Comparison of Different Features and Classifiers for Driver Fatigue Detection Based on a Single EEG Channel
المؤلف
المصدر
Computational and Mathematical Methods in Medicine
العدد
المجلد 2017، العدد 2017 (31 ديسمبر/كانون الأول 2017)، ص ص. 1-9، 9ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2017-01-31
دولة النشر
مصر
عدد الصفحات
9
التخصصات الرئيسية
الملخص EN
Driver fatigue has become an important factor to traffic accidents worldwide, and effective detection of driver fatigue has major significance for public health.
The purpose method employs entropy measures for feature extraction from a single electroencephalogram (EEG) channel.
Four types of entropies measures, sample entropy (SE), fuzzy entropy (FE), approximate entropy (AE), and spectral entropy (PE), were deployed for the analysis of original EEG signal and compared by ten state-of-the-art classifiers.
Results indicate that optimal performance of single channel is achieved using a combination of channel CP4, feature FE, and classifier Random Forest (RF).
The highest accuracy can be up to 96.6%, which has been able to meet the needs of real applications.
The best combination of channel + features + classifier is subject-specific.
In this work, the accuracy of FE as the feature is far greater than the Acc of other features.
The accuracy using classifier RF is the best, while that of classifier SVM with linear kernel is the worst.
The impact of channel selection on the Acc is larger.
The performance of various channels is very different.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Hu, Jianfeng. 2017. Comparison of Different Features and Classifiers for Driver Fatigue Detection Based on a Single EEG Channel. Computational and Mathematical Methods in Medicine،Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1142162
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Hu, Jianfeng. Comparison of Different Features and Classifiers for Driver Fatigue Detection Based on a Single EEG Channel. Computational and Mathematical Methods in Medicine No. 2017 (2017), pp.1-9.
https://search.emarefa.net/detail/BIM-1142162
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Hu, Jianfeng. Comparison of Different Features and Classifiers for Driver Fatigue Detection Based on a Single EEG Channel. Computational and Mathematical Methods in Medicine. 2017. Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1142162
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1142162
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