Distraction detection of driver based on EEG signals in a simulated driving with alternative secondary task

2021 ◽  
Author(s):  
Lirong Yan ◽  
Yuan Chen ◽  
Jiawen Zhang ◽  
Zhizhou Guan ◽  
Yibo Wu ◽  
...  
Keyword(s):  
Secondary Task ◽  
Eeg Signals ◽  
Simulated Driving

scholarly journals Detecting driver mental fatigue based on Electroencephalogram (EEG) signals during simulated driving

2021 ◽  
Vol 1070 (1) ◽  
pp. 012096
Author(s):  
S Pradeep Kumar ◽  
Suganiya Murugan ◽  
Jerritta Selvaraj ◽  
Arun Sahayadhas
Keyword(s):  
Mental Fatigue ◽  
Eeg Signals ◽  
Simulated Driving ◽  
Electroencephalogram Eeg

scholarly journals Neural Correlates of Simulated Driving While Performing a Secondary Task: A Review

2019 ◽  
Vol 10 ◽  
Author(s):  
Massimiliano Palmiero ◽  
Laura Piccardi ◽  
Maddalena Boccia ◽  
Francesca Baralla ◽  
Pierluigi Cordellieri ◽  
...  
Keyword(s):  
Secondary Task ◽  
Neural Correlates ◽  
Simulated Driving

scholarly journals Detection of Driver Braking Intention Using EEG Signals During Simulated Driving

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2863 ◽  
Author(s):  
Trung-Hau Nguyen ◽  
Wan-Young Chung
Keyword(s):  
Training Data ◽  
Detection Accuracy ◽  
Eeg Signals ◽  
Simulated Driving ◽  
Emergency Braking ◽  
Emergency Situations ◽  
Artificial Neural Network Classifier ◽  
Short Period ◽  
Novel Method ◽  
Electroencephalogram Eeg

In this work, we developed a novel system to detect the braking intention of drivers in emergency situations using electroencephalogram (EEG) signals. The system acquired eight-channel EEG and motion-sensing data from a custom-designed EEG headset during simulated driving. A novel method for accurately labeling the training data during an extremely short period after the onset of an emergency stimulus was introduced. Two types of features, including EEG band power-based and autoregressive (AR)-based, were investigated. It turned out that the AR-based feature in combination with artificial neural network classifier provided better detection accuracy of the system. Experimental results for ten subjects indicated that the proposed system could detect the emergency braking intention approximately 600 ms before the onset of the executed braking event, with high accuracy of 91%. Thus, the proposed system demonstrated the feasibility of developing a brain-controlled vehicle for real-world applications.

Event-related potentials and secondary task performance during simulated driving

2008 ◽  
Vol 40 (1) ◽  
pp. 1-7 ◽  
Author(s):  
A.E. Wester ◽  
K.B.E. Böcker ◽  
E.R. Volkerts ◽  
J.C. Verster ◽  
J.L. Kenemans
Keyword(s):  
Task Performance ◽  
Secondary Task ◽  
Event Related Potentials ◽  
Simulated Driving ◽  
Related Potentials

Characteristic Extraction of Fatigue Driver's EEG Signals Based on Wavelet Entropy

2013 ◽  
Vol 779-780 ◽  
pp. 1019-1022
Author(s):  
Ning Ning Zhang ◽  
Qiang Zhang
Keyword(s):  
Wavelet Transform ◽  
Brain Function ◽  
Eeg Signals ◽  
Transform Method ◽  
Wavelet Entropy ◽  
Simulated Driving ◽  
Characteristic Extraction ◽  
Eeg Data ◽  
Significant Difference ◽  
Electroencephalogram Eeg

This study aims to develop a method to detect drivers fatigue using the EEG signals. Experiments have been designed to test the subjects under simulated driving and actual driving, and the fatigue drivers Electroencephalogram (EEG) signals were collected. Wavelet transform method was applied to de-noise the raw EEG data. The H, R (H=α/β; R= (α+θ)/β) wavelet entropy were calculated. The results show that the fatigue drivers H, R wavelet entropy decreased after rest (P<0.05). It is concluded that there are significant difference in brain function between fatigue states and recovered after rest. It is shown that H, R wavelet entropy is an effective eigenvalue to measure drivers fatigue.

