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Developing an EEG-based on-line closed-loop lapse detection and mitigation system.

Wang YT, Huang KC, Wei CS, Huang TY, Ko LW, Lin CT, Cheng CK, Jung TP - Front Neurosci (2014)

Bottom Line: However, the arousing auditory signals were not always effective.The EEG spectra exhibited statistically significant differences between effective and ineffective arousing signals, suggesting that EEG spectra could be used as a countermeasure of the efficacy of arousing signals.The on-line testing results of the OCLDM System validated the efficacy of the arousing signals in improving subjects' response times to the subsequent lane-departure events.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Engineering, Jacobs School of Engineering, University of California San Diego La Jolla, CA, USA ; Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego La Jolla, CA, USA.

ABSTRACT
In America, 60% of adults reported that they have driven a motor vehicle while feeling drowsy, and at least 15-20% of fatal car accidents are fatigue-related. This study translates previous laboratory-oriented neurophysiological research to design, develop, and test an On-line Closed-loop Lapse Detection and Mitigation (OCLDM) System featuring a mobile wireless dry-sensor EEG headgear and a cell-phone based real-time EEG processing platform. Eleven subjects participated in an event-related lane-keeping task, in which they were instructed to manipulate a randomly deviated, fixed-speed cruising car on a 4-lane highway. This was simulated in a 1st person view with an 8-screen and 8-projector immersive virtual-reality environment. When the subjects experienced lapses or failed to respond to events during the experiment, auditory warning was delivered to rectify the performance decrements. However, the arousing auditory signals were not always effective. The EEG spectra exhibited statistically significant differences between effective and ineffective arousing signals, suggesting that EEG spectra could be used as a countermeasure of the efficacy of arousing signals. In this on-line pilot study, the proposed OCLDM System was able to continuously detect EEG signatures of fatigue, deliver arousing warning to subjects suffering momentary cognitive lapses, and assess the efficacy of the warning in near real-time to rectify cognitive lapses. The on-line testing results of the OCLDM System validated the efficacy of the arousing signals in improving subjects' response times to the subsequent lane-departure events. This study may lead to a practical on-line lapse detection and mitigation system in real-world environments.

No MeSH data available.


Related in: MedlinePlus

Average component EEG power changes in alpha (top panel) and theta (bottom panel) bands from the bilateral occipital components (lower right corner). All the trials are aligned to the lane-deviation onsets at time 0 s (vertical solid black line). The red, light blue, dark blue, and black traces are the averaged spectra of trials with effective feedback, with ineffective feedback, without feedback, and in alertness, respectively. The green horizontal line indicates the statistically significant differences (p < 0.01) between trials with effective feedback and without feedback. The brown indicates the statistically significant differences (p < 0.01) between trials with effective feedback and ineffective feedback.
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Figure 3: Average component EEG power changes in alpha (top panel) and theta (bottom panel) bands from the bilateral occipital components (lower right corner). All the trials are aligned to the lane-deviation onsets at time 0 s (vertical solid black line). The red, light blue, dark blue, and black traces are the averaged spectra of trials with effective feedback, with ineffective feedback, without feedback, and in alertness, respectively. The green horizontal line indicates the statistically significant differences (p < 0.01) between trials with effective feedback and without feedback. The brown indicates the statistically significant differences (p < 0.01) between trials with effective feedback and ineffective feedback.

Mentions: Next, this study explored temporal spectral dynamics preceding, during and following fatigue-related behavioral lapses and following arousing warning. Figure 3 shows time courses of spectral changes in the bilateral occipital area following ineffective warning (light blue trace), effective warning (red trace), and without warning (dark blue trace), compared to those of the alert trials (black trace). Figure 3 shows that both theta- an alpha-band power steadily increased prior to the lane-departure onset (at time 0 s). Again, the trends of steady increasing theta- and alpha-band power leading to behavioral lapses in the three groups of drowsy trials were nearly identical, indicating the robustness of the theta and alpha augmentation preceding the behavioral lapses.


Developing an EEG-based on-line closed-loop lapse detection and mitigation system.

Wang YT, Huang KC, Wei CS, Huang TY, Ko LW, Lin CT, Cheng CK, Jung TP - Front Neurosci (2014)

Average component EEG power changes in alpha (top panel) and theta (bottom panel) bands from the bilateral occipital components (lower right corner). All the trials are aligned to the lane-deviation onsets at time 0 s (vertical solid black line). The red, light blue, dark blue, and black traces are the averaged spectra of trials with effective feedback, with ineffective feedback, without feedback, and in alertness, respectively. The green horizontal line indicates the statistically significant differences (p < 0.01) between trials with effective feedback and without feedback. The brown indicates the statistically significant differences (p < 0.01) between trials with effective feedback and ineffective feedback.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4195274&req=5

Figure 3: Average component EEG power changes in alpha (top panel) and theta (bottom panel) bands from the bilateral occipital components (lower right corner). All the trials are aligned to the lane-deviation onsets at time 0 s (vertical solid black line). The red, light blue, dark blue, and black traces are the averaged spectra of trials with effective feedback, with ineffective feedback, without feedback, and in alertness, respectively. The green horizontal line indicates the statistically significant differences (p < 0.01) between trials with effective feedback and without feedback. The brown indicates the statistically significant differences (p < 0.01) between trials with effective feedback and ineffective feedback.
Mentions: Next, this study explored temporal spectral dynamics preceding, during and following fatigue-related behavioral lapses and following arousing warning. Figure 3 shows time courses of spectral changes in the bilateral occipital area following ineffective warning (light blue trace), effective warning (red trace), and without warning (dark blue trace), compared to those of the alert trials (black trace). Figure 3 shows that both theta- an alpha-band power steadily increased prior to the lane-departure onset (at time 0 s). Again, the trends of steady increasing theta- and alpha-band power leading to behavioral lapses in the three groups of drowsy trials were nearly identical, indicating the robustness of the theta and alpha augmentation preceding the behavioral lapses.

Bottom Line: However, the arousing auditory signals were not always effective.The EEG spectra exhibited statistically significant differences between effective and ineffective arousing signals, suggesting that EEG spectra could be used as a countermeasure of the efficacy of arousing signals.The on-line testing results of the OCLDM System validated the efficacy of the arousing signals in improving subjects' response times to the subsequent lane-departure events.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Engineering, Jacobs School of Engineering, University of California San Diego La Jolla, CA, USA ; Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego La Jolla, CA, USA.

ABSTRACT
In America, 60% of adults reported that they have driven a motor vehicle while feeling drowsy, and at least 15-20% of fatal car accidents are fatigue-related. This study translates previous laboratory-oriented neurophysiological research to design, develop, and test an On-line Closed-loop Lapse Detection and Mitigation (OCLDM) System featuring a mobile wireless dry-sensor EEG headgear and a cell-phone based real-time EEG processing platform. Eleven subjects participated in an event-related lane-keeping task, in which they were instructed to manipulate a randomly deviated, fixed-speed cruising car on a 4-lane highway. This was simulated in a 1st person view with an 8-screen and 8-projector immersive virtual-reality environment. When the subjects experienced lapses or failed to respond to events during the experiment, auditory warning was delivered to rectify the performance decrements. However, the arousing auditory signals were not always effective. The EEG spectra exhibited statistically significant differences between effective and ineffective arousing signals, suggesting that EEG spectra could be used as a countermeasure of the efficacy of arousing signals. In this on-line pilot study, the proposed OCLDM System was able to continuously detect EEG signatures of fatigue, deliver arousing warning to subjects suffering momentary cognitive lapses, and assess the efficacy of the warning in near real-time to rectify cognitive lapses. The on-line testing results of the OCLDM System validated the efficacy of the arousing signals in improving subjects' response times to the subsequent lane-departure events. This study may lead to a practical on-line lapse detection and mitigation system in real-world environments.

No MeSH data available.


Related in: MedlinePlus