Emotion Recognition Using Multi-View EEG-fNIRS and Cross-Attention Feature Fusion.
Overview
Abstract
To improve the accuracy of emotion recognition, this paper proposes a multi-view EEG-fNIRS and cross-attention fusion module named FGCN-TCNN-CAF, which employs a differentiated modeling strategy for the frequency, spatial, and temporal features of EEG-fNIRS signals. First, frequency-domain and time-domain features are extracted from EEG, and time-domain features are obtained from fNIRS signals. Then, a frequency-domain graph convolutional network (FGCN) and a time-domain convolutional network (TCNN) are deployed in parallel. The EEG feature views from different frequency bands are modeled using an FGCN module to capture graph-structured relationships, while the time-domain views of EEG and fNIRS are processed by a TCNN module to extract spatial and temporal features. Finally, a cross-attention fusion network (CAF) is applied to achieve interactive fusion of multimodal features. Experiments demonstrate that the proposed multi-view EEG approach achieves higher recognition accuracy compared to using only the EEG view. Additionally, the mmultimodalrecognition results outperform single-modal EEG and single-modal fNIRS by 1.73% and 6.65%, respectively. When compared with other emotion recognition models, the proposed method achieves the highest accuracy of 96.09%, proving its superior performance.
Reproduced under the paper's license (CC BY), from the paper cited above.
Code
The paper links to its data, not to its authors' code: see the Data section.
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Data
Datasets cited
- gitee.com/
tycgj/ , at gitee.com; found in the text, “2.1. EEG-fNIRS Dataset”enter
Data Availability Statement
The data presented in this study are available on request from the corresponding author due to privacy and ethical reasons. If you are interested in the dataset and want to use it, please download and fill out the license agreement (https://
Reproduced under the paper's license (CC BY), from the paper cited above.
Versions
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Version 1, 30 September 2026: the first record
Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 5 keywords, 7 MeSH terms, 3 funders, 31 references.
Cite
This paper
Yan, N., Chen, G., & Zhang, X. (2026). Emotion Recognition Using Multi-View EEG-fNIRS and Cross-Attention Feature Fusion. Biosensors, 16(3), 145. https://
BibTeX
@article{yan2026emotion,
author = {Yan, Ni and Chen, Guijun and Zhang, Xueying},
title = {{Emotion Recognition Using Multi-View EEG-fNIRS and Cross-Attention Feature Fusion}},
journal = {Biosensors},
year = {2026},
month = mar,
volume = {16},
number = {3},
pages = {145},
publisher = {Multidisciplinary Digital Publishing Institute (MDPI)},
issn = {2079-6374},
doi = {10.3390/
url = {https://
pmid = {41892037},
pmcid = {PMC13023579}
}
RIS
TY - JOUR
AU - Yan, Ni
AU - Chen, Guijun
AU - Zhang, Xueying
TI - Emotion Recognition Using Multi-View EEG-fNIRS and Cross-Attention Feature Fusion
T2 - Biosensors
J2 - Biosensors (Basel)
PY - 2026
DA - 2026/
VL - 16
IS - 3
SP - 145
SN - 2079-6374
PB - Multidisciplinary Digital Publishing Institute (MDPI)
DO - 10.3390/
UR - https://
LA - en
ER -
CSL-JSON
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"publisher": "Multidisciplinary Digital Publishing Institute (MDPI)",
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"language": "en",
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