Method for Emotion Recognition of EEG Signals Based on Recursive Graph and Spatiotemporal Attention Mechanism.
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- # EEG Emotion Recognition Based on TCSA and EfficientNetV2
- Code and data for the paper "A Method for Emotion Recognition of EEG Signals Based on Recursive Graph and Spatiotemporal Attention Mechanism".
- ## Environment
- - Python >= 3.8
- - PyTorch >= 1.10.0
- - numpy >= 1.21.0
- - scipy >= 1.7.0
- - pyts >= 0.12.0
- - matplotlib >= 3.5.0
- ## File Description
- - `EEG_Channel_Rearrange_To_CSV.py`: Rearrange EEG channel data and save as CSV.
- - `rp.py`: Generate recurrence plot (RP) from EEG CSV data.
- - `model.py`: EfficientNetV2 model with TCSA attention mechanism for classification.
- ## Dataset
- This study uses the public DEAPandDREAMER dataset
README.md at commit 0a241a1, no license · at the source
Overview
- School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China
- National Key Laboratory of Information Systems Engineering, Nanjing 210096, China
Abstract
Emotion recognition plays a crucial role in human–computer interaction and mental health applications. Traditional Electroencephalogram (EEG)-based emotion recognition methods are limited in classification accuracy due to their neglect of the spatiotemporal characteristics of the signals and individual differences. This study proposes a novel EEG emotion recognition framework that integrates spatiotemporal features to enhance performance through the following innovations: (1) the use of a Recurrence Plot (RP) to transform one-dimensional EEG signals into two-dimensional images, enhancing the representation of nonlinear dynamic features; (2) the design of a Spatiotemporal Channel Attention Module (TCSA), which combines temporal convolution, channel, and spatial attention mechanisms to optimize the capture of complex patterns; and (3) the integration of the lightweight and efficient network Efficientnet to construct the TCSA-Efficientnet classification model. On the Database for Emotion Analysis using Physiological Signals (DEAP) dataset, the proposed method achieves accuracy rates of 99.11% and 99.33% for valence and arousal classification tasks, respectively. On the Database for Emotion Recognition Using EEG and Physiological Signals (DREAMER) dataset, the method achieves accuracy rates of 98.08% and 97.49%, outperforming other EEG-based emotion classification models on both datasets. This demonstrates its advantages in accuracy, robustness, and generalization.
Reproduced under the paper's license (CC BY), from the paper cited above.
Repository
Its files are read in the Code ↔ Paper reader above.
huang0122/EEG-Emotion-Recognition
0a241a13541c13a26e738f608901fc8a9102bdcf, 2 March 2026Availability: 1 check, the latest on 30 September 2026: the link answers
- 30 September 2026: the link answers
1 file
- README.md, Text, 18 lines
The paper's code and data availability statement is in the Data section.
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Data
No dataset and no data link were found in the paper.
Data Availability Statement
The data presented in this study are available on request from the corresponding author due to privacy restrictions. The code related to this work has been uploaded to a public GitHub repository: https://
Reproduced under the paper's license (CC BY), from the paper cited above.
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Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 7 keywords, 3 funders, 38 references.
Cite
This paper
Huang, D., Xu, L., & Li, Y. (2026). Method for Emotion Recognition of EEG Signals Based on Recursive Graph and Spatiotemporal Attention Mechanism. Brain sciences, 16(4), 377. https://
BibTeX
@article{huang2026method
author = {Huang, Dong and Xu, Lin and Li, Yuwen},
title = {{Method for Emotion Recognition of EEG Signals Based on Recursive Graph and Spatiotemporal Attention Mechanism}},
journal = {Brain sciences},
year = {2026},
month = mar,
volume = {16},
number = {4},
pages = {377},
publisher = {Multidisciplinary Digital Publishing Institute (MDPI)},
issn = {2076-3425},
doi = {10.3390/
url = {https://
pmid = {42041787},
pmcid = {PMC13114897}
}
RIS
TY - JOUR
AU - Huang, Dong
AU - Xu, Lin
AU - Li, Yuwen
TI - Method for Emotion Recognition of EEG Signals Based on Recursive Graph and Spatiotemporal Attention Mechanism
T2 - Brain sciences
J2 - Brain Sci
PY - 2026
DA - 2026/
VL - 16
IS - 4
SP - 377
SN - 2076-3425
PB - Multidisciplinary Digital Publishing Institute (MDPI)
DO - 10.3390/
UR - https://
LA - en
ER -
CSL-JSON
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