ArticleSensors (Basel, Switzerland)2023
FCAN-XGBoost: A Novel Hybrid Model for EEG Emotion Recognition.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
What it found
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Who cites it
6 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
- A Resource-Efficient Multi-Entropy Fusion Method and Its Application for EEG-Based Emotion Recognition.Entropy (Basel, Switzerland) · 2025Article
- Multivariate Modelling and Prediction of High-Frequency Sensor-Based Cerebral Physiologic Signals: Narrative Review of Machine Learning Methodologies.Sensors (Basel, Switzerland) · 2024Review
- CSA-SA-CRTNN: A Dual-Stream Adaptive Convolutional Cyclic Hybrid Network Combining Attention Mechanisms for EEG Emotion Recognition.Brain sciences · 2024Article
- CATM: A Multi-Feature-Based Cross-Scale Attentional Convolutional EEG Emotion Recognition Model.Sensors (Basel, Switzerland) · 2024Article
- Comparative analysis of ROCKET-driven and classic EEG features in predicting attachment styles.BMC psychology · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
In recent years, artificial intelligence (AI) technology has promoted the development of electroencephalogram (EEG) emotion recognition. However, existing methods often overlook the computational cost of EEG emotion recognition, and there is still room for improvement in the accuracy of EEG emotion recognition. In this study, we propose a novel EEG emotion recognition algorithm called FCAN-XGBoost, which is a fusion of two algorithms, FCAN and XGBoost. The FCAN module is a feature attention network (FANet) that we have proposed for the first time, which processes the differential entropy (
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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.