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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

TSFNet: Temporal-Spatial Fusion Network for Hybrid Brain-Computer Interface.

Yan Zhang¹, Bo Yin¹, Xiaoyang Yuan¹

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

Sensors (Basel, Switzerland)

|October 16, 2025

Summary

This summary is machine-generated.

This study introduces a novel Temporal-Spatial Fusion Network (TSFNet) for hybrid brain-computer interfaces (BCIs) that effectively integrates electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) signals. TSFNet significantly improves classification accuracy for motor imagery, mental arithmetic, and word generation tasks.

Keywords:

deep learning electroencephalography functional near-infrared spectroscopy hybrid brain-computer interface multimodal fusion

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Area of Science:

Neuroscience
Biomedical Engineering
Signal Processing

Background:

Unimodal brain-computer interfaces (BCIs) face limitations due to single-modality constraints.
Hybrid BCIs combining electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) offer complementary data but struggle with spatiotemporal feature integration due to signal asynchrony.

Purpose of the Study:

To develop a novel deep fusion network for synergistic integration of EEG and fNIRS signals.
To enhance classification performance in hybrid BCIs across diverse tasks.

Main Methods:

Proposed a Temporal-Spatial Fusion Network (TSFNet) with EEG-fNIRS-guided Fusion (EFGF) and Cross-Attention-based Feature Enhancement (CAFÉ) layers.
EFGF layer extracts temporal (EEG) and spatial (fNIRS) features for integrated attention mapping.
CAFÉ layer uses cross-attention for bidirectional interaction, enhancing fusion and filtering fNIRS data.

Main Results:

TSFNet achieved superior classification performance on motor imagery (MI), mental arithmetic (MA), and word generation (WG) tasks.
Average accuracies reached 70.18% for MI, 86.26% for MA, and 81.13% for WG.
Outperformed existing state-of-the-art multimodal algorithms in classification accuracy.

Conclusions:

TSFNet offers an effective solution for deep fusion of multimodal spatiotemporal features in hybrid BCIs.
The proposed network demonstrates significant potential for real-world BCI applications.
Synergistic integration of EEG and fNIRS via TSFNet overcomes limitations of unimodal and simpler multimodal approaches.