mlsanityData to 14 Oct 2026

MAtt: A Manifold Attention Network for EEG Decoding

arXiv 2210.01986 ↗First posted 5 Oct 2022cs.LG eess.SP q-bio.NCcited by 44

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Abstract (arXiv)

Recognition of electroencephalographic (EEG) signals highly affect the efficiency of non-invasive brain-computer interfaces (BCIs). While recent advances of deep-learning (DL)-based EEG decoders offer improved performances, the development of geometric learning (GL) has attracted much attention for offering exceptional robustness in decoding noisy EEG data. However, there is a lack of studies on the merged use of deep neural networks (DNNs) and geometric learning for EEG decoding. We herein propose a manifold attention network (mAtt), a novel geometric deep learning (GDL)-based model, featuring a manifold attention mechanism that characterizes spatiotemporal representations of EEG data fully on a Riemannian symmetric positive definite (SPD) manifold. The evaluation of the proposed MAtt on both time-synchronous and -asyncronous EEG datasets suggests its superiority over other leading DL methods for general EEG decoding. Furthermore, analysis of model interpretation reveals the capability of MAtt in capturing informative EEG features and handling the non-stationarity of brain dynamics.

Key facts. Cited by 44 papers in the map (data through 14 Oct 2026). Builds on 18 of its 66 references that are in the map. First posted on arXiv 5 Oct 2022.

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18 of 66 references.

References · 18

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  3. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,442
  4. 2013Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Mapscited by 2,371
  5. 2014Striving for Simplicity: The All Convolutional Netcited by 990
  6. 2016EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfacescited by 628
  7. 2016Geometric deep learning on graphs and manifolds using mixture model CNNscited by 468
  8. 2016A Riemannian Network for SPD Matrix Learningcited by 162
  9. 2019Deep learning-based electroencephalography analysis: a systematic reviewcited by 156
  10. 2021SleepTransformer: Automatic Sleep Staging with Interpretability and Uncertainty Quantificationcited by 84
  11. 2019Riemannian batch normalization for SPD neural networkscited by 72
  12. 2020EEG-TCNet: An Accurate Temporal Convolutional Network for Embedded Motor-Imagery Brain-Machine Interfacescited by 63
6 more
  1. 2021FBCNet: A Multi-view Convolutional Neural Network for Brain-Computer Interfacecited by 57
  2. 2020Federated Transfer Learning for EEG Signal Classificationcited by 37
  3. 2017Riemannian approach to batch normalizationcited by 35
  4. 2022Tensor-CSPNet: A Novel Geometric Deep Learning Framework for Motor Imagery Classificationcited by 21
  5. 2018Compact Convolutional Neural Networks for Classification of Asynchronous Steady-state Visual Evoked Potentialscited by 15
  6. 2020MVC-Net: A Convolutional Neural Network Architecture for Manifold-Valued Images With Applicationscited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 2; 2023: 6; 2024: 9; 2025: 11; 2026: 16.

44 papers.

