Effect of alcohol-dependent EEG on the traffic signal recognition

Wang, H.; He, Feng; Du, Jieqiong; Liu, C.; Zhao, Haibin

doi:10.1109/itab.2008.4570536

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…Başar and Güntekin (2008) in their review on the major changes in brain oscillations in patients with alcoholism and those with genetic disorders, has reported the following effects: i) the reduction of amplitude in alcoholics while taking VEP signal during a visual oddball task (Jones et al, 2006); ii) increase in α, β coherence (Winterer et al, 2003); and iii) increased variation in low voltage α waves (Enoch et al, 1999). Further, Wang et al (2008) studied the time course of the alcohol-effect on the traffic signal recognition and showed the enhanced delta power of EEG in alcoholics. Studies also have shown a reduced activity in δ, θ, α1 bands and P300 amplitudes of the off springs of alcoholics as compared to age matched controls (Kamarajan et al, 2006;Rangaswamy et al, 2007).…”

Section: Reduction In the Amplitude Of Error Related Negativity (Ern)mentioning

confidence: 99%

EEG Based Detection of Alcoholics

Shri

Sriraam

2012

International Journal of Biomedical and Clinical Engineering

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The short term and long term effects of alcohol on various organs of the body, especially on the human brain is well established by numerous studies. Invasive methods such as Transcranial Magnetic Stimulation (TMS) and non invasive imaging techniques such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), and functional MRI activated electro-encephalogram (EEG) have been used to study the changes in EEG activity due to alcoholism. Even with the advent of neuro imaging techniques, EEG happens to be an important tool for brain study providing a non- invasive and cost effective method to detect the effects of alcohol on the human brain. This paper discusses the harmful effects of alcohol on different organs of the body. The advances in the development of EEG signal processing algorithms over the past decade for alcoholic detection are reviewed and their limitations are reported. Further the use of EEG for mass screening of alcoholics and biometric application is discussed in detail.

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Section: Reduction In the Amplitude Of Error Related Negativity (Ern)mentioning

confidence: 99%

EEG Based Detection of Alcoholics

Shri

Sriraam

2012

International Journal of Biomedical and Clinical Engineering

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“…Appropriate selection of EEG signal channel can significantly reduce the computational complexity and computation time. [ 16 17 ]…”

Section: Introductionmentioning

confidence: 99%

An Efficient Method for Classification of Alcoholic and Normal Electroencephalogram Signals Based on Selection of an Appropriate Feature

Behzadfar¹,

Dorvashi²,

Shahgholian³

2023

Journal of Medical Signals &Amp; Sensors

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Background: Alcohol addiction contributes to disorders in brain's normal patterns. Analysis of electroencephalogram (EEG) signal helps to diagnose and classify alcoholic and normal EEG signal. Methods: One-second EEG signal was applied to classify alcoholic and normal EEG signal. To determine discriminative feature and EEG channel between the alcoholic and normal EEG signal, different frequency and non-frequency features of EEG signal, including power of EEG signal, permutation entropy (PE), approximate entropy (ApEn), katz fractal dimension (katz FD) and Petrosion fractal dimension (Petrosion FD) were extracted from alcoholic and normal EEG signal. Statistical analysis and Davis-Bouldin criterion (DB) were utilized to specify and select most discriminative feature and EEG channel between the alcoholic and normal EEG signal. Results: Results of statistical analysis and DB criterion showed that the Katz FD in FP2 channel showed the best discrimination between the alcoholic and normal EEG signal. The Katz FD in FP2 channel showed the accuracies of 98.77% and 98.5% by two classifiers with 10-fold cross validation. Conclusion: This method helps to diagnose alcoholic and normal EEG signal with the minimum number of feature and channel, which provides low computational complexity. This is helpful to faster and more accurate classification of normal and alcoholic subjects.

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“…Among these techniques, electroencephalography (EEG) is a reliable and significant method of measuring neurophysiological activity in the human brain and the psychological state of drivers when driving. Using advanced data mining techniques, the EEG signal can be utilized to identify a driver’s alertness (Chuang et al, 2015), to predict the distraction (Wang et al, 2015), to study a driver’s perception of signal lights (Wang et al, 2008), to monitor a driver’s driving states (Peng and Wu, 2009), and to predict a driver’s intention to emergency brake (Kim et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Correlation Among Behavior, Personality, and Electroencephalography Revealed by a Simulated Driving Experiment

et al. 2019

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Drivers play the most important role in the human-vehicle-environment system and driving behaviors are significantly influenced by the cognitive state of the driver and his/her personality. In this paper, we aimed to explore the correlation among driving behaviors, personality and electroencephalography (EEG) using a simulated driving experiment. A total of 36 healthy subjects participated in the study. The 64-channel EEG data and the driving data, including the real-time position of the vehicle, the rotation angle of the steering wheel and the speed were acquired simultaneously during driving. The Cattell 16 Personality Factor Questionnaire (16PF) was utilized to evaluate the personalities of subjects. Through hierarchical clustering of the 16PF personality traits, the subjects were divided into four groups, i.e., the Inapprehension group, Insensitivity group, Apprehension group and the Unreasoning group, named after their representative personality trait. Their driving performance and turning behaviors were compared and EEG preprocessing, source reconstruction and the comparisons among the four groups were performed using Statistical Parameter Mapping (SPM). The turning process of the subjects can be formulated into two steps, rotating the steering wheel toward the turning direction and entering the turn, and then rotating the steering wheel back and leaving the turn. The bilateral frontal gyrus was found to be activated when turning left and right, which might be associated with its function in attention, decision-making and executive control functions in visual-spatial and visual-motor processes. The Unreasoning group had the worst driving performance with highest rates of car collision and the most intensive driving action, which was related to a higher load of visual spatial attention and decision making, when the occipital and superior frontal areas played a very important role. Apprehension (O) and Tension (Q4) had a positive correlation, and Reasoning (B) had a negative correlation with dangerous driving behaviors. Our results demonstrated the close correlation among driving behaviors, personality and EEG and may be taken as a reference for the prediction and precaution of dangerous driving behaviors in people with specific personality traits.

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Effect of alcohol-dependent EEG on the traffic signal recognition

Cited by 4 publications

References 11 publications

EEG Based Detection of Alcoholics

EEG Based Detection of Alcoholics

An Efficient Method for Classification of Alcoholic and Normal Electroencephalogram Signals Based on Selection of an Appropriate Feature

Correlation Among Behavior, Personality, and Electroencephalography Revealed by a Simulated Driving Experiment

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