Can arousing feedback rectify lapses in driving? Prediction from EEG power spectra

Lin, Chin‐Teng; Huang, Kuo‐How; Chuang, Chun‐Hsiang; Ko, Li-Wei; Jung, Tzyy-Ping

doi:10.1088/1741-2560/10/5/056024

Cited by 38 publications

(41 citation statements)

References 48 publications

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“…13,14,25,36 Several studies, have also explored the use of warnings as feedback to help individuals combat drowsiness or prevent cognitive lapses. 6-8, 25, 30-32, 37-40 Our previous studies further showed that warning signals improved task performance of individuals experiencing momentary cognitive lapses and reset the EEG spectra in the alpha and theta bands to alert baseline power, 25,32 suggesting that auditory feedback assisted subjects in reducing their drowsiness, reflected in both behavioral performance and brain activities. However, the above-mentioned studies defined cognitive fatigue in terms of behavioral performance (non-responsive to lane departures).…”

Section: Changes In Behavioral Performance With the Warning Systemmentioning

confidence: 99%

“…Our previous studies 25,31 delivered an auditory warning signal at 1,750 Hz to drivers to restore performance that had been decreased by fatigue, and thereby reduced the magnitude of cognitive lapses. Furthermore, we have explored the effects of arousal feedback to reduce cognitive lapses in situations requiring attention 25,32 and conduct an on-line lapse detection and mitigation system 28 in real-world environments.…”

Section: Introductionmentioning

confidence: 99%

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An EEG-Based Fatigue Detection and Mitigation System

Huang

Chuang

et al. 2016

Int. J. Neur. Syst.

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Research has indicated that fatigue is a critical factor in cognitive lapses because it negatively affects an individual's internal state, which is then manifested physiologically. This study explores neurophysiological changes, measured by electroencephalogram (EEG), due to fatigue. This study further demonstrates the feasibility of an online closed-loop EEG-based fatigue detection and mitigation system that detects physiological change and can thereby prevent fatigue-related cognitive lapses. More importantly, this work compares the efficacy of fatigue detection and mitigation between the EEG-based and a nonEEG-based random method. Twelve healthy subjects participated in a sustained-attention driving experiment. Each participant's EEG signal was monitored continuously and a warning was delivered in real-time to participants once the EEG signature of fatigue was detected. Study results indicate suppression of the alpha- and theta-power of an occipital component and improved behavioral performance following a warning signal; these findings are in line with those in previous studies. However, study results also showed reduced warning efficacy (i.e. increased response times (RTs) to lane deviations) accompanied by increased alpha-power due to the fluctuation of warnings over time. Furthermore, a comparison of EEG-based and nonEEG-based random approaches clearly demonstrated the necessity of adaptive fatigue-mitigation systems, based on a subject's cognitive level, to deliver warnings. Analytical results clearly demonstrate and validate the efficacy of this online closed-loop EEG-based fatigue detection and mitigation mechanism to identify cognitive lapses that may lead to catastrophic incidents in countless operational environments.

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Section: Changes In Behavioral Performance With the Warning Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An EEG-Based Fatigue Detection and Mitigation System

Huang

Chuang

et al. 2016

Int. J. Neur. Syst.

Self Cite

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“…In the real world, simulations to someone do not occur one by one but normally come at the same time, just like watching while listening. In this situation, the EEG dynamics of the brain is very difficult to recognize, such as the discrimination between neuronal activities related to visual from auditory stimuli [38][39][40][41][42][43][44][45]. This study revealed how multisensory information integrates into the human brain under inhibition and the results should be helpful for understanding how auditory inputs affect visual perception and behavior.…”

Section: Neural Oscillations Under Inhibition With Visual and Auditormentioning

confidence: 86%

Exploration of Brain Connectivity during Human Inhibitory Control Using Inter-Trial Coherence

Chikara

2020

Sensors

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Inhibitory control is a cognitive process that inhibits a response. It is used in everyday activities, such as driving a motorcycle, driving a car and playing a game. The effect of this process can be compared to the red traffic light in the real world. In this study, we investigated brain connectivity under human inhibitory control using the phase lag index and inter-trial coherence (ITC). The human brain connectivity gives a more accurate representation of the functional neural network. Results of electroencephalography (EEG), the data sets were generated from twelve healthy subjects during left and right hand inhibitions using the auditory stop-signal task, showed that the inter-trial coherence in delta (1–4 Hz) and theta (4–7 Hz) band powers increased over the frontal and temporal lobe of the brain. These EEG delta and theta band activities neural markers have been related to human inhibition in the frontal lobe. In addition, inter-trial coherence in the delta-theta and alpha (8–12 Hz) band powers increased at the occipital lobe through visual stimulation. Moreover, the highest brain connectivity was observed under inhibitory control in the frontal lobe between F3-F4 channels compared to temporal and occipital lobes. The greater EEG coherence and phase lag index in the frontal lobe is associated with the human response inhibition. These findings revealed new insights to understand the neural network of brain connectivity and underlying mechanisms during human response inhibition.

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“…Recently, some studies [10,11] concentrated on detecting the information on drowsiness available from a full EEG spectrum.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study on Classification Methods of Sleep Stages by Using EEG

Kim

2014

Journal of Korea Multimedia Society

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Electrophysiological recordings are considered a reliable method of assessing a person's alertness. Sleep medicine is asked to offer objective methods to measure daytime alertness, tiredness and sleepiness. As EEG signals are non-stationary, the conventional method of frequency analysis is not highly successful in recognition of alertness level. In this paper, EEG signals have been analyzed using wavelet transform as well as discrete wavelet transform and classification using statistical classifiers such as euclidean and mahalanobis distance classifiers and a promising method SVM (Support Vector Machine). As a result of simulation, the average values of accuracies for the Linear Discriminant Analysis (LDA)-Quadratic, k-Nearest Neighbors (k-NN)-Euclidean, and Linear SVM were 48%, 34.2%, and 86%, respectively. The experimental results show that SVM classification method offer the better performance for reliable classification of the EEG signal in comparison with the other classification methods.

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Can arousing feedback rectify lapses in driving? Prediction from EEG power spectra

Abstract: The findings of this study, in conjunction with previous studies on EEG correlates of behavioral lapses, might lead to a practical closed-loop system to predict, monitor and rectify behavioral lapses of human operators in attention-critical settings.

Cited by 38 publications

References 48 publications

An EEG-Based Fatigue Detection and Mitigation System

An EEG-Based Fatigue Detection and Mitigation System

Exploration of Brain Connectivity during Human Inhibitory Control Using Inter-Trial Coherence

A Comparative Study on Classification Methods of Sleep Stages by Using EEG

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