@prefix owl: . @prefix rdf: . @prefix dct: . @prefix event: . @prefix bibo: . @prefix void: . @prefix geo: . @prefix xsd: . @prefix rdfs: . @prefix eprel: . @prefix cc: . @prefix ep: . @prefix epid: . @prefix skos: . @prefix dc: . @prefix foaf: . <> foaf:primaryTopic ; rdfs:comment "The repository administrator has not yet configured an RDF license."^^xsd:string . rdfs:label "1-s2.0-S1697260026000153-main.pdf"^^xsd:string . dc:format "text/html"; dc:title "HTML Summary of #28495 \n\nAttention-based multi-feature fusion neuromarker for EEG-driven stress classification in learners\n\n"; foaf:primaryTopic . dct:hasPart ; ep:hasFile ; rdf:type bibo:Document, ep:Document; rdfs:label "Attention-based multi-feature fusion neuromarker for EEG-driven stress classification in learners (Texto)"^^xsd:string . rdf:_1 ; rdf:_2 ; rdf:_3 ; rdf:_4 ; rdf:_5 ; rdf:_6 ; rdf:_7 ; rdf:_8 . bibo:abstract "With the growing academic pressure and competitive educational environment, students often face mental stress, which can affect their academic performance and mental health. Its accurate and timely detection and prevention is important. Traditionally, mental stress has been reported by self-assessment, which is highly subjective and can be erroneous. With advances in neuroscience, electroencephalogram (EEG) signals have been used to study brain states more objectively. EEG-based features, including time-domain, frequency-domain, and various types of connectivity features, have been used to effectively classify stress signals. However, these individual features are only able to present one aspect of the brain under stress. Several studies have combined a distinct set of features extracted from EEG signals, including time and frequency domain features, with other peripheral signals. Stress is a complex mechanism which leads to alternation in brain dynamics, its connectivity patterns and information flow. This study proposed a feature-fusion model that can effectively combine spatial features, i.e. Microstates (MS), connectivity features like Transfer Entropy (TE) and Granger Causality (GC), which provided a new neuromarker for stress classification. These features are combined with attention fusion, which enhances the discriminant features and mitigates the individual limitations within each modality. We also extracted microstates for stress-based signals. It provided a new set of microstate topomaps to study brain networks when under stress, which was not explored previously. The proposed Attention-fusion based multi-feature set is classified using Support Vector Machine, Linear Discriminant Analysis (LDA) and Multilayer Perceptron (MLP) and gave a reliable accuracy of 95.47%, 98.91%, and 83.49%, respectively. To validate the proposed method, the classification results were compared with individual and binary fusion of MS, TE and GC features, which further confirmed the robustness of the framework. This proposed feature fusion provides a more robust stress classification neuromarker, which can effectively cover the brain dynamics for accurate reporting of the underlying mental state."^^xsd:string; bibo:authorList ; bibo:issue "1"; bibo:status , ; bibo:volume "26"; dct:creator , , , , , , , ; dct:date "2026-03"; dct:isPartOf , ; dct:subject ; dct:title "Attention-based multi-feature fusion neuromarker for EEG-driven stress classification in learners"^^xsd:string; ep:hasDocument ; owl:sameAs ; rdf:type bibo:AcademicArticle, bibo:Article, ep:ArticleEPrint, ep:EPrint; rdfs:seeAlso . rdf:type skos:Concept; skos:prefLabel "Psicología"@es, "Psychology"@en . foaf:familyName "Shafi"^^xsd:string; foaf:givenName "Imran"^^xsd:string; foaf:name "Imran Shafi"^^xsd:string; rdf:type foaf:Person . foaf:familyName "Ejaz"^^xsd:string; foaf:givenName "Saliha"^^xsd:string; foaf:name "Saliha Ejaz"^^xsd:string; rdf:type foaf:Person . foaf:familyName "Ashraf"^^xsd:string; foaf:givenName "Imran"^^xsd:string; foaf:name "Imran Ashraf"^^xsd:string; rdf:type foaf:Person . foaf:familyName "Ahmad"^^xsd:string; foaf:givenName "Jamil"^^xsd:string; foaf:name "Jamil Ahmad"^^xsd:string; rdf:type foaf:Person . foaf:familyName "Choi"^^xsd:string; foaf:givenName "Jin-Ghoo"^^xsd:string; foaf:name "Jin-Ghoo Choi"^^xsd:string; rdf:type foaf:Person . foaf:familyName "Javed"^^xsd:string; foaf:givenName "Soyiba"^^xsd:string; foaf:name "Soyiba Javed"^^xsd:string; rdf:type foaf:Person . foaf:familyName "Alemany Iturriaga"^^xsd:string; foaf:givenName "Josep"^^xsd:string; foaf:name "Josep Alemany Iturriaga"^^xsd:string; rdf:type foaf:Person . foaf:familyName "Allende Monje"^^xsd:string; foaf:givenName "Samuel"^^xsd:string; foaf:name "Samuel Allende Monje"^^xsd:string; rdf:type foaf:Person . bibo:issn "16972600"; foaf:name "International Journal of Clinical and Health Psychology"^^xsd:string; owl:sameAs ; rdf:type bibo:Collection .