driver in differentiating between safe and critical situations. The research presented in this paper focused on this specific problem. Even after the development of wearable and compact multi-modal bio-sensing systems in recent years, their application in driver awareness context has been scarcely explored. The capability of simultaneously recording different kinds of bio-sensing data in addition to traditionally used computer vision systems provide exciting opportunities to explore the limitations of these modalities. In this work, we explore the applications of three different bio-sensing modalities namely electroencephalogram (EEG), photoplethysmogram (PPG) and galvanic skin response (GSR) along with a camera-based vision system in driver awareness context. We assess the information from these sensors independently and together using both signal processing and deep learning tools. We show that our methods outperform previously reported studies in the context of monitoring driver awareness and detecting hazardous/non-hazardous situations for short time scales of two-seconds. We verify our methods by collecting user data on twelve subjects for two real-world driving datasets among which one is publicly available (KITTI dataset) and one that we collect ourselves (LISA dataset) with the vehicle being driven in an autonomous mode. This work presents an exhaustive evaluation of multiple sensor modalities on two different datasets for attention monitoring and hazardous events classification.

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“…In these systems, the strategy to extract the information could cause a loss of vital data. In [16], the authors propose a system to detect vigilance levels using EEG signals and combine SVM algorithms with multi-particle optimization obtaining 84.1% accuracy. The model presented a low prediction performance in some predictions due to the complexity of the data.…”

Section: Introductionmentioning

confidence: 99%

Feature Selection Model based on EEG signals to Assess the Cognitive Workload in Drivers

Becerra-Sánchez¹,

Reyes²,

Ibáñez³

2020

Preprint

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In recent years, research has focused on generating mechanisms to assess the levels of subjects' cognitive workload when performing various activities that demand high concentration levels, such as driving a vehicle. These mechanisms have implemented several tools to analyze cognitive workload where the electroencephalographic (EEG) signals are the most used due to its high precision. However, one of the main challenges in the EEG signals implementing is finding the appropriate information to identify cognitive states. Here we show a new feature selection model for pattern recognition using information from EEG signals based on machine learning techniques called GALoRIS. GALoRIS combines Genetic Algorithms and Logistic Regression to create a new fitness function that identifies and selects the critical EEG features that contribute to recognizing high and low cognitive workload and structures a new dataset capable of optimizing the model's predictive process. We found that GALoRIS identifies data related to high and low cognitive workload of subjects while driving a vehicle using information extracted from multiple EEG signals, reducing the original dataset by more than 50%, maximizing the model's predictive capacity-achieving a precision rate greater than 90%.

show abstract

Detection of Driver Vigilance Level Using EEG Signals and Driving Contexts

Cited by 69 publications

References 47 publications

Feature Selection Model based on EEG Signals for Assessing the Cognitive Workload in Drivers

Feature Selection Model based on EEG Signals for Assessing the Cognitive Workload in Drivers

On Assessing Driver Awareness of Situational Criticalities: Multi-modal Bio-Sensing and Vision-Based Analysis, Evaluations, and Insights

Feature Selection Model based on EEG signals to Assess the Cognitive Workload in Drivers

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