ArticleSensors (Basel, Switzerland)2022
EEG Emotion Classification Network Based on Attention Fusion of Multi-Channel Band Features.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Review of Deep Learning Techniques for EEG-Based Emotion Recognition: Models, Methods, and Datasets.F1000Research · 2025Pooled it
- EEG Emotion Recognition Network Based on Attention and Spatiotemporal Convolution.Sensors (Basel, Switzerland) · 2024Article
- Neuroimaging Study of Brain Functional Differences in Generalized Anxiety Disorder and Depressive Disorder.Brain sciences · 2023Article
- Altered Functional Brain Network Structure between Patients with High and Low Generalized Anxiety Disorder.Diagnostics (Basel, Switzerland) · 2023Article
- Emotion Classification from Multi-Band Electroencephalogram Data Using Dynamic Simplifying Graph Convolutional Network and Channel Style Recalibration Module.Sensors (Basel, Switzerland) · 2023Article
- Special Issue "Emotion Intelligence Based on Smart Sensing".Sensors (Basel, Switzerland) · 2023Article
- Mini review: Challenges in EEG emotion recognition.Frontiers in psychology · 2023Review
- Pleasantness Recognition Induced by Different Odor Concentrations Using Olfactory Electroencephalogram Signals.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Understanding learners' emotions can help optimize instruction sand further conduct effective learning interventions. Most existing studies on student emotion recognition are based on multiple manifestations of external behavior, which do not fully use physiological signals. In this context, on the one hand, a learning emotion EEG dataset (LE-EEG) is constructed, which captures physiological signals reflecting the emotions of boredom, neutrality, and engagement during learning; on the other hand, an EEG emotion classification network based on attention fusion (ECN-AF) is proposed. To be specific, on the basis of key frequency bands and channels selection, multi-channel band features are first extracted (using a multi-channel backbone network) and then fused (using attention units). In order to verify the performance, the proposed model is tested on an open-access dataset SEED (
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.