GLSTNet: A Global-Local Spatial Relations and Temporal Dynamics Network for EEG-Based Emotion Recognition.

Overview

1. School of Computer Science, Jiangsu University of Science and Technology, Zhenjiang 212003, China; (R.Z.); (M.Z.)
2. The State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China; (C.M.); (C.L.)
3. School of Information and Artificial Intelligence, Yangzhou University, Yangzhou 225127, China

Abstract

Electroencephalography (EEG)-based emotion recognition is an important biosensing technique for affective brain-computer interfaces (BCIs), mental-state assessment, and physiological monitoring. Existing methods often rely on a single spectral descriptor or regular two-dimensional brain maps, which makes it difficult to jointly model local spatial representations, global spatial relations, and temporal dynamics. This paper proposes GLSTNet, a global-local spatial relations and temporal dynamics network for EEG emotion recognition. EEG trials are divided into short windows, from which multi-band spectral features are extracted and arranged into compact spatial maps. The local spatial encoder (LSE) learns local spatial and spatial–spectral representations from these compact multi-band spatial maps. The global spatial-relation encoder (GSRE) models long-range spatial relations between non-adjacent electrodes using a Pearson correlation prior and a learnable residual adjacency matrix. After local and global representations are integrated through gated fusion, the temporal dynamics encoder (TDE) models consecutive EEG windows using a gated recurrent unit with temporal attention. Comprehensive validation is conducted on two public EEG emotion datasets, the Database for Emotion Analysis using Physiological Signals (DEAP) and the SJTU Emotion EEG Dataset (SEED). In the subject-dependence setting, GLSTNet achieves 93.50 ± 3.22% accuracy for valence and 93.79 ± 3.64% accuracy for arousal on DEAP, and 92.48 ± 3.30% accuracy on SEED. In the subject-independence setting with target-subject calibration, GLSTNet obtains 75.61 ± 6.19% and 79.57 ± 5.99% accuracy for DEAP valence and arousal, respectively, and 88.22 ± 4.70% accuracy on SEED. These results indicate that integrating global-local spatial relations with temporal dynamics provides an effective representation strategy for EEG-based emotion recognition.

Reproduced under the paper's license (CC BY), from the paper cited above.

Code

The paper says that its authors' code is available on request: it was not published with the paper, so there is nothing to verify.

The paper's code and data availability statement is in the Data section.

Tracing map

A tracing map links a paper to the code its authors published: this paper has none (its code is available on request), so it has no map.

Data

No dataset and no data link were found in the paper.

Data Availability Statement

The DEAP dataset is available from its original provider at https://www.eecs.qmul.ac.uk/mmv/datasets/deap/ (accessed on 4 April 2026). The SEED dataset is available from the BCMI Laboratory at Shanghai Jiao Tong University at https://bcmi.sjtu.edu.cn/~seed/ (accessed on 4 April 2026). These datasets are subject to the access policies of their respective providers. The code used in this study will be made available from the corresponding author upon reasonable request.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 5 keywords, 6 MeSH terms, 3 funders, 34 references.

Cite

This paper

Zhang, R., Zhong, M., Ma, C., Xiao, Z., Zhang, Y., & Liu, C. (2026). GLSTNet: A Global-Local Spatial Relations and Temporal Dynamics Network for EEG-Based Emotion Recognition. Biosensors, 16(8), 421. https://doi.org/10.3390/bios16080421

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.3390/bios16080400

EEG and ECG Wearable Biosensor-Based Affective State Analysis Using Deep Learning over 6G IoT Healthcare Networks.

Journal: Biosensors

In common: EEG, 10 references

[2] doi:10.3390/brainsci16070716

RGB-Style Input Representations for EEG: Evaluating Spatial Concatenation Versus Band-Wise Stacking in Deep Emotion Recognition.

Journal: Brain sciences

In common: EEG, cognitive, 7 references

[3] doi:10.3389/fnins.2026.1810609

A dual-branch network with brain region-constrained attention for EEG emotion recognition.

Journal: Frontiers in neuroscience

In common: EEG, cognitive, 4 references

[4] doi:10.3390/s26175327 [code]

Subject Identity Confounds qEEG Emotion Recognition on DEAP and DREAMER.

Journal: Sensors (Basel, Switzerland)

In common: EEG, cognitive, 3 references

[5] doi:10.1038/s41597-026-07456-0

A multimodal dataset for emotional transition analysis in virtual reality.

Journal: Scientific data

In common: EEG, 3 references

[6] doi:10.1038/s41598-026-53295-9

EEG-based dynamic emotion recognition using multi-scale wavelet transform with a Spatio-Temporal neural network.

Journal: Scientific reports

In common: EEG, cognitive, 2 references

[7] doi:10.3390/s26103217 [code]

A Supervised Contrastive Variational Autoencoder with Probabilistic Latent Alignment for Cross-Domain EEG Emotion Recognition.

Journal: Sensors (Basel, Switzerland)

In common: EEG, cognitive, 2 references

[8] doi:10.1002/cns.71096

Dynamic Hemispheric Lateralization of Naturalistic Emotional Processing After Unilateral Stroke: An EEG Study.

Journal: CNS neuroscience & therapeutics

In common: EEG, cognitive, 2 references

[9] doi:

Effect of Emotional States on EEG-Based Biometric Identification: A Comparative Study of Classifiers

Journal: Bioengineering (Basel, Switzerland)

In common: EEG, cognitive, 2 references

[10] doi:10.3390/s26144636

Toward Robust EEG Classification Using Adaptive CNN-Transformer and Inception Architectures and Signal Level Augmentation.

Journal: Sensors (Basel, Switzerland)

In common: EEG, 2 references

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.