@article{4746,
  author = {Ahmed Naddami, Hajar Ait Lamkademe},
  title = {Modeling EEG Signals as Graphs: A GNN-Based Framework for Eye State Detection with Embedding Space Analysis},
  journal = {Journal of Data Processing},
  year = {2026},
  volume = {16},
  number = {2},
  doi = {https://doi.org/10.6025/jdp/2026/16/2/53-73},
  url = {https://www.dline.info/jdp/fulltext/v16n2/jdpv16n2_1.pdf},
  abstract = {Brain computer interfaces (BCIs) have emerged as a transformative technology enabling direct communication
between the human brain and external devices. Central to their effectiveness is the accurate
decoding of electroencephalography (EEG) signals, which encapsulate complex neural dynamics across
spatial and temporal scales. However, EEG signals are inherently noisy, high-dimensional, non-stationary,
and characterized by irregular spatial structures, making their analysis particularly challenging.
Traditional deep learning approaches, including convolutional neural networks (CNNs) and recurrent neural
networks (RNNs), have demonstrated considerable success in EEG classification tasks. Nevertheless, these
models operate under Euclidean assumptions and fail to adequately capture the intrinsic non-Euclidean
topology of brain connectivity. In contrast, Graph Neural Networks (GNNs) provide a principled framework
for modeling such data by explicitly incorporating relationships between EEG electrodes through graph
structures.
In this work, EEG signals are modeled as graphs, where electrodes correspond to nodes and functional
relationships define edges. A comprehensive formulation of GNN-based learning is presented, including
spectral and spatial graph convolutions, attention mechanisms, and spatiotemporal extensions. Furthermore,
embedding space analysis is discussed to enhance the interpretability of learned representations. The paper
synthesizes recent advancements in graph-based EEG modeling while highlighting key challenges and future
research directions.},
}