Frequency-Domain Hybrid Model for EEG-Based Emotion Recognition

Jinyu Liu, Naidan Feng () and Yongquan Liang
Additional contact information
Jinyu Liu: College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Naidan Feng: College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Yongquan Liang: College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China

Mathematics, 2025, vol. 13, issue 7, 1-18

Abstract: Emotion recognition based on Electroencephalogram (EEG) signals plays a vital role in affective computing and human–computer interaction (HCI). However, noise, artifacts, and signal distortions present challenges that limit classification accuracy and robustness. To address these issues, we propose ECA-ResDNN, a novel hybrid model designed to leverage the frequency, spatial, and temporal characteristics of EEG signals for improved emotion recognition. Unlike conventional models, ECA-ResDNN integrates an Efficient Channel Attention (ECA) mechanism within a residual neural network to enhance feature selection in the frequency domain while preserving essential spatial information. A Deep Neural Network further extracts temporal dependencies, improving classification precision. Additionally, a hybrid loss function that combines cross-entropy loss and fuzzy set loss enhances the model’s robustness to noise and uncertainty. Experimental results demonstrate that ECA-ResDNN significantly outperforms existing approaches in both accuracy and robustness, underscoring its potential for applications in affective computing, mental health monitoring, and intelligent human–computer interaction.

Keywords: EEG; emotion recognition; fuzzy set theory; attention mechanism; residual neural network (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/13/7/1072/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/7/1072/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:13:y:2025:i:7:p:1072-:d:1620345

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().