ArticleBiomedical optics express2021
Multimodal fusion of EEG-fNIRS: a mutual information-based hybrid classification framework.
Article in Biomedical optics express, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- A weighted EEG-fNIRS integration model enhances cortical representation of inter-muscular coordination.Medical & biological engineering & computing · 2026Article
- EEG-fNIRS Cross-Subject Emotion Recognition Based on Attention Graph Isomorphism Network and Contrastive Learning.Brain sciences · 2026Article
- Hybrid EEG-fNIRS phoneme classification based on imagined and perceived speech.Frontiers in neuroergonomics · 2026Article
- [Prospects and technical challenges of non-invasive brain-computer interfaces in manned space missions].Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2025Article
- Feature fusion analysis approach based on synchronous EEG-fNIRS signals: application in etomidate use disorder individuals.Biomedical optics express · 2025Article
- Multi-branch GAT-GRU-transformer for explainable EEG-based finger motor imagery classification.Frontiers in human neuroscience · 2025Article
- Multimodal fNIRS-EEG sensor fusion: Review of data-driven methods and perspective for naturalistic brain imaging.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- A Brain Network Analysis Model for Motion Sickness in Electric Vehicles Based on EEG and fNIRS Signal Fusion.Sensors (Basel, Switzerland) · 2024Article
- EEG Dataset for the Recognition of Different Emotions Induced in Voice-User Interaction.Scientific data · 2024Article
- Deep learning networks based decision fusion model of EEG and fNIRS for classification of cognitive tasks.Cognitive neurodynamics · 2024Article
- A Within-Subject Multimodal NIRS-EEG Classifier for Infant Data.Sensors (Basel, Switzerland) · 2024Article
- Efficient Feature Learning Model of Motor Imagery EEG Signals with L1-Norm and Weighted Fusion.Biosensors · 2024Article
- EF-Net: Mental State Recognition by Analyzing Multimodal EEG-fNIRS via CNN.Sensors (Basel, Switzerland) · 2024Article
- Transformer-Based Multi-Modal Data Fusion Method for COPD Classification and Physiological and Biochemical Indicators Identification.Biomolecules · 2023Article
- OptEF-BCI: An Optimization-Based Hybrid EEG and fNIRS-Brain Computer Interface.Bioengineering (Basel, Switzerland) · 2023Article
- Age-dependent neurovascular coupling characteristics in children and adults during general anesthesia.Biomedical optics express · 2023Article
- Functional Near-Infrared Spectroscopy as a Personalized Digital Healthcare Tool for Brain Monitoring.Journal of clinical neurology (Seoul, Korea) · 2023Review
- A Graph-Based Nonlinear Dynamic Characterization of Motor Imagery Toward an Enhanced Hybrid BCI.Neuroinformatics · 2022Article
- Using Convolutional Neural Networks for the Assessment Research of Mental Health.Computational intelligence and neuroscience · 2022Article
- Most favorable stimulation duration in the sensorimotor cortex for fNIRS-based BCI.Biomedical optics express · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Multimodal data fusion is one of the current primary neuroimaging research directions to overcome the fundamental limitations of individual modalities by exploiting complementary information from different modalities. Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) are especially compelling modalities due to their potentially complementary features reflecting the electro-hemodynamic characteristics of neural responses. However, the current multimodal studies lack a comprehensive systematic approach to properly merge the complementary features from their multimodal data. Identifying a systematic approach to properly fuse EEG-fNIRS data and exploit their complementary potential is crucial in improving performance. This paper proposes a framework for classifying fused EEG-fNIRS data at the feature level, relying on a mutual information-based feature selection approach with respect to the complementarity between features. The goal is to optimize the complementarity, redundancy and relevance between multimodal features with respect to the class labels as belonging to a pathological condition or healthy control. Nine amyotrophic lateral sclerosis (ALS) patients and nine controls underwent multimodal data recording during a visuo-mental task. Multiple spectral and temporal features were extracted and fed to a feature selection algorithm followed by a classifier, which selected the optimized subset of features through a cross-validation process. The results demonstrated considerably improved hybrid classification performance compared to the individual modalities and compared to conventional classification without feature selection, suggesting a potential efficacy of our proposed framework for wider neuro-clinical applications.
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Registered trials
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