View in EDS

Generalized EEG-Based Drowsiness Prediction System by Using a Self-Organizing Neural Fuzzy System.

Saved in:
Bibliographic Details
Title: Generalized EEG-Based Drowsiness Prediction System by Using a Self-Organizing Neural Fuzzy System.
Authors: Lin, Fu-Chang1, Ko, Li-Wei2, Chuang, Chun-Hsiang1, Su, Tung-Ping3, Lin, Chin-Teng1
Source: IEEE Transactions on Circuits & Systems. Part I: Regular Papers. Sep2012, Vol. 59 Issue 9, p2044-2055. 12p.
Subjects: Electroencephalography, Generalization, Drowsiness, Prediction theory, Self-organizing systems, Fuzzy neural networks, Electrooculography
Abstract: A generalized EEG-based Neural Fuzzy system to predict driver's drowsiness was proposed in this study. Driver's drowsy state monitoring system has been implicated as a causal factor for the safety driving issue, especially when the driver fell asleep or distracted in driving. However, the difficulties in developing such a system are lack of significant index for detecting the driver's drowsy state in real-time and the interference of the complicated noise in a realistic and dynamic driving environment. In our past studies, we found that the electroencephalogram (EEG) power spectrum changes were highly correlated with the driver's behavior performance especially the occipital component. Different from presented subject-dependent drowsy state monitor systems, whose system performance may decrease rapidly when different subject applies with the drowsiness detection model constructed by others, in this study, we proposed a generalized EEG-based Self-organizing Neural Fuzzy system to monitor and predict the driver's drowsy state with the occipital area. Two drowsiness prediction models, subject-dependent and generalized cross-subject predictors, were investigated in this study for system performance analysis. Correlation coefficients and root mean square errors are showed as the experimental results and interpreted the performances of the proposed system significantly better than using other traditional Neural Networks (p-value <0.038). Besides, the proposed EEG-based Self-organizing Neural Fuzzy system can be generalized and applied in the subjects' independent sessions. This unique advantage can be widely used in the real-life applications. [ABSTRACT FROM PUBLISHER]
Copyright of IEEE Transactions on Circuits & Systems. Part I: Regular Papers is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
FullText Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 79680216
AccessLevel: 6
PubType: Periodical
PubTypeId: serialPeriodical
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Generalized EEG-Based Drowsiness Prediction System by Using a Self-Organizing Neural Fuzzy System.
– Name: Author
  Label: Authors
  Group: Au
  Data: &lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Lin%2C+Fu-Chang%22&quot;&gt;Lin, Fu-Chang&lt;/searchLink&gt;&lt;relatesTo&gt;1&lt;/relatesTo&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Ko%2C+Li-Wei%22&quot;&gt;Ko, Li-Wei&lt;/searchLink&gt;&lt;relatesTo&gt;2&lt;/relatesTo&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Chuang%2C+Chun-Hsiang%22&quot;&gt;Chuang, Chun-Hsiang&lt;/searchLink&gt;&lt;relatesTo&gt;1&lt;/relatesTo&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Su%2C+Tung-Ping%22&quot;&gt;Su, Tung-Ping&lt;/searchLink&gt;&lt;relatesTo&gt;3&lt;/relatesTo&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Lin%2C+Chin-Teng%22&quot;&gt;Lin, Chin-Teng&lt;/searchLink&gt;&lt;relatesTo&gt;1&lt;/relatesTo&gt;
– Name: TitleSource
  Label: Source
  Group: Src
  Data: &lt;searchLink fieldCode=&quot;JN&quot; term=&quot;%22IEEE+Transactions+on+Circuits+%26+Systems%2E+Part+I%3A+Regular+Papers%22&quot;&gt;IEEE Transactions on Circuits &amp; Systems. Part I: Regular Papers&lt;/searchLink&gt;. Sep2012, Vol. 59 Issue 9, p2044-2055. 12p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: &lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Electroencephalography%22&quot;&gt;Electroencephalography&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Generalization%22&quot;&gt;Generalization&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Drowsiness%22&quot;&gt;Drowsiness&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Prediction+theory%22&quot;&gt;Prediction theory&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Self-organizing+systems%22&quot;&gt;Self-organizing systems&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Fuzzy+neural+networks%22&quot;&gt;Fuzzy neural networks&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Electrooculography%22&quot;&gt;Electrooculography&lt;/searchLink&gt;
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: A generalized EEG-based Neural Fuzzy system to predict driver&#39;s drowsiness was proposed in this study. Driver&#39;s drowsy state monitoring system has been implicated as a causal factor for the safety driving issue, especially when the driver fell asleep or distracted in driving. However, the difficulties in developing such a system are lack of significant index for detecting the driver&#39;s drowsy state in real-time and the interference of the complicated noise in a realistic and dynamic driving environment. In our past studies, we found that the electroencephalogram (EEG) power spectrum changes were highly correlated with the driver&#39;s behavior performance especially the occipital component. Different from presented subject-dependent drowsy state monitor systems, whose system performance may decrease rapidly when different subject applies with the drowsiness detection model constructed by others, in this study, we proposed a generalized EEG-based Self-organizing Neural Fuzzy system to monitor and predict the driver&#39;s drowsy state with the occipital area. Two drowsiness prediction models, subject-dependent and generalized cross-subject predictors, were investigated in this study for system performance analysis. Correlation coefficients and root mean square errors are showed as the experimental results and interpreted the performances of the proposed system significantly better than using other traditional Neural Networks (p-value &lt;0.038). Besides, the proposed EEG-based Self-organizing Neural Fuzzy system can be generalized and applied in the subjects&#39; independent sessions. This unique advantage can be widely used in the real-life applications. [ABSTRACT FROM PUBLISHER]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: &lt;i&gt;Copyright of IEEE Transactions on Circuits &amp; Systems. Part I: Regular Papers is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder&#39;s express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.&lt;/i&gt; (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=79680216
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1109/TCSI.2012.2185290
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 12
        StartPage: 2044
    Subjects:
      – SubjectFull: Electroencephalography
        Type: general
      – SubjectFull: Generalization
        Type: general
      – SubjectFull: Drowsiness
        Type: general
      – SubjectFull: Prediction theory
        Type: general
      – SubjectFull: Self-organizing systems
        Type: general
      – SubjectFull: Fuzzy neural networks
        Type: general
      – SubjectFull: Electrooculography
        Type: general
    Titles:
      – TitleFull: Generalized EEG-Based Drowsiness Prediction System by Using a Self-Organizing Neural Fuzzy System.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Lin, Fu-Chang
      – PersonEntity:
          Name:
            NameFull: Ko, Li-Wei
      – PersonEntity:
          Name:
            NameFull: Chuang, Chun-Hsiang
      – PersonEntity:
          Name:
            NameFull: Su, Tung-Ping
      – PersonEntity:
          Name:
            NameFull: Lin, Chin-Teng
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 09
              Text: Sep2012
              Type: published
              Y: 2012
          Identifiers:
            – Type: issn-print
              Value: 15498328
          Numbering:
            – Type: volume
              Value: 59
            – Type: issue
              Value: 9
          Titles:
            – TitleFull: IEEE Transactions on Circuits & Systems. Part I: Regular Papers
              Type: main
ResultId 1