Using EEG to detect driving fatigue based on common spatial pattern and support vector machine

Wang, Li; Johnson, David M.; Lin, Yingzi

doi:10.3906/elk-2008-83

Cited by 18 publications

(3 citation statements)

References 32 publications

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“…Bu sınıflandırma algoritması, istatistiksel öğrenme teorisine dayanmaktadır. DVM'nin matematiksel algoritmaları, başlangıçta iki sınıflı doğrusal verilerin sınıflandırma problemi için tasarlanmış ve daha sonra çok sınıflı ve doğrusal olmayan verilerin sınıflandırılması için genelleştirilmiştir [25,26]. DVM tarafından üretilen doğrusal ve doğrusal olmayan hiperdüzlemler Şekil 4'te gösterilmektedir.…”

Section: Destek Vektör Makinesiunclassified

Transfer Öğrenme ve Komşuluk Bileşen Analizine Dayalı Balgam Yayma Mikroskop Görüntüleri Üzerinden Otomatik Tüberküloz Teşhisi

Nasip

Zengin

2022

Mühendislik Bilimleri Ve Araştırmaları Dergisi

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Tüberküloz, Mycobacterium tuberculosis bakterisinin neden olduğu, dünya çapında etkili olan ve ölümle sonuçlanabilen bulaşıcı bir hastalıktır. Kişiden alınan balgam örneğinin mikroskop altında incelenmesi hastalığın en yaygın tanı yöntemlerindendir. Ancak bu manuel işlem hataya açık ve zaman alıcıdır. Bu çalışmada, balgam yayma mikroskop görüntüleri kullanılarak otomatik tüberküloz teşhisi için bir model önerilmiştir. Modelde veri setine ait öznitelikler transfer öğrenme yaklaşımı ile Vgg19, ResNet50 ve DenseNet201 kullanılarak elde edilmiştir. Öznitelikler birleştirildikten sonra Komşuluk Bileşen Analizi (KBA) ile ağırlıklandırılıp en iyi olanları seçilmiştir. Seçilen öznitelikler Destek Vektör Makinesi (DVM) ile eğitilmiş, modelin performansı beş kat çapraz doğrulama kullanılarak hesaplanmıştır. Deneysel sonuçlarda %99.03 doğruluk, %99.24 kesinlik, %98.81 duyarlılık, %99.25 özgüllük ve %99.02 f1-skoru elde edilmiştir.

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Section: Destek Vektör Makinesiunclassified

Transfer Öğrenme ve Komşuluk Bileşen Analizine Dayalı Balgam Yayma Mikroskop Görüntüleri Üzerinden Otomatik Tüberküloz Teşhisi

Nasip

Zengin

2022

Mühendislik Bilimleri Ve Araştırmaları Dergisi

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“…This classification algorithm is based on statistical learning theory. The mathematical algorithms of SVM were originally developed for the classification problem of two-class data and then generalized for the classification of multi-class data [25]. The functional interval refers to the loss function.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

A Hybrid Model: Multiple Feature Selection Approach Using Transfer Learning for Bacteria Classification

Nasip¹,

Zengin²

2022

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Analysis of microscope images is an important topic in medical image processing. However, classification of bacteria, which come in different shapes and sizes and are very small structures, based on their morphological structures is a difficult and time-consuming process that cannot be performed by the naked eye. In this study, a hybrid model for bacterial classification is proposed using transfer learning and feature selection methods together. DenseNet201 is used as a feature extractor with the transfer learning approach in the model. The extracted features were selected separately using four different feature selection algorithms and the best features were merged. The best features were trained and classified using Support Vector Machine (SVM). The dataset used was the Digital Image of Bacterial Species (DIBaS) dataset, which contains 33 bacterial species. The dataset was used with 5-fold and 10-fold cross validation, and the average of the two models was used as the evaluation criterion. In the experimental results, 99.78% accuracy, 99.91% precision, 99.88% sensitivity and 99.89% f-1 score were achieved. Thanks to feature selection, the best features that directly affect the classification performance in the dataset are selected. The proposed model can be helpful in making a preliminary diagnosis or a diagnosis in the clinic. Thanks to its fast and accurate classification performance, it can be used for real-time decision making systems.

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“…Based on the PSD features, a kernel partial least squares classifier was able to achieve 98.80% accuracy in fatigue state identification [ 7 ]. Wang et al used common spatial pattern (CSP) to extract features from 14 EEG channels and reported a 90% accuracy with support vector machine (SVM) [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Mental Fatigue Degree Recognition Based on Relative Band Power and Fuzzy Entropy of EEG

Tang

et al. 2023

IJERPH

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Mental fatigue is a common phenomenon in our daily lives. Long-term fatigue can lead to a decline in a person’s operational functions and seriously affect work efficiency. In this paper, a method that recognizes the degree of mental fatigue based on relative band power and fuzzy entropy of Electroencephalogram (EEG) is proposed. The N-back experiment was used to induce mental fatigue in subjects, and the corresponding EEG signals were recorded during the experiment. A preprocessing method based on complementary ensemble empirical modal decomposition (CEEMD) and independent component analysis (ICA) was designed to remove noise from the raw EEG signal. The relative band power feature, which has been used extensively in fatigue recognition studies, was extracted from the EEG signals. Meanwhile, fuzzy entropy, a feature commonly used in attention recognition, was also extracted for fatigue recognition, based on previous findings that an increase in fatigue is accompanied by a decrease in attention. The two features were fed into an extreme gradient boosting (XGBoost) classifier to distinguish three different degrees of fatigue, which resulted in an average accuracy of 92.39% based on data from eight subjects. The promising results indicate the effectiveness of the proposed method in mental fatigue degree identification.

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Using EEG to detect driving fatigue based on common spatial pattern and support vector machine

Cited by 18 publications

References 32 publications

Transfer Öğrenme ve Komşuluk Bileşen Analizine Dayalı Balgam Yayma Mikroskop Görüntüleri Üzerinden Otomatik Tüberküloz Teşhisi

Transfer Öğrenme ve Komşuluk Bileşen Analizine Dayalı Balgam Yayma Mikroskop Görüntüleri Üzerinden Otomatik Tüberküloz Teşhisi

A Hybrid Model: Multiple Feature Selection Approach Using Transfer Learning for Bacteria Classification

Mental Fatigue Degree Recognition Based on Relative Band Power and Fuzzy Entropy of EEG

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