scholarly journals Brain Complex Network Characteristic Analysis of Fatigue during Simulated Driving Based on Electroencephalogram Signals

Entropy ◽  
2019 ◽  
Vol 21 (4) ◽  
pp. 353 ◽  
Author(s):  
Chunxiao Han ◽  
Xiaozhou Sun ◽  
Yaru Yang ◽  
Yanqiu Che ◽  
Yingmei Qin
Keyword(s):  
Complex Network ◽  
Traffic Accidents ◽  
Brain Regions ◽  
Eeg Signals ◽  
Characteristic Analysis ◽  
Simulated Driving ◽  
Complex Network Theory ◽  
Driving Fatigue ◽  
Delta Rhythm ◽  
Fatigue Driving

Fatigued driving is one of the major causes of traffic accidents. Frequent repetition of driving behavior for a long time may lead to driver fatigue, which is closely related to the central nervous system. In the present work, we designed a fatigue driving simulation experiment and collected the electroencephalogram (EEG) signals. Complex network theory was introduced to study the evolution of brain dynamics under different rhythms of EEG signals during several periods of the simulated driving. The results show that as the fatigue degree deepened, the functional connectivity and the clustering coefficients increased while the average shortest path length decreased for the delta rhythm. In addition, there was a significant increase of the degree centrality in partial channels on the right side of the brain for the delta rhythm. Therefore, it can be concluded that driving fatigue can cause brain complex network characteristics to change significantly for certain brain regions and certain rhythms. This exploration may provide a theoretical basis for further finding objective and effective indicators to evaluate the degree of driving fatigue and to help avoid fatigue driving.

When Hearing Does Not Mean Understanding: On the Neural Processing of Syntactically Complex Sentences by Listeners With Hearing Loss

2021 ◽  
pp. 1-13
Author(s):  
Margreet Vogelzang ◽  
Christiane M. Thiel ◽  
Stephanie Rosemann ◽  
Jochem W. Rieger ◽  
Esther Ruigendijk
Keyword(s):  
Hearing Loss ◽  
Sentence Processing ◽  
Secondary Task ◽  
Sentence Comprehension ◽  
Inferior Frontal Gyrus ◽  
Normal Hearing ◽  
Cognitive Demands ◽  
Sentence Complexity ◽  
Complex Sentences ◽  
And Task

Purpose Adults with mild-to-moderate age-related hearing loss typically exhibit issues with speech understanding, but their processing of syntactically complex sentences is not well understood. We test the hypothesis that listeners with hearing loss' difficulties with comprehension and processing of syntactically complex sentences are due to the processing of degraded input interfering with the successful processing of complex sentences. Method We performed a neuroimaging study with a sentence comprehension task, varying sentence complexity (through subject–object order and verb–arguments order) and cognitive demands (presence or absence of a secondary task) within subjects. Groups of older subjects with hearing loss ( n = 20) and age-matched normal-hearing controls ( n = 20) were tested. Results The comprehension data show effects of syntactic complexity and hearing ability, with normal-hearing controls outperforming listeners with hearing loss, seemingly more so on syntactically complex sentences. The secondary task did not influence off-line comprehension. The imaging data show effects of group, sentence complexity, and task, with listeners with hearing loss showing decreased activation in typical speech processing areas, such as the inferior frontal gyrus and superior temporal gyrus. No interactions between group, sentence complexity, and task were found in the neuroimaging data. Conclusions The results suggest that listeners with hearing loss process speech differently from their normal-hearing peers, possibly due to the increased demands of processing degraded auditory input. Increased cognitive demands by means of a secondary visual shape processing task influence neural sentence processing, but no evidence was found that it does so in a different way for listeners with hearing loss and normal-hearing listeners.

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