On the public citation graph
STR-BNet: Joint Spatiotemporal Representation Learning and Adaptive Brain Network Modeling for EEG Recognition
0 signed · 31 inferred · view as graph
Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.
Is this your work? Sign in with ORCID and put your name on the
claims that are yours.
Reference health. See whether every reference in this work resolves to a
known work and none has been retracted — and get a dated CiteStamped
badge you can embed.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/s00521-024-09479-3openalex-snapshot
Emotion recognition based on phase-locking value brain functional network and topological data analysis
inferredcites →https://doi.org/10.1007/s11571-024-10162-5openalex-snapshot
Set-pMAE: spatial-spEctral-temporal based parallel masked autoEncoder for EEG emotion recognition
inferredcites →https://doi.org/10.1016/j.eswa.2025.129855openalex-snapshot
EEG-based emotion identification from nerve conduction mechanisms: A gustatory-emotion coupling model combined with multiblock attention module
inferredcites →https://doi.org/10.1016/j.inffus.2023.102019openalex-snapshot
Emotion recognition and artificial intelligence: A systematic review (2014–2023) and research recommendations
inferredcites →https://doi.org/10.1016/j.neuroimage.2023.120006openalex-snapshot
Effect of channel density, inverse solutions and connectivity measures on EEG resting-state networks reconstruction: A simulation study
inferredcites →https://doi.org/10.1038/s41598-022-22079-2openalex-snapshot
The thresholding problem and variability in the EEG graph network parameters
inferredcites →https://doi.org/10.1088/1741-2552/abc902openalex-snapshot
A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers
inferredcites →https://doi.org/10.1109/jbhi.2024.3395622openalex-snapshot
Attention-Based Temporal Graph Representation Learning for EEG-Based Emotion Recognition
inferredcites →https://doi.org/10.1109/jbhi.2024.3443651openalex-snapshot
GMAEEG: A Self-Supervised Graph Masked Autoencoder for EEG Representation Learning
inferredcites →https://doi.org/10.1109/jbhi.2025.3606944openalex-snapshot
MtRBD: Advancing iRBD Analysis With Multi-Task Learning for Joint Sleep Staging and RSWA Detection
inferredcites →https://doi.org/10.1109/jproc.2018.2825200openalex-snapshot
Application of Graph Theory to Assess Static and Dynamic Brain Connectivity: Approaches for Building Brain Graphs
inferredcites →https://doi.org/10.1109/jsen.2025.3575103openalex-snapshot
BiGRU-TFA: An Attention-Enhanced Model for EEG Signal Reconstruction Using Temporal and Frequency Features
inferredcites →https://doi.org/10.1109/mis.2024.3352972openalex-snapshot
From Electroencephalogram Data to Brain Networks: Graph-Learning-Based Brain Disease Diagnosis
inferredcites →https://doi.org/10.1109/taffc.2018.2817622openalex-snapshot
EEG Emotion Recognition Using Dynamical Graph Convolutional Neural Networks
inferredcites →https://doi.org/10.1109/taffc.2020.2994159openalex-snapshot
EEG-Based Emotion Recognition Using Regularized Graph Neural Networks
inferredcites →https://doi.org/10.1109/taffc.2021.3064940openalex-snapshot
Variational Instance-Adaptive Graph for EEG Emotion Recognition
inferredcites →https://doi.org/10.1109/tamd.2015.2431497openalex-snapshot
Investigating Critical Frequency Bands and Channels for EEG-Based Emotion Recognition with Deep Neural Networks
inferredcites →https://doi.org/10.1109/tcds.2022.3147839openalex-snapshot
Residual GCB-Net: Residual Graph Convolutional Broad Network on Emotion Recognition
inferredcites →https://doi.org/10.1109/tfuzz.2024.3363623openalex-snapshot
A Temporal Multi-View Fuzzy Classifier for Fusion Identification on Epileptic Brain Network
inferredcites →https://doi.org/10.1109/tnnls.2023.3236635openalex-snapshot
LGGNet: Learning From Local-Global-Graph Representations for Brain–Computer Interface
inferredcites →https://doi.org/10.1109/tnnls.2024.3405663openalex-snapshot
An Efficient Graph Learning System for Emotion Recognition Inspired by the Cognitive Prior Graph of EEG Brain Network
inferredcites →https://doi.org/10.1109/tnnls.2024.3438835openalex-snapshot
SAGN: Sparse Adaptive Gated Graph Neural Network With Graph Regularization for Identifying Dual-View Brain Networks
inferredcites →https://doi.org/10.1109/tnsre.2022.3230250openalex-snapshot
EEG Conformer: Convolutional Transformer for EEG Decoding and Visualization
inferredcites →https://doi.org/10.1109/tpami.2016.2572683openalex-snapshot
Fully Convolutional Networks for Semantic Segmentation
inferredcites →https://doi.org/10.1109/tpami.2024.3443253openalex-snapshot
Graph Multi-Convolution and Attention Pooling for Graph Classification
inferredcites →https://doi.org/10.1111/ejn.15361openalex-snapshot
Methodological considerations for studying neural oscillations
inferredcites →https://doi.org/10.1145/3637528.3671600openalex-snapshot
EEG2Rep: Enhancing Self-supervised EEG Representation Through Informative Masked Inputs
inferredcites →https://doi.org/10.1145/3736574openalex-snapshot
Self-supervised Learning for Electroencephalogram: A Systematic Survey
inferredcites →https://doi.org/10.3389/fnhum.2021.653659openalex-snapshot
BENDR: Using Transformers and a Contrastive Self-Supervised Learning Task to Learn From Massive Amounts of EEG Data
inferredcites →https://doi.org/10.52202/075280-3420openalex-snapshot
BIOT: Biosignal Transformer for Cross-data Learning in the Wild
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID: icsacinstitute.org/submit.