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Updated: Jan 14, 2026

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An Explainable 3D-Deep Learning Model for EEG Decoding in Brain-Computer Interface Applications.

Muhammad Suffian1, Cosimo Ieracitano2, Francesco C Morabito3

  • 1DIIES, University Mediterranea of Reggio Calabria, Via Zehender, Loc. Feo di Vito, Reggio Calabria 89122, Italy.

International Journal of Neural Systems
|October 19, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces EEGCubeNet, a deep learning framework for faster and more interpretable electroencephalographic (EEG) decoding in brain-computer interface (BCI) systems. It significantly reduces calibration time by using global-to-subject specific fine-tuning.

Keywords:
3D convolutional neural networksElectroencephalographybrain–computer interfacesexplainable artificial intelligence

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Area of Science:

  • Neuroscience
  • Machine Learning
  • Biomedical Engineering

Background:

  • Decoding electroencephalographic (EEG) signals is crucial for brain-computer interface (BCI) development.
  • High inter-subject variability in EEG necessitates time-consuming user-specific calibration, limiting deep learning applications.
  • Existing methods often require large datasets for effective model training.

Purpose of the Study:

  • To propose a multidimensional and explainable deep learning framework for fast and interpretable EEG decoding.
  • To address the challenges of inter-subject variability and reduce adaptation time in BCI systems.
  • To introduce a novel deep learning model, EEGCubeNet, for enhanced EEG signal processing.

Main Methods:

  • EEG signals were projected into the spatial-spectral-temporal domain.
  • A custom three-dimensional (3D) Convolutional Neural Network (EEGCubeNet) was developed.
  • A global-to-subject-specific fine-tuning approach was employed, combining population-level training with individual user fine-tuning.
  • A 3D occlusion sensitivity analysis-based explainability method (3D xAI-OSA) was introduced.

Main Results:

  • EEGCubeNet achieved state-of-the-art performance in discriminating motor imagery tasks (hand open/close) from a resting state.
  • The model demonstrated high accuracy ([Formula: see text] and [Formula: see text] for HC vs. RE and HO vs. RE, respectively).
  • Reduced framework complexity and training time were observed compared to existing methods.
  • The 3D xAI-OSA method provided relevance maps for enhanced prediction transparency.

Conclusions:

  • The proposed EEGCubeNet framework offers a significant advancement in fast and interpretable EEG decoding for BCI applications.
  • The global-to-subject-specific fine-tuning strategy effectively reduces adaptation time and improves model efficiency.
  • The integration of explainability features enhances the transparency and trustworthiness of the deep learning model.
  • The developed framework shows promise for broader adoption of deep learning in BCI research and development.