Driving Fatigue Detection from EEG Using a Modified PCANet Method
المؤلفون المشاركون
She, Qingshan
Ma, Yuliang
Luo, Zhizeng
Zhang, Yingchun
Chen, Bin
Li, Rihui
Wang, Chushan
Wang, Jun
المصدر
Computational Intelligence and Neuroscience
العدد
المجلد 2019، العدد 2019 (31 ديسمبر/كانون الأول 2019)، ص ص. 1-9، 9ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2019-07-14
دولة النشر
مصر
عدد الصفحات
9
التخصصات الرئيسية
الملخص EN
The rapid development of the automotive industry has brought great convenience to our life, which also leads to a dramatic increase in the amount of traffic accidents.
A large proportion of traffic accidents were caused by driving fatigue.
EEG is considered as a direct, effective, and promising modality to detect driving fatigue.
In this study, we presented a novel feature extraction strategy based on a deep learning model to achieve high classification accuracy and efficiency in using EEG for driving fatigue detection.
EEG signals were recorded from six healthy volunteers in a simulated driving experiment.
The feature extraction strategy was developed by integrating the principal component analysis (PCA) and a deep learning model called PCA network (PCANet).
In particular, the principal component analysis (PCA) was used to preprocess EEG data to reduce its dimension in order to overcome the limitation of dimension explosion caused by PCANet, making this approach feasible for EEG-based driving fatigue detection.
Results demonstrated high and robust performance of the proposed modified PCANet method with classification accuracy up to 95%, which outperformed the conventional feature extraction strategies in the field.
We also identified that the parietal and occipital lobes of the brain were strongly associated with driving fatigue.
This is the first study, to the best of our knowledge, to practically apply the modified PCANet technique for EEG-based driving fatigue detection.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Ma, Yuliang& Chen, Bin& Li, Rihui& Wang, Chushan& Wang, Jun& She, Qingshan…[et al.]. 2019. Driving Fatigue Detection from EEG Using a Modified PCANet Method. Computational Intelligence and Neuroscience،Vol. 2019, no. 2019, pp.1-9.
https://search.emarefa.net/detail/BIM-1129465
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Ma, Yuliang…[et al.]. Driving Fatigue Detection from EEG Using a Modified PCANet Method. Computational Intelligence and Neuroscience No. 2019 (2019), pp.1-9.
https://search.emarefa.net/detail/BIM-1129465
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Ma, Yuliang& Chen, Bin& Li, Rihui& Wang, Chushan& Wang, Jun& She, Qingshan…[et al.]. Driving Fatigue Detection from EEG Using a Modified PCANet Method. Computational Intelligence and Neuroscience. 2019. Vol. 2019, no. 2019, pp.1-9.
https://search.emarefa.net/detail/BIM-1129465
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1129465
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر