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Updated: Jun 29, 2026

Motor Imagery Performance Through Embodied Digital Twins in a Virtual Reality-Enabled Brain-Computer Interface Environment
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A Robust Multi-Branch CNN-LSTM Architecture for Cross-Subject Motor Imagery Classification.

Simone Zini1, Federico Bidone1, Paolo Napoletano1

  • 1Department of Informatics, Systems and Communication, University of Milano-Bicocca, Viale Sarca 336, 20126 Milano, Italy.

Sensors (Basel, Switzerland)
|June 12, 2026
PubMed
Summary
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This study introduces a novel deep learning model for brain-computer interfaces (BCIs) that improves motor imagery (MI) decoding accuracy. The new architecture enhances signal processing for more reliable EEG-based device control.

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Machine Learning

Background:

  • Brain-computer interfaces (BCIs) using motor imagery (MI) translate EEG signals into device commands.
  • Challenges include low signal-to-noise ratio and inter-subject variability, hindering plug-and-play functionality.
  • Existing methods often require extensive calibration for reliable performance.

Purpose of the Study:

  • To develop a robust deep learning architecture for accurate and efficient MI decoding.
  • To address challenges of signal variability and improve portability of BCIs.
  • To reduce the need for lengthy user-specific calibration.

Main Methods:

  • Proposed a multi-branch convolutional long short-term memory (CNN-LSTM) architecture.
  • Employed multi-scale temporal feature extraction and within-trial sequence modeling.
Keywords:
BCIEEGLSTMdeep learningmotor imagery

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  • Utilized group normalization and supervision over sub-window sequences for enhanced robustness.

    • Main Results:

    • Achieved high accuracy on the EEGMMI dataset (up to 82.63% for binary, 74.10% for four-class MI).
    • Demonstrated strong subject-independent performance (74.9% for three-class MI).
    • Showcased effective zero-shot transfer and rapid fine-tuning on the ISLab-MI wearable dataset.

    Conclusions:

    • The proposed CNN-LSTM architecture significantly improves MI decoding accuracy and efficiency.
    • The model offers data-efficient and portable solutions for practical BCI applications.
    • This approach advances the development of reliable, low-calibration BCIs.