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Updated: Apr 17, 2026

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Bidirectional Mamba-2 boosts EEG super-resolution via regression and diffusion.

Ugo Lomoio1,2, Pietro Lió3, Pietro Hiram Guzzi1

  • 1Department of Surgical and Medical Sciences, Magna Graecia University, Catanzaro 88100, Italy.

Bioinformatics (Oxford, England)
|April 15, 2026
PubMed
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This study introduces DiBiMa-EEGSR, a novel framework for enhancing electroencephalography (EEG) signal resolution. The method improves spatial and temporal accuracy without extra hardware, enabling clearer brain activity imaging.

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Electroencephalography (EEG) offers high temporal resolution for brain activity but suffers from limited spatial detail due to sparse electrode placement.
  • Improving spatial resolution in EEG is crucial for detailed neuroimaging and clinical diagnostics.

Purpose of the Study:

  • To develop a novel framework for spatio-temporal super-resolution of EEG signals using low-density recordings.
  • To enhance the anatomical coherence and contextual awareness of reconstructed high-resolution EEG data.

Main Methods:

  • Introduced DiBiMa-EEGSR, a bidirectional Mamba-2 diffusion framework for EEG super-resolution.
  • Formulated super-resolution as conditional generative inference, integrating diffusion with a bidirectional state-space model.

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  • Utilized low-resolution inputs, electrode positions, and task labels for conditioning, and employed a one-step sampling strategy for efficient inference.

    • Main Results:

    • DiBiMa-EEGSR significantly improved reconstruction accuracy, spatial coherence, and spectral preservation compared to existing methods.
    • The framework demonstrated superior performance in both spatial and temporal upsampling tasks across public benchmarks.
    • Achieved high-resolution EEG reconstruction without requiring additional hardware.

    Conclusions:

    • DiBiMa-EEGSR provides a scalable and effective solution for high-resolution electrophysiological imaging from standard EEG.
    • The proposed method advances EEG analysis by enabling more detailed insights into brain function and dysfunction.
    • The framework offers a promising pathway for improved non-invasive neuroimaging techniques.