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Related Experiment Video

Updated: Jun 13, 2026

Conducting Concurrent Electroencephalography and Functional Near-Infrared Spectroscopy Recordings with a Flanker Task
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Conducting Concurrent Electroencephalography and Functional Near-Infrared Spectroscopy Recordings with a Flanker Task

Published on: May 24, 2020

EC-Transformer: Connectivity-Informed Embeddings and Adaptive Gating for fNIRS.

Neda Abdollahpour, N Sertac Artan

    IEEE Journal of Biomedical and Health Informatics
    |June 11, 2026
    PubMed
    Summary

    This study introduces the Effective Connectivity Transformer (EC-Transformer) for brain-computer interface (BCI) decoding using functional near-infrared spectroscopy (fNIRS) signals. The EC-Transformer enhances decoding accuracy by integrating brain connectivity patterns with temporal dynamics, achieving competitive performance with lower complexity.

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    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Machine Learning

    Background:

    • Functional Near-Infrared Spectroscopy (fNIRS) is a non-invasive brain activity monitoring tool.
    • Accurate brain-computer interface (BCI) decoding faces challenges in modeling both temporal dynamics and inter-regional brain interactions.
    • Existing transformer-based models for fNIRS decoding can be complex and computationally intensive.

    Purpose of the Study:

    • To propose an Effective Connectivity Transformer (EC-Transformer) for improved fNIRS signal decoding in BCIs.
    • To integrate connectivity-informed representations into transformer models for fNIRS data.
    • To enhance decoding accuracy and computational efficiency compared to existing methods.

    Main Methods:

    • Developed the EC-Transformer architecture combining time-wise and connectivity-based embeddings.

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  • Utilized positional encoding and bidirectional LSTMs for temporal dynamics.
  • Incorporated an adaptive gating mechanism for dynamic fusion of representations.
  • Evaluated the model on two public fNIRS datasets using leave-one-subject-out validation.

    • Main Results:

    • Achieved decoding accuracies of 76.83% ± 2.4% for mental arithmetic and 76.03% ± 2.00% for motor execution tasks.
    • Demonstrated competitive performance with significantly lower model complexity (0.7M parameters vs. 1.7M-3.5M).
    • Ablation studies confirmed the benefit of EC-based embeddings for complementing temporal modeling.
    • Interpretability analyses revealed task-related connectivity patterns consistent with known brain networks.

    Conclusions:

    • The EC-Transformer effectively integrates connectivity information for enhanced fNIRS decoding in BCIs.
    • The proposed framework offers a computationally efficient alternative to existing complex models.
    • Incorporating physiologically structured connectivity data improves transformer-based fNIRS decoding performance.
    • Findings support the use of connectivity-informed representations for advancing BCI technology.