# LGGNet: Learning from Local-Global-Graph Representations for Brain-Computer Interface

arXiv: https://arxiv.org/abs/2105.02786 · HTML: https://mlsanity.com/paper/2105.02786/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 75 references that are in the map.
- First posted on arXiv 5 May 2021.

## Builds on (most cited first, 13 of 13)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps](https://mlsanity.com/paper/1312.6034/) (2013)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016)
- [EEG-Based Emotion Recognition Using Regularized Graph Neural Networks](https://mlsanity.com/paper/1907.07835/) (2019)
- [Identifying Stable Patterns over Time for Emotion Recognition from EEG](https://mlsanity.com/paper/1601.02197/) (2016)
- [Multi-channel EEG recordings during a sustained-attention driving task](https://mlsanity.com/paper/1809.06534/) (2018)
- [Utilizing Deep Learning Towards Multi-modal Bio-sensing and Vision-based Affective Computing](https://mlsanity.com/paper/1905.07039/) (2019)
- [TSception: A Deep Learning Framework for Emotion Detection Using EEG](https://mlsanity.com/paper/2004.02965/) (2020)
- [EEG-based video identification using graph signal modeling and graph convolutional neural network](https://mlsanity.com/paper/1809.04229/) (2018)
- [Emotion self-regulation training in major depressive disorder using simultaneous real-time fMRI and EEG neurofeedback](https://mlsanity.com/paper/1909.05764/) (2019)

## Built on by (most cited first, 30 of 36)

- [CBraMod: A Criss-Cross Brain Foundation Model for EEG Decoding](https://mlsanity.com/paper/2412.07236/) (2024)
- [Decoding Natural Images from EEG for Object Recognition](https://mlsanity.com/paper/2308.13234/) (2023)
- [Federated Graph Neural Networks: Overview, Techniques and Challenges](https://mlsanity.com/paper/2202.07256/) (2022)
- [EEG-Deformer: A Dense Convolutional Transformer for Brain-computer Interfaces](https://mlsanity.com/paper/2405.00719/) (2024)
- [Graph Neural Network-based EEG Classification: A Survey](https://mlsanity.com/paper/2310.02152/) (2023)
- [Interpretable and Robust AI in EEG Systems: A Survey](https://mlsanity.com/paper/2304.10755/) (2023)
- [Biologically Plausible Brain Graph Transformer](https://mlsanity.com/paper/2502.08958/) (2025)
- [MASA-TCN: Multi-anchor Space-aware Temporal Convolutional Neural Networks for Continuous and Discrete EEG Emotion Recognition](https://mlsanity.com/paper/2308.16207/) (2023)
- [DeeperBrain: A Neuro-Grounded EEG Foundation Model Towards Universal BCI](https://mlsanity.com/paper/2601.06134/) (2026)
- [LibEER: A Comprehensive Benchmark and Algorithm Library for EEG-based Emotion Recognition](https://mlsanity.com/paper/2410.09767/) (2024)
- [THD-BAR: Topology Hierarchical Derived Brain Autoregressive Modeling for EEG Generic Representations](https://mlsanity.com/paper/2511.13733/) (2025)
- [Graph Neural Networks in EEG-based Emotion Recognition: A Survey](https://mlsanity.com/paper/2402.01138/) (2024)
- [You Only Acquire Sparse-channel (YOAS): A Unified Framework for Dense-channel EEG Generation](https://mlsanity.com/paper/2406.15269/) (2024)
- [BrainGPT: Unleashing the Potential of EEG Generalist Foundation Model by Autoregressive Pre-training](https://mlsanity.com/paper/2410.19779/) (2024)
- [MSGM: A Multi-Scale Spatiotemporal Graph Mamba for EEG Emotion Recognition](https://mlsanity.com/paper/2507.15914/) (2025)
- [Spatial-Functional awareness Transformer-based graph archetype contrastive learning for Decoding Visual Neural Representations from EEG](https://mlsanity.com/paper/2509.24761/) (2025)
- [Conditional Generative Models for Simulation of EMG During Naturalistic Movements](https://mlsanity.com/paper/2211.01856/) (2022)
- [MVGT: A Multi-view Graph Transformer Based on Spatial Relations for EEG Emotion Recognition](https://mlsanity.com/paper/2407.03131/) (2024)
- [MEEG and AT-DGNN: Improving EEG Emotion Recognition with Music Introducing and Graph-based Learning](https://mlsanity.com/paper/2407.05550/) (2024)
- [A Hybrid Graph Neural Network for Enhanced EEG-Based Depression Detection](https://mlsanity.com/paper/2410.18103/) (2024)
- [Decoding Covert Speech from EEG Using a Functional Areas Spatio-Temporal Transformer](https://mlsanity.com/paper/2504.03762/) (2025)
- [BrainPro: Towards Large-scale Brain State-aware EEG Representation Learning](https://mlsanity.com/paper/2509.22050/) (2025)
- [LEL: Lipschitz Continuity Constrained Ensemble Learning for Efficient EEG-Based Intra-subject Emotion Recognition](https://mlsanity.com/paper/2504.09156/) (2025)
- [Calibration-Free EEG-based Driver Drowsiness Detection with Online Test-Time Adaptation](https://mlsanity.com/paper/2511.22030/) (2025)
- [BrainStack: Neuro-MoE with Functionally Guided Expert Routing for EEG-Based Language Decoding](https://mlsanity.com/paper/2601.21148/) (2026)
- [NAKUL-Med: Spectral-Graph State Space Models with Dynamics Kernels for Medical Signals](https://mlsanity.com/paper/2605.00871/) (2026)
- [EEG_RL-Net: Enhancing EEG MI Classification through Reinforcement Learning-Optimised Graph Neural Networks](https://mlsanity.com/paper/2405.00723/) (2024)
- [LDGCN: An Edge-End Lightweight Dual GCN Based on Single-Channel EEG for Driver Drowsiness Monitoring](https://mlsanity.com/paper/2407.05749/) (2024)
- [Exact Fit Attention in Node-Holistic Graph Convolutional Network for Improved EEG-Based Driver Fatigue Detection](https://mlsanity.com/paper/2501.15062/) (2025)
- [GSDNet: Revisiting Incomplete Multimodal-Diffusion from Graph Spectrum Perspective for Conversation Emotion Recognition](https://mlsanity.com/paper/2506.12325/) (2025)

## Abstract (arXiv)

Neuropsychological studies suggest that co-operative activities among different brain functional areas drive high-level cognitive processes. To learn the brain activities within and among different functional areas of the brain, we propose LGGNet, a novel neurologically inspired graph neural network, to learn local-global-graph representations of electroencephalography (EEG) for Brain-Computer Interface (BCI). The input layer of LGGNet comprises a series of temporal convolutions with multi-scale 1D convolutional kernels and kernel-level attentive fusion. It captures temporal dynamics of EEG which then serves as input to the proposed local and global graph-filtering layers. Using a defined neurophysiologically meaningful set of local and global graphs, LGGNet models the complex relations within and among functional areas of the brain. Under the robust nested cross-validation settings, the proposed method is evaluated on three publicly available datasets for four types of cognitive classification tasks, namely, the attention, fatigue, emotion, and preference classification tasks. LGGNet is compared with state-of-the-art methods, such as DeepConvNet, EEGNet, R2G-STNN, TSception, RGNN, AMCNN-DGCN, HRNN and GraphNet. The results show that LGGNet outperforms these methods, and the improvements are statistically significant (p<0.05) in most cases. The results show that bringing neuroscience prior knowledge into neural network design yields an improvement of classification performance. The source code can be found at https://github.com/yi-ding-cs/LGG