Matches in SemOpenAlex for { <https://semopenalex.org/work/W4361792172> ?p ?o ?g. }
Showing items 1 to 90 of
90
with 100 items per page.
- W4361792172 abstract "Electroencephalographic (EEG) technology's non-invasive, inexpensive, and potable qualities have recently increased interest in EEG-based driving fatigue detection. EEG signals have been one of the most accurate and reliable markers of driver fatigue. Despite this, extracting valuable features from cluttered EEG signals still difficult to detect driving fatigue. This study aims to create a novel real-time methodology for detecting driving fatigue based on EEG signals. The study utilizes the Discrete Wavelet Transform (DWT) to obtain different EEG bands and compute power spectrum density (PSD) and other statistical features over each DWT band for the online detection of mental fatigue. Deep learning, particularly convolutional neural networks (CNN), has demonstrated impressive results in recent years as a method to extract features from EEG signals among various analysis techniques successfully. Although automatic feature extraction and accurate classification are advantages of deep learning, designing the network structure can be challenging and requires a vast amount of prior knowledge. Therefore, we used these features as input to CNN instead of using raw EEG data directly. Classification results of multiple machine learning models such as Support Vector Machine (SVM), k-nearest neighbor (kNN), Linear discriminant analysis (LDA), Decision Tree (DT), and Naive Bayes (NB) classifiers are also explored to obtain an optimum solution of the driver's fatigue evaluation. Two driving fatigue EEG datasets were used as testbeds to denote the effectiveness of five conventional classifiers and CNN. The proposed method reached more than 99% classification accuracy using a kNN and CNN in both datasets. The outcomes confirmed the efficacy of the suggested approach." @default.
- W4361792172 created "2023-04-05" @default.
- W4361792172 creator A5007108785 @default.
- W4361792172 creator A5018726069 @default.
- W4361792172 creator A5032591667 @default.
- W4361792172 creator A5064105727 @default.
- W4361792172 creator A5087264309 @default.
- W4361792172 date "2022-12-07" @default.
- W4361792172 modified "2023-09-26" @default.
- W4361792172 title "A Multi-Model Analysis for Driving Fatigue Detection using EEG Signals" @default.
- W4361792172 cites W2086983697 @default.
- W4361792172 cites W2156552533 @default.
- W4361792172 cites W2316106704 @default.
- W4361792172 cites W2771546113 @default.
- W4361792172 cites W2783590821 @default.
- W4361792172 cites W2789220724 @default.
- W4361792172 cites W2908578648 @default.
- W4361792172 cites W2915436731 @default.
- W4361792172 cites W2918092040 @default.
- W4361792172 cites W2921035096 @default.
- W4361792172 cites W3037459016 @default.
- W4361792172 cites W3114735065 @default.
- W4361792172 cites W3133223187 @default.
- W4361792172 cites W3151715958 @default.
- W4361792172 cites W4200330058 @default.
- W4361792172 doi "https://doi.org/10.1109/iecbes54088.2022.10079534" @default.
- W4361792172 hasPublicationYear "2022" @default.
- W4361792172 type Work @default.
- W4361792172 citedByCount "0" @default.
- W4361792172 crossrefType "proceedings-article" @default.
- W4361792172 hasAuthorship W4361792172A5007108785 @default.
- W4361792172 hasAuthorship W4361792172A5018726069 @default.
- W4361792172 hasAuthorship W4361792172A5032591667 @default.
- W4361792172 hasAuthorship W4361792172A5064105727 @default.
- W4361792172 hasAuthorship W4361792172A5087264309 @default.
- W4361792172 hasConcept C108583219 @default.
- W4361792172 hasConcept C113238511 @default.
- W4361792172 hasConcept C118552586 @default.
- W4361792172 hasConcept C119857082 @default.
- W4361792172 hasConcept C12267149 @default.
- W4361792172 hasConcept C153180895 @default.
- W4361792172 hasConcept C154945302 @default.
- W4361792172 hasConcept C15744967 @default.
- W4361792172 hasConcept C196216189 @default.
- W4361792172 hasConcept C28490314 @default.
- W4361792172 hasConcept C41008148 @default.
- W4361792172 hasConcept C46286280 @default.
- W4361792172 hasConcept C47432892 @default.
- W4361792172 hasConcept C52001869 @default.
- W4361792172 hasConcept C522805319 @default.
- W4361792172 hasConcept C52622490 @default.
- W4361792172 hasConcept C69738355 @default.
- W4361792172 hasConcept C81363708 @default.
- W4361792172 hasConcept C84525736 @default.
- W4361792172 hasConceptScore W4361792172C108583219 @default.
- W4361792172 hasConceptScore W4361792172C113238511 @default.
- W4361792172 hasConceptScore W4361792172C118552586 @default.
- W4361792172 hasConceptScore W4361792172C119857082 @default.
- W4361792172 hasConceptScore W4361792172C12267149 @default.
- W4361792172 hasConceptScore W4361792172C153180895 @default.
- W4361792172 hasConceptScore W4361792172C154945302 @default.
- W4361792172 hasConceptScore W4361792172C15744967 @default.
- W4361792172 hasConceptScore W4361792172C196216189 @default.
- W4361792172 hasConceptScore W4361792172C28490314 @default.
- W4361792172 hasConceptScore W4361792172C41008148 @default.
- W4361792172 hasConceptScore W4361792172C46286280 @default.
- W4361792172 hasConceptScore W4361792172C47432892 @default.
- W4361792172 hasConceptScore W4361792172C52001869 @default.
- W4361792172 hasConceptScore W4361792172C522805319 @default.
- W4361792172 hasConceptScore W4361792172C52622490 @default.
- W4361792172 hasConceptScore W4361792172C69738355 @default.
- W4361792172 hasConceptScore W4361792172C81363708 @default.
- W4361792172 hasConceptScore W4361792172C84525736 @default.
- W4361792172 hasFunder F4320321147 @default.
- W4361792172 hasLocation W43617921721 @default.
- W4361792172 hasOpenAccess W4361792172 @default.
- W4361792172 hasPrimaryLocation W43617921721 @default.
- W4361792172 hasRelatedWork W2146076056 @default.
- W4361792172 hasRelatedWork W2732542196 @default.
- W4361792172 hasRelatedWork W2962261620 @default.
- W4361792172 hasRelatedWork W3127425528 @default.
- W4361792172 hasRelatedWork W3203501097 @default.
- W4361792172 hasRelatedWork W4205717897 @default.
- W4361792172 hasRelatedWork W4205958290 @default.
- W4361792172 hasRelatedWork W4286960226 @default.
- W4361792172 hasRelatedWork W4313203779 @default.
- W4361792172 hasRelatedWork W4320802194 @default.
- W4361792172 isParatext "false" @default.
- W4361792172 isRetracted "false" @default.
- W4361792172 workType "article" @default.