Evaluation of Laplacian Spatial Filter Implementation in Detecting Driver Vigilance Using Linear Classifier
Maintaining a high level of safety awareness among drivers is essential to ensure the safe operation of automated vehicles (AVs). Many factors can influence the model's performance in achieving accuracy, such as the application of spatial filters, the type of feature selection, and the clas...
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| Format: | Article |
| Language: | English |
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Universitas Indonesia
2024-12-01
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| Series: | International Journal of Technology |
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| Online Access: | https://ijtech.eng.ui.ac.id/article/view/7166 |
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| _version_ | 1846100651056037888 |
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| author | Dedik Romahadi Aberham Genetu Feleke Rikko Putra Youlia |
| author_facet | Dedik Romahadi Aberham Genetu Feleke Rikko Putra Youlia |
| author_sort | Dedik Romahadi |
| collection | DOAJ |
| description | Maintaining a high level of safety awareness among
drivers is essential to ensure the safe operation of automated vehicles (AVs).
Many factors can influence the model's performance in achieving accuracy, such
as the application of spatial filters, the type of feature selection, and the
classifier. Complex modeling, accuracy achievement, and learning time are also
practically difficult. No study has discussed the application of the Laplacian
Spatial Filter (LSF) to driver vigilance classification performance. Therefore,
this study aimed to analyze driving vigilance detection using LSF and linear
classification models. The study involved signal preprocessing and feature extraction
in signal energy, followed by the application of Kruskal - Wallis (KW) and
Minimum Redundancy - Maximum Relevance (MRMR) for feature selection. Finally,
various classification models such as Linear Discriminant Analysis (LDA),
Logistic Regression (LR), Naive Bayes (NB), and Support Vector Machine (SVM)
were used for exploration. The results were significant, with SVM without LSF
achieving the highest average accuracy of 84.26% in the intra-subject and
70.15% across the subject. Based on this study, LSF was not recommended for
EEG-based driver vigilance detection. |
| format | Article |
| id | doaj-art-edef90994fb94ffe8e262f1f19a46ed7 |
| institution | Kabale University |
| issn | 2086-9614 2087-2100 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Universitas Indonesia |
| record_format | Article |
| series | International Journal of Technology |
| spelling | doaj-art-edef90994fb94ffe8e262f1f19a46ed72024-12-30T01:56:46ZengUniversitas IndonesiaInternational Journal of Technology2086-96142087-21002024-12-011561712172910.14716/ijtech.v15i6.71667166Evaluation of Laplacian Spatial Filter Implementation in Detecting Driver Vigilance Using Linear ClassifierDedik Romahadi0Aberham Genetu Feleke1Rikko Putra Youlia21. Department of Mechanical Engineering, Faculty of Engineering, Mercu Buana University, East Meruya Street No. 1, Kembangan, West Jakarta (Postal Code 11650), Indonesia. 2. School of Mechanical Engi2. School of Mechanical Engineering, Beijing Institute of Technology, Zhongguancun South Street No. 5, Haidian District, Beijing (Postal Code 100081), China1. Department of Mechanical Engineering, Faculty of Engineering, Mercu Buana University, East Meruya Street No. 1, Kembangan, West Jakarta (Postal Code 11650), IndonesiaMaintaining a high level of safety awareness among drivers is essential to ensure the safe operation of automated vehicles (AVs). Many factors can influence the model's performance in achieving accuracy, such as the application of spatial filters, the type of feature selection, and the classifier. Complex modeling, accuracy achievement, and learning time are also practically difficult. No study has discussed the application of the Laplacian Spatial Filter (LSF) to driver vigilance classification performance. Therefore, this study aimed to analyze driving vigilance detection using LSF and linear classification models. The study involved signal preprocessing and feature extraction in signal energy, followed by the application of Kruskal - Wallis (KW) and Minimum Redundancy - Maximum Relevance (MRMR) for feature selection. Finally, various classification models such as Linear Discriminant Analysis (LDA), Logistic Regression (LR), Naive Bayes (NB), and Support Vector Machine (SVM) were used for exploration. The results were significant, with SVM without LSF achieving the highest average accuracy of 84.26% in the intra-subject and 70.15% across the subject. Based on this study, LSF was not recommended for EEG-based driver vigilance detection.https://ijtech.eng.ui.ac.id/article/view/7166electroencephalogramlaplacian spatial filterlinear classifiermachine learningvigilance detection |
| spellingShingle | Dedik Romahadi Aberham Genetu Feleke Rikko Putra Youlia Evaluation of Laplacian Spatial Filter Implementation in Detecting Driver Vigilance Using Linear Classifier International Journal of Technology electroencephalogram laplacian spatial filter linear classifier machine learning vigilance detection |
| title | Evaluation of Laplacian Spatial Filter Implementation in Detecting Driver Vigilance Using Linear Classifier |
| title_full | Evaluation of Laplacian Spatial Filter Implementation in Detecting Driver Vigilance Using Linear Classifier |
| title_fullStr | Evaluation of Laplacian Spatial Filter Implementation in Detecting Driver Vigilance Using Linear Classifier |
| title_full_unstemmed | Evaluation of Laplacian Spatial Filter Implementation in Detecting Driver Vigilance Using Linear Classifier |
| title_short | Evaluation of Laplacian Spatial Filter Implementation in Detecting Driver Vigilance Using Linear Classifier |
| title_sort | evaluation of laplacian spatial filter implementation in detecting driver vigilance using linear classifier |
| topic | electroencephalogram laplacian spatial filter linear classifier machine learning vigilance detection |
| url | https://ijtech.eng.ui.ac.id/article/view/7166 |
| work_keys_str_mv | AT dedikromahadi evaluationoflaplacianspatialfilterimplementationindetectingdrivervigilanceusinglinearclassifier AT aberhamgenetufeleke evaluationoflaplacianspatialfilterimplementationindetectingdrivervigilanceusinglinearclassifier AT rikkoputrayoulia evaluationoflaplacianspatialfilterimplementationindetectingdrivervigilanceusinglinearclassifier |