Related Experiment Video
Updated: Jan 11, 2026
13:18
Conducting Concurrent Electroencephalography and Functional Near-Infrared Spectroscopy Recordings with a Flanker Task
Published on: May 24, 2020
8.2K
EFRM: A Multimodal EEG-fNIRS Representation-learning Model for few-shot brain-signal classification
1Division of Intelligent Robot, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu, 42988, South Korea.
Computers in Biology and Medicine
|November 12, 2025
Summary
This study introduces a multimodal brain signal model for robust classification using minimal labeled data. The approach enhances transfer learning for electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) signals, outperforming single-modality methods.
Area of Science:
- Neuroscience
- Machine Learning
- Biomedical Engineering
Background:
- Robust brain signal classifiers are needed for minimal labeled data scenarios.
- Transfer learning is a promising strategy, but existing methods often lack generalization across modalities like EEG and fNIRS.
Purpose of the Study:
- To develop a multimodal representation model adaptable to EEG-only, fNIRS-only, and paired EEG-fNIRS datasets.
- To improve transfer learning performance for brain signal classification, particularly for fNIRS signals.
Main Methods:
- A two-stage approach: pre-training for modality-specific and shared representations, followed by transfer learning for downstream tasks.
- Utilized large-scale unlabeled datasets (approx. 1250 h from 918 participants) for pre-training.
- Enabled adaptation to single-modality datasets, unlike previous multimodal methods.
Main Results:
- The multimodal model outperformed single-modality approaches by leveraging shared domains across EEG and fNIRS.
- Achieved competitive performance against state-of-the-art supervised models, even with limited labeled data.
- Demonstrated significant improvements in fNIRS classification compared to previously pre-trained models.
Conclusions:
- The proposed method offers a flexible and practical solution for brain signal classification using transfer learning.
- Effective for both single and multimodal brain signal data, enhancing classifier robustness.
- Advances the application of transfer learning in neuroimaging analysis, especially for fNIRS.
