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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

The system diagram of the proposed OCLDM System. (A) The EEG headgear collected 4-channel brain activities from the lateral occipital area while a subject was performing the lane-drifting experiment. The mobile signal-processing platform received the acquired EEG raw data through Bluetooth, and the event markers generated from the lane-departure scene through an USB interface. Finally, the auditory feedback was delivered to the subject when the averaged EEG power across four channels was 3 dB over the alert baseline. (B) A photo of a subject performing the on-line driving experiment while wearing a 4-channel EEG headgear (the white small box attached on a flexible band) over the lateral occipital area.
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Figure 4: The system diagram of the proposed OCLDM System. (A) The EEG headgear collected 4-channel brain activities from the lateral occipital area while a subject was performing the lane-drifting experiment. The mobile signal-processing platform received the acquired EEG raw data through Bluetooth, and the event markers generated from the lane-departure scene through an USB interface. Finally, the auditory feedback was delivered to the subject when the averaged EEG power across four channels was 3 dB over the alert baseline. (B) A photo of a subject performing the on-line driving experiment while wearing a 4-channel EEG headgear (the white small box attached on a flexible band) over the lateral occipital area.

Mentions: Figure 4A shows the system diagram of the proposed OCLDM System. The system consists of two major components: (1) a mobile platform featuring the OCLDM algorithm, and (2) a mobile and wireless 4-channel headgear measuring EEG signals over the hair-bearing occipital regions with dry EEG sensors (Liao et al., 2011). The OCLDM System was implemented as an App on an Android-based platform (e.g., Samsung Galaxy S3). The smartphone has a Bluetooth module, 16 GB RAM, an ARM Cortex-A9 processor, Android (Ice Cream Sandwich) OS, and other components. When the App is launched, it can automatically search and connect to a nearby EEG headgear to receive data from the EEG acquisition headgear. In the mean time, the App opened an USB port to receive the events from a four-lane highway scene to synchronize the EEG data and scene events. The build-in speaker (or plug-in a ear set) of the smartphone delivers auditory warning signal once the OCLDM System detects that the subject is experiencing a cognitive lapse. Both the EEG data and scene-generated events could be logged onto either smartphone's build-in memory or an external microSD card for further analysis.


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)

The system diagram of the proposed OCLDM System. (A) The EEG headgear collected 4-channel brain activities from the lateral occipital area while a subject was performing the lane-drifting experiment. The mobile signal-processing platform received the acquired EEG raw data through Bluetooth, and the event markers generated from the lane-departure scene through an USB interface. Finally, the auditory feedback was delivered to the subject when the averaged EEG power across four channels was 3 dB over the alert baseline. (B) A photo of a subject performing the on-line driving experiment while wearing a 4-channel EEG headgear (the white small box attached on a flexible band) over the lateral occipital area.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 4: The system diagram of the proposed OCLDM System. (A) The EEG headgear collected 4-channel brain activities from the lateral occipital area while a subject was performing the lane-drifting experiment. The mobile signal-processing platform received the acquired EEG raw data through Bluetooth, and the event markers generated from the lane-departure scene through an USB interface. Finally, the auditory feedback was delivered to the subject when the averaged EEG power across four channels was 3 dB over the alert baseline. (B) A photo of a subject performing the on-line driving experiment while wearing a 4-channel EEG headgear (the white small box attached on a flexible band) over the lateral occipital area.
Mentions: Figure 4A shows the system diagram of the proposed OCLDM System. The system consists of two major components: (1) a mobile platform featuring the OCLDM algorithm, and (2) a mobile and wireless 4-channel headgear measuring EEG signals over the hair-bearing occipital regions with dry EEG sensors (Liao et al., 2011). The OCLDM System was implemented as an App on an Android-based platform (e.g., Samsung Galaxy S3). The smartphone has a Bluetooth module, 16 GB RAM, an ARM Cortex-A9 processor, Android (Ice Cream Sandwich) OS, and other components. When the App is launched, it can automatically search and connect to a nearby EEG headgear to receive data from the EEG acquisition headgear. In the mean time, the App opened an USB port to receive the events from a four-lane highway scene to synchronize the EEG data and scene events. The build-in speaker (or plug-in a ear set) of the smartphone delivers auditory warning signal once the OCLDM System detects that the subject is experiencing a cognitive lapse. Both the EEG data and scene-generated events could be logged onto either smartphone's build-in memory or an external microSD card for further analysis.

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