ArticleCognitive neurodynamics2024
Deep learning networks based decision fusion model of EEG and fNIRS for classification of cognitive tasks.
Article in Cognitive neurodynamics, 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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10 citing papers in PubMed.
- 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
- TSFNet: Temporal-Spatial Fusion Network for Hybrid Brain-Computer Interface.Sensors (Basel, Switzerland) · 2025Article
- Advancing Cognitive Load Detection in Simulated Driving Scenarios Through Deep Learning and fNIRS Data.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
- Multi-modal cross-domain self-supervised pre-training for fMRI and EEG fusion.Neural networks : the official journal of the International Neural Network Society · 2025Article
- A working memory model based on recurrent neural networks using reinforcement learning.Cognitive neurodynamics · 2024Article
- Classification algorithm for motor imagery fusing CNN and attentional mechanisms based on functional near-infrared spectroscopy brain image.Cognitive neurodynamics · 2024Article
- Enhancing Classification Accuracy with Integrated Contextual Gate Network: Deep Learning Approach for Functional Near-Infrared Spectroscopy Brain-Computer Interface Application.Sensors (Basel, Switzerland) · 2024Article
- Enhancing learning experiences: EEG-based passive BCI system adapts learning speed to cognitive load in real-time, with motivation as catalyst.Frontiers in human neuroscience · 2024Article
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2 authors.
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Abstract
The detection of the cognitive tasks performed by a subject during data acquisition of a neuroimaging method has a wide range of applications: functioning of brain-computer interface (BCI), detection of neuronal disorders, neurorehabilitation for disabled patients, and many others. Recent studies show that the combination or fusion of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) demonstrates improved classification and detection performance compared to sole-EEG and sole-fNIRS. Deep learning (DL) networks are suitable for the classification of large volume time-series data like EEG and fNIRS. This study performs the decision fusion of EEG and fNIRS. The classification of EEG, fNIRS, and decision-fused EEG-fNIRSinto cognitive task labels is performed by DL networks. Two different open-source datasets of simultaneously recorded EEG and fNIRS are examined in this study. Dataset 01 is comprised of 26 subjects performing 3 cognitive tasks: n-back, discrimination or selection response (DSR), and word generation (WG). After data acquisition, fNIRS is converted to oxygenated hemoglobin (HbO
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