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Related Experiment Video

Updated: Feb 28, 2026

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

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

Published on: June 30, 2018

Decoupled Bidirectional Spatio-Temporal Fusion Network for Hybrid EEG-fNIRS Cognitive Task Classification.

Zirui Wang¹, Guanghao Huang¹, Zhuochao Chen¹

¹Institute for Future, School of Automation, Qingdao University, Qingdao 266071, China.

|February 27, 2026

Summary

This summary is machine-generated.

This study introduces BiSTF-Net, a novel method for fusing electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) signals. The new approach significantly improves cognitive task recognition accuracy using multimodal neuroimaging.

Keywords:

EEG cognitive task classification decoupled fusion fNIRS multimodal neuroimaging spatio-temporal alignment

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Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study

Published on: July 21, 2021

Area of Science:

Neuroimaging
Cognitive Neuroscience
Biomedical Engineering

Background:

Multimodal neuroimaging, integrating electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), is crucial for brain function studies.
Significant spatio-temporal heterogeneity between EEG and fNIRS signals presents challenges for efficient data fusion.
Cognitive task recognition requires robust methods for combining diverse neural data streams.

Purpose of the Study:

To present the BiSTF-Net, a novel deep learning architecture for enhanced cognitive task recognition.
To address the challenge of fusing heterogeneous EEG and fNIRS signals for improved classification accuracy.
To develop a robust and interpretable solution for multimodal neuroimaging data analysis.

Main Methods:

Implemented a BiSTF-Net architecture featuring decoupled, bi-directional spatio-temporal fusion.
Utilized bi-directional cross-modal guidance (Bi-CMG) for mutual enhancement of spatial features between EEG and fNIRS.
Employed adaptive temporal alignment (ATA) for data-driven alignment of fNIRS signal latencies and symmetric cross-attention fusion (SCAF) for deep feature fusion.

Main Results:

BiSTF-Net achieved high average accuracies: 83.33% for mental arithmetic (MA), 82.09% for motor imagery (MI), and 84.99% for word generation (WG).
The proposed method demonstrated superior performance compared to existing techniques in cognitive task classification.
The fusion strategy resulted in a modality-invariant and discriminative representation of neural activity.

Conclusions:

BiSTF-Net offers a superior, robust, and interpretable approach for multimodal EEG-fNIRS cognitive task classification.
The method provides a strong foundation for future research in multimodal data fusion and clinical applications.
The spatio-temporal fusion mechanisms effectively address the heterogeneity of EEG and fNIRS signals.