…
comment: The repository administrator has not yet configured an RDF license.
primary topic: Attention-based multi-feature fusion neuromarker for EEG-driven stress classification in learners

Attention-based multi-feature fusion neuromarker for EEG-driven stress classification in learners
type: AcademicArticle
type: Article
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 …
date: 2026-03
volume: 26
issue: 1
is part of: International Journal of Clinical and Health Psychology
status: peerReviewed
status: published
author list: authors
type: EPrint
type: ArticleEPrint
is part of: repository
has document: Attention-based multi-feature fusion neuromarker for EEG-driven stress classification in learners (Texto)
subject: Psychology

Attention-based multi-feature fusion neuromarker for EEG-driven stress classification in learners (Texto)
type: Document
has file: 1-s2.0-S1697260026000153-main.pdf
has part: 1-s2.0-S1697260026000153-main.pdf

International Journal of Clinical and Health Psychology
type: Collection
issn: 16972600

Jin-Ghoo Choi
type: Person
given name: Jin-Ghoo
family name: Choi

Jamil Ahmad
type: Person
given name: Jamil
family name: Ahmad

Soyiba Javed
type: Person
given name: Soyiba
family name: Javed

Josep Alemany Iturriaga
type: Person
given name: Josep
family name: Alemany Iturriaga

Samuel Allende Monje
type: Person
given name: Samuel
family name: Allende Monje

Imran Ashraf
type: Person
given name: Imran
family name: Ashraf

Saliha Ejaz
type: Person
given name: Saliha
family name: Ejaz

Imran Shafi
type: Person
given name: Imran
family name: Shafi

HTML Summary of #28495 Attention-based multi-feature fusion neuromarker for EEG-driven stress classification in learners
format: text/html
primary topic: Attention-based multi-feature fusion neuromarker for EEG-driven stress classification in learners

Psychology
type: Concept
preferred label: Psicología @es