ArticleFrontiers in human neuroscience2022
Improved classification performance of EEG-fNIRS multimodal brain-computer interface based on multi-domain features and multi-level progressive learning.
Article in Frontiers in human neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed.
- Placebo and nocebo effects on pain through the lens of the predictive brain: Neurobiological mechanisms and translational implications.Experimental physiology · 2026Review
- Brain-Computer Interfaces: The Dawn of a New Era in Disease Treatment.Exploration (Beijing, China) · 2026Review
- Full-Stack Architectures for Intelligent Brain-Computer Interfaces.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- MGFNet: A Multi-Granularity Fusion Network with Coupling-Guided Sparse Routing for Hybrid EEG-fNIRS Decoding.Sensors (Basel, Switzerland) · 2026Article
- Toward practical BCIs: a BMNABC-based feature selection and sensor optimization framework for implicit learning detection from multimodal EEG-fNIRS data.Frontiers in human neuroscience · 2026Article
- Neurophysiological signatures of latent brain vulnerability in perioperative neurocognitive disorders: integrating EEG, cerebral oximetry, and processed EEG.Frontiers in medicine · 2026Review
- Machine Learning-Based Comparative Analysis of Subject-Independent EEG Data Classification Across Multiple Meditation and Non-Meditation Sessions.Sensors (Basel, Switzerland) · 2025Article
- Research on drug addiction detection based on AR-TSNET with bimodal EEG-NIRS.Biomedical engineering online · 2025Article
- TSFNet: Temporal-Spatial Fusion Network for Hybrid Brain-Computer Interface.Sensors (Basel, Switzerland) · 2025Article
- Compact Colocated Bimodal EEG/fNIRS Multi-Distance Sensor.Sensors (Basel, Switzerland) · 2025Article
- [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
- Novel machine learning-driven comparative analysis of CSP, STFT, and CSP-STFT fusion for EEG data classification across multiple meditation and non-meditation sessions in BCI pipeline.Brain informatics · 2025Article
- Multimodal fNIRS-EEG sensor fusion: Review of data-driven methods and perspective for naturalistic brain imaging.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Recent progress on smart lower prosthetic limbs: a comprehensive review on using EEG and fNIRS devices in rehabilitation.Frontiers in bioengineering and biotechnology · 2024Review
- Exploring the application and challenges of fNIRS technology in early detection of Parkinson's disease.Frontiers in aging neuroscience · 2024Article
- Motor imagery electroencephalogram classification algorithm based on joint features in the spatial and frequency domains and instance transfer.Frontiers in human neuroscience · 2023Article
- Temporal neural coupling: reconciling the Bobath concept with task-oriented training for post-stroke upper limb recovery-a perspective.Frontiers in neurologyArticle
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4 authors.
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Abstract
Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) have potentially complementary characteristics that reflect the electrical and hemodynamic characteristics of neural responses, so EEG-fNIRS-based hybrid brain-computer interface (BCI) is the research hotspots in recent years. However, current studies lack a comprehensive systematic approach to properly fuse EEG and fNIRS data and exploit their complementary potential, which is critical for improving BCI performance. To address this issue, this study proposes a novel multimodal fusion framework based on multi-level progressive learning with multi-domain features. The framework consists of a multi-domain feature extraction process for EEG and fNIRS, a feature selection process based on atomic search optimization, and a multi-domain feature fusion process based on multi-level progressive machine learning. The proposed method was validated on EEG-fNIRS-based motor imagery (MI) and mental arithmetic (MA) tasks involving 29 subjects, and the experimental results show that multi-domain features provide better classification performance than single-domain features, and multi-modality provides better classification performance than single-modality. Furthermore, the experimental results and comparison with other methods demonstrated the effectiveness and superiority of the proposed method in EEG and fNIRS information fusion, it can achieve an average classification accuracy of 96.74% in the MI task and 98.42% in the MA task. Our proposed method may provide a general framework for future fusion processing of multimodal brain signals based on EEG-fNIRS.
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