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  1. 2023Decoding Natural Images from EEG for Object Recognitioncited by 81
  2. 2022Graph Neural Networks on SPD Manifolds for Motor Imagery Classification: A Perspective from the Time-Frequency Analysiscited by 15
  3. 2023Adaptive Log-Euclidean Metrics for SPD Matrix Learningcited by 13
  4. 2024Mind's Eye: Image Recognition by EEG via Multimodal Similarity-Keeping Contrastive Learningcited by 12
  5. 2023R-Mixup: Riemannian Mixup for Biological Networkscited by 9
  6. 2025Learning to Normalize on the SPD Manifold under Bures-Wasserstein Geometrycited by 7
  7. 2025Is Limited Participant Diversity Impeding EEG-based Machine Learning?cited by 6
  8. 2025SPD Matrix Learning for Neuroimaging Analysis: Perspectives, Methods, and Challengescited by 6
  9. 2026Neural Networks on Symmetric Spaces of Noncompact Typecited by 6
  10. 2024Geometric Neural Network based on Phase Space for BCI-EEG decodingcited by 5
  11. 2023Structure-Preserving Transformers for Sequences of SPD Matricescited by 4
  12. 2024Robust Hyperbolic Learning with Curvature-Aware Optimizationcited by 4
32 more
  1. 2024Fast and Stable Riemannian Metrics on SPD Manifolds via Cholesky Product Geometrycited by 4
  2. 2024Are EEG Sequences Time Series? EEG Classification with Time Series Models and Joint Subject Trainingcited by 3
  3. 2024Riemannian Geometry-Based EEG Approaches: A Literature Reviewcited by 3
  4. 2025Geometric Machine Learning on EEG Signalscited by 3
  5. 2025Spatial-Functional awareness Transformer-based graph archetype contrastive learning for Decoding Visual Neural Representations from EEGcited by 3
  6. 2024EEG-MACS: Manifold Attention and Confidence Stratification for EEG-based Cross-Center Brain Disease Diagnosis under Unreliable Annotationscited by 2
  7. 2025EEG-ReMinD: Enhancing Neurodegenerative EEG Decoding through Self-Supervised State Reconstruction-Primed Riemannian Dynamicscited by 2
  8. 2025DFNN: A Deep Fr'echet Neural Network Framework for Learning Metric-Space-Valued Responsescited by 2
  9. 2026HEEGNet: Hyperbolic Embeddings for EEGcited by 2
  10. 2026SPD Learn: A Geometric Deep Learning Python Library for Neural Decoding Through Trivializationcited by 2
  11. 2022CLEEGN: A Convolutional Neural Network for Plug-and-Play Automatic EEG Reconstructioncited by 1
  12. 2024Quantifying Spatial Domain Explanations in BCI using Earth Mover's Distancecited by 1
  13. 2023LGL-BCI: A Motor-Imagery-Based Brain-Computer Interface with Geometric Learningcited by 0
  14. 2023Brain-inspired Computing Based on Deep Learning for Human-computer Interaction: A Reviewcited by 0
  15. 2024Automatic Classification of Sleep Stages from EEG Signals Using Riemannian Metrics and Transformer Networkscited by 0
  16. 2025SSVEP-BiMA: Bifocal Masking Attention Leveraging Native and Symmetric-Antisymmetric Components for Robust SSVEP Decodingcited by 0
  17. 2025Lightweight Transformer for EEG Classification via Balanced Signed Graph Algorithm Unrollingcited by 0
  18. 2025Learning Topology-Driven Multi-Subspace Fusion for Grassmannian Deep Networkcited by 0
  19. 2025Multivariate Time Series Forecasting with Hybrid Euclidean-SPD Manifold Graph Neural Networkscited by 0
  20. 2026A Unified SPD Token Transformer Framework for EEG Classification: Systematic Comparison of Geometric Embeddingscited by 0
  21. 2026RepSPD: Enhancing SPD Manifold Representation in EEGs via Dynamic Graphscited by 0
  22. 2026LAtte: Hyperbolic Lorentz Attention for Cross-Subject EEG Classificationcited by 0
  23. 2026EEG-SeeGraph: Interpreting functional connectivity disruptions in dementias via sparse-explanatory dynamic EEG-graph learningcited by 0
  24. 2026CliffSearch: Structured Agentic Co-Evolution over Theory and Code for Scientific Algorithm Discoverycited by 0
  25. 2026Batch Normalization for Neural Networks on Complex Domainscited by 0
  26. 2026AIM: Adversarial Information Masking for Faithfulness Evaluation of Saliency Mapscited by 0
  27. 2026Riemannian Networks over Full-Rank Correlation Matricescited by 0
  28. 2026A Sliced-Wasserstein Framework on Correlation Matrices for EEG Decodingcited by 0
  29. 2026Towards Robust EEG Decoding Based on Riemannian Self-Attentioncited by 0
  30. 2026PRISM: Prioritized Channel Importance with Semi-supervised Domain Adaptation for Cross-Subject EEG Emotion Recognitioncited by 0
  31. 2026Itextsuperscript2RiMA: Spectral Riemannian Representation with Temporal Attention for Mental Stress Detection based on EEG Signalscited by 0
  32. 2026Time Window Optimization for Motor Imagery Classification via Temporal Structure Embedded Reinforcement Learningcited by 0