Developing an EEG based On-line Closed-loop Lapse Detection and Mitigation System
In America, sixty percent 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...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2014-10-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fnins.2014.00321/full |
| _version_ | 1818994279218937856 |
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| author | Yu-Te eWang Yu-Te eWang Kuan-Chih eHuang Chun-Shu eWei Chun-Shu eWei Teng-Yi eHuang Li-Wei eKo Chin-Teng eLin Chung-Kuan eCheng Chung-Kuan eCheng Tzyy-Ping eJung Tzyy-Ping eJung Tzyy-Ping eJung |
| author_facet | Yu-Te eWang Yu-Te eWang Kuan-Chih eHuang Chun-Shu eWei Chun-Shu eWei Teng-Yi eHuang Li-Wei eKo Chin-Teng eLin Chung-Kuan eCheng Chung-Kuan eCheng Tzyy-Ping eJung Tzyy-Ping eJung Tzyy-Ping eJung |
| author_sort | Yu-Te eWang |
| collection | DOAJ |
| description | In America, sixty percent 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-realty environment. When the subjects experienced lapses or failed to respond to events during the experiment, auditory feedback 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 feedback to subjects suffering momentary cognitive lapses, and assess the efficacy of the feedback 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. |
| first_indexed | 2024-12-20T20:55:25Z |
| format | Article |
| id | doaj.art-ed6e3ded1f0a4b51bdc361ecdce75d48 |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-12-20T20:55:25Z |
| publishDate | 2014-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-ed6e3ded1f0a4b51bdc361ecdce75d482022-12-21T19:26:50ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2014-10-01810.3389/fnins.2014.0032186921Developing an EEG based On-line Closed-loop Lapse Detection and Mitigation SystemYu-Te eWang0Yu-Te eWang1Kuan-Chih eHuang2Chun-Shu eWei3Chun-Shu eWei4Teng-Yi eHuang5Li-Wei eKo6Chin-Teng eLin7Chung-Kuan eCheng8Chung-Kuan eCheng9Tzyy-Ping eJung10Tzyy-Ping eJung11Tzyy-Ping eJung12University of California San DiegoSwartz Center for Computational Neuroscience, Institute for Neural ComputationNational Chiao-Tung UniversityUniversity of California San DiegoSwartz Center for Computational Neuroscience, Institute for Neural ComputationNational Chiao-Tung UniversityNational Chiao-Tung UniversityNational Chiao-Tung UniversityUniversity of California San DiegoUniversity of California San DiegoUniversity of California San DiegoSwartz Center for Computational Neuroscience, Institute for Neural ComputationUniversity of California San DiegoIn America, sixty percent 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-realty environment. When the subjects experienced lapses or failed to respond to events during the experiment, auditory feedback 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 feedback to subjects suffering momentary cognitive lapses, and assess the efficacy of the feedback 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.http://journal.frontiersin.org/Journal/10.3389/fnins.2014.00321/fullFatigueBrain computer interface (BCI)electroencephalogram (EEG)drivingsmartphonedrowsiness |
| spellingShingle | Yu-Te eWang Yu-Te eWang Kuan-Chih eHuang Chun-Shu eWei Chun-Shu eWei Teng-Yi eHuang Li-Wei eKo Chin-Teng eLin Chung-Kuan eCheng Chung-Kuan eCheng Tzyy-Ping eJung Tzyy-Ping eJung Tzyy-Ping eJung Developing an EEG based On-line Closed-loop Lapse Detection and Mitigation System Frontiers in Neuroscience Fatigue Brain computer interface (BCI) electroencephalogram (EEG) driving smartphone drowsiness |
| title | Developing an EEG based On-line Closed-loop Lapse Detection and Mitigation System |
| title_full | Developing an EEG based On-line Closed-loop Lapse Detection and Mitigation System |
| title_fullStr | Developing an EEG based On-line Closed-loop Lapse Detection and Mitigation System |
| title_full_unstemmed | Developing an EEG based On-line Closed-loop Lapse Detection and Mitigation System |
| title_short | Developing an EEG based On-line Closed-loop Lapse Detection and Mitigation System |
| title_sort | developing an eeg based on line closed loop lapse detection and mitigation system |
| topic | Fatigue Brain computer interface (BCI) electroencephalogram (EEG) driving smartphone drowsiness |
| url | http://journal.frontiersin.org/Journal/10.3389/fnins.2014.00321/full |
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