# GCNs-Net: A Graph Convolutional Neural Network Approach for Decoding Time-resolved EEG Motor Imagery Signals

arXiv: https://arxiv.org/abs/2006.08924 · HTML: https://mlsanity.com/paper/2006.08924/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2020.

## Built on by (most cited first, 16 of 16)

- [Graph Neural Networks in IoT: A Survey](https://mlsanity.com/paper/2203.15935/) (2022)
- [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)
- [A Knowledge-Driven Cross-view Contrastive Learning for EEG Representation](https://mlsanity.com/paper/2310.03747/) (2023)
- [EEG_GLT-Net: Optimising EEG Graphs for Real-time Motor Imagery Signals Classification](https://mlsanity.com/paper/2404.11075/) (2024)
- [Wind Power Scenario Generation Using Graph Convolutional Generative Adversarial Network](https://mlsanity.com/paper/2212.10454/) (2022)
- [Privacy-preserving household load forecasting based on non-intrusive load monitoring: A federated deep learning approach](https://mlsanity.com/paper/2206.15192/) (2022)
- [A Human-Machine Joint Learning Framework to Boost Endogenous BCI Training](https://mlsanity.com/paper/2309.03209/) (2023)
- [EEG_RL-Net: Enhancing EEG MI Classification through Reinforcement Learning-Optimised Graph Neural Networks](https://mlsanity.com/paper/2405.00723/) (2024)
- [Mirror contrastive loss based sliding window transformer for subject-independent motor imagery based EEG signal recognition](https://mlsanity.com/paper/2409.00130/) (2024)
- [Graph Neural Networks Uncover Geometric Neural Representations in Reinforcement-Based Motor Learning](https://mlsanity.com/paper/2410.23812/) (2024)
- [Exact Fit Attention in Node-Holistic Graph Convolutional Network for Improved EEG-Based Driver Fatigue Detection](https://mlsanity.com/paper/2501.15062/) (2025)
- [EEG2GAIT: A Hierarchical Graph Convolutional Network for EEG-based Gait Decoding](https://mlsanity.com/paper/2504.03757/) (2025)
- [Motor Imagery EEG Signal Classification Using Minimally Random Convolutional Kernel Transform and Hybrid Deep Learning](https://mlsanity.com/paper/2508.16179/) (2025)
- [GL-LFGNN:A Global-Local Dual-branch Causal Graph Neural Network Based on Liang-Kleeman Information Flow for EEG Emotion Recognition](https://mlsanity.com/paper/2605.25061/) (2026)
- [EEG-Based Motor Imagery BCI Algorithms and Technologies: A Review](https://mlsanity.com/paper/2609.32930/) (2026)

## Abstract (arXiv)

Towards developing effective and efficient brain-computer interface (BCI) systems, precise decoding of brain activity measured by electroencephalogram (EEG), is highly demanded. Traditional works classify EEG signals without considering the topological relationship among electrodes. However, neuroscience research has increasingly emphasized network patterns of brain dynamics. Thus, the Euclidean structure of electrodes might not adequately reflect the interaction between signals. To fill the gap, a novel deep learning framework based on the graph convolutional neural networks (GCNs) is presented to enhance the decoding performance of raw EEG signals during different types of motor imagery (MI) tasks while cooperating with the functional topological relationship of electrodes. Based on the absolute Pearson's matrix of overall signals, the graph Laplacian of EEG electrodes is built up. The GCNs-Net constructed by graph convolutional layers learns the generalized features. The followed pooling layers reduce dimensionality, and the fully-connected softmax layer derives the final prediction. The introduced approach has been shown to converge for both personalized and group-wise predictions. It has achieved the highest averaged accuracy, 93.06% and 88.57% (PhysioNet Dataset), 96.24% and 80.89% (High Gamma Dataset), at the subject and group level, respectively, compared with existing studies, which suggests adaptability and robustness to individual variability. Moreover, the performance is stably reproducible among repetitive experiments for cross-validation. The excellent performance of our method has shown that it is an important step towards better BCI approaches. To conclude, the GCNs-Net filters EEG signals based on the functional topological relationship, which manages to decode relevant features for brain motor imagery.