ArticleBioMed research international2020
A Computationally Efficient Method for Hybrid EEG-fNIRS BCI Based on the Pearson Correlation.
Article in BioMed research international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Improving Individual-Specific SSVEP-BCI with Adaptive Channel and Subspace Selection in TRCA.Sensors (Basel, Switzerland) · 2026Article
- A hybrid CNN model for classification of motor tasks obtained from hybrid BCI system.Scientific reports · 2025Article
- Filter bank common spatial pattern and envelope-based features in multimodal EEG-fTCD brain-computer interfaces.PloS one · 2025Article
- Multimodal fNIRS-EEG sensor fusion: Review of data-driven methods and perspective for naturalistic brain imaging.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Short-term microgravity effects simulation does not affect fNIRS measures of cerebral oxygenation changes induced by cognitive load.Frontiers in physiology · 2025Article
- A bimodal deep learning network based on CNN for fine motor imagery.Cognitive neurodynamics · 2024Article
- Electroencephalographic Signal Data Augmentation Based on Improved Generative Adversarial Network.Brain sciences · 2024Article
- Exploratory insights into prefrontal cortex activity in continuous glucose monitoring: findings from a portable wearable functional near-infrared spectroscopy system.Frontiers in neuroscience · 2024Article
- Graph-based EEG approach for depression prediction: integrating time-frequency complexity and spatial topology.Frontiers in neuroscience · 2024Article
- Recent progress on smart lower prosthetic limbs: a comprehensive review on using EEG and fNIRS devices in rehabilitation.Frontiers in bioengineering and biotechnology · 2024Review
- Mental Stress Classification Based on Selected Electroencephalography Channels Using Correlation Coefficient of Hjorth Parameters.Brain sciences · 2023Article
- OptEF-BCI: An Optimization-Based Hybrid EEG and fNIRS-Brain Computer Interface.Bioengineering (Basel, Switzerland) · 2023Article
- Metaheuristic Optimization-Based Feature Selection for Imagery and Arithmetic Tasks: An fNIRS Study.Sensors (Basel, Switzerland) · 2023Article
- Concurrent fNIRS and EEG for Brain Function Investigation: A Systematic, Methodology-Focused Review.Sensors (Basel, Switzerland) · 2022Review
- Bimodal EEG-fNIRS in Neuroergonomics. Current Evidence and Prospects for Future Research.Frontiers in neuroergonomics · 2022Review
- EEG-fNIRS-based hybrid image construction and classification using CNN-LSTM.Frontiers in neurorobotics · 2022Article
- Electroacupuncture Alters BCI-Based Brain Network in Stroke Patients.Computational intelligence and neuroscience · 2022Article
- Summary of over Fifty Years with Brain-Computer Interfaces-A Review.Brain sciences · 2021Review
- Hybrid EEG-fNIRS BCI Fusion Using Multi-Resolution Singular Value Decomposition (MSVD).Frontiers in human neuroscience · 2020Article
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
A hybrid brain computer interface (BCI) system considered here is a combination of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). EEG-fNIRS signals are simultaneously recorded to achieve high motor imagery task classification. This integration helps to achieve better system performance, but at the cost of an increase in system complexity and computational time. In hybrid BCI studies, channel selection is recognized as the key element that directly affects the system's performance. In this paper, we propose a novel channel selection approach using the Pearson product-moment correlation coefficient, where only highly correlated channels are selected from each hemisphere. Then, four different statistical features are extracted, and their different combinations are used for the classification through KNN and Tree classifiers. As far as we know, there is no report available that explored the Pearson product-moment correlation coefficient for hybrid EEG-fNIRS BCI channel selection