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Related Experiment Video

Updated: Jun 13, 2026

Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
11:25

Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

MGFNet: A Multi-Granularity Fusion Network with Coupling-Guided Sparse Routing for Hybrid EEG-fNIRS Decoding.

Yan Zhang1, Xiaoyu Gong1, Xiaoyang Yuan1

  • 1School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China.

Sensors (Basel, Switzerland)
|June 12, 2026
PubMed
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MGFNet enhances hybrid brain-computer interfaces (BCIs) by effectively fusing electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) data. This novel approach achieves high accuracy in decoding cognitive tasks and demonstrates improved robustness against signal degradation.

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Hybrid brain-computer interfaces (BCIs) integrate electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) for improved performance.
  • Existing deep fusion methods often use static late-fusion, limiting their ability to capture cross-modal dependencies and handle signal degradation.
  • There is a need for advanced fusion strategies that can effectively combine EEG and fNIRS data while being robust to noise.

Purpose of the Study:

  • To propose MGFNet, a multi-granularity fusion network for enhanced hybrid BCI decoding.
  • To address the limitations of static late-fusion methods in exploiting cross-modal dependencies and mitigating modality-specific signal degradation.
  • To evaluate the performance and robustness of MGFNet on benchmark cognitive tasks.
Keywords:
CGSCREEG-fNIRS hybrid BCIadaptive routingcross-modal couplingmultimodal fusionrobustness

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Related Experiment Videos

Last Updated: Jun 13, 2026

Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
11:25

Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
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Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

Main Methods:

  • Developed MGFNet with intra-modal encoders for modality-specific representations (EEG, HbO, HbR).
  • Incorporated cross-modal interaction encoders using dilated convolutions for long-range EEG-fNIRS dependency capture.
  • Introduced a Coupling-Guided Sparse Component Routing (CGSCR) module for adaptive routing and a deep supervision strategy for optimization.

Main Results:

  • MGFNet achieved high classification accuracies: 99.40% on the n-back task and 99.03% on the word generation (WG) task.
  • Outperformed representative comparison methods under a matched within-subject evaluation protocol.
  • Demonstrated significant robustness against controlled EEG corruption, outperforming a static-fusion variant by over 9% on the n-back task.

Conclusions:

  • MGFNet effectively decodes cognitive tasks using hybrid EEG-fNIRS data.
  • The proposed multi-granularity fusion approach significantly improves BCI performance and robustness.
  • MGFNet represents a promising advancement for hybrid BCI applications, particularly in challenging signal conditions.