relation: http://repositorio.unincol.edu.co/id/eprint/28495/ canonical: http://repositorio.unincol.edu.co/id/eprint/28495/ title: Attention-based multi-feature fusion neuromarker for EEG-driven stress classification in learners creator: Ejaz, Saliha creator: Javed, Soyiba creator: Shafi, Imran creator: Ahmad, Jamil creator: Allende Monje, Samuel creator: Alemany Iturriaga, Josep creator: Choi, Jin-Ghoo creator: Ashraf, Imran subject: Psicología description: 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. date: 2026-03 type: Artículo type: PeerReviewed format: text language: en identifier: http://repositorio.unincol.edu.co/id/eprint/28495/1/1-s2.0-S1697260026000153-main.pdf identifier: Artículo Materias > Psicología Universidad Europea del Atlántico > Investigación > Producción Científica Fundación Universitaria Internacional de Colombia > Investigación > Artículos y libros Universidad Internacional Iberoamericana México > Investigación > Producción Científica Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica Universidad Internacional do Cuanza > Investigación > Producción Científica Universidad de La Romana > Investigación > Producción Científica Abierto Inglés 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. metadata Ejaz, Saliha; Javed, Soyiba; Shafi, Imran; Ahmad, Jamil; Allende Monje, Samuel; Alemany Iturriaga, Josep; Choi, Jin-Ghoo y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, samuel.allende@uneatlantico.es, josep.alemany@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR (2026) Attention-based multi-feature fusion neuromarker for EEG-driven stress classification in learners. International Journal of Clinical and Health Psychology, 26 (1). p. 100678. ISSN 16972600 relation: http://doi.org/10.1016/j.ijchp.2026.100678 relation: doi:10.1016/j.ijchp.2026.100678 language: en