ArticleSensors (Basel, Switzerland)2024
EF-Net: Mental State Recognition by Analyzing Multimodal EEG-fNIRS via CNN.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Spatial-Frequency Hypergraph Neural Network for EEG-fNIRS Emotion Recognition.Brain sciences · 2026Article
- MSA-CNN: A Multi-Scale Attention Convolutional Neural Network for fNIRS-Based Emotion Recognition.Biosensors · 2026Article
- Detecting and Improving Human Cognitive State in Real-Time Using Data-Driven Adaptive Systems: A Systematic Review.Bioengineering (Basel, Switzerland) · 2026Review
- MGFNet: A Multi-Granularity Fusion Network with Coupling-Guided Sparse Routing for Hybrid EEG-fNIRS Decoding.Sensors (Basel, Switzerland) · 2026Article
- Decoupled Bidirectional Spatio-Temporal Fusion Network for Hybrid EEG-fNIRS Cognitive Task Classification.Brain sciences · 2026Article
- Functional Near-Infrared Spectroscopy (fNIRS) in Objective Audiometry: A Scoping Review and Clinical Perspectives.Audiology research · 2025Review
- TSFNet: Temporal-Spatial Fusion Network for Hybrid Brain-Computer Interface.Sensors (Basel, Switzerland) · 2025Article
- TopoTempNet: A High-Accuracy and Interpretable Decoding Method for fNIRS-Based Motor Imagery.Sensors (Basel, Switzerland) · 2025Article
- A review of hybrid EEG-based multimodal human-computer interfaces using deep learning: applications, advances, and challenges.Biomedical engineering letters · 2025Review
- Multimodal MBC-ATT: cross-modality attentional fusion of EEG-fNIRS for cognitive state decoding.Frontiers in human neuroscience · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Analysis of brain signals is essential to the study of mental states and various neurological conditions. The two most prevalent noninvasive signals for measuring brain activities are electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). EEG, characterized by its higher sampling frequency, captures more temporal features, while fNIRS, with a greater number of channels, provides richer spatial information. Although a few previous studies have explored the use of multimodal deep-learning models to analyze brain activity for both EEG and fNIRS,
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.