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  "author": "Yusuf Yigit Pilavci",
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  "author": "Yusuf Yigit Pilavci",
            
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  "doi": "10.57702/obe96336",
            
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  "doi_date_published": "2025-01-03",
            
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  "extra_authors": [
            
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    {
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    {
            
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      "extra_author": "Nicolas Farrugia",
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      "extra_author": "Nicolas Farrugia",
            
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      "orcid": ""
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      "name": "graph-signal-processing",
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      "name": "graph-signal-processing",
            
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      "title": "Graph Signal Processing"
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      "title": "Graph Signal Processing"
            
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    },
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    {
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    {
            
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      "description": "",
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      "description": "",
            
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      "display_name": "Neuroimaging",
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      "display_name": "Neuroimaging",
            
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      "name": "neuroimaging",
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      "name": "neuroimaging",
            
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      "title": "Neuroimaging"
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      "title": "Neuroimaging"
            
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  "name": 
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  "name": 
            
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-transform-as-feature-extractor-for-machine-learning-in-neuroimaging",
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-transform-as-feature-extractor-for-machine-learning-in-neuroimaging",
            
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  "notes": "Graph Signal Processing has become a very useful framework 
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  "notes": "Graph Signal Processing has become a very useful framework 
            
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for signal operations and representations defined on irregular 
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for signal operations and representations defined on irregular 
            
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domains. Exploiting transformations that are defined on graph models 
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domains. Exploiting transformations that are defined on graph models 
            
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can be highly beneficial when the graph encodes relationships between 
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can be highly beneficial when the graph encodes relationships between 
            
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signals.",
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signals.",
            
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      "name": "Neuroimaging",
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  ],
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  "title": "Spectral Graph Wavelet Transform as Feature Extractor for 
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  "title": "Spectral Graph Wavelet Transform as Feature Extractor for 
            
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Machine Learning in Neuroimaging",
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Machine Learning in Neuroimaging",
            
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}
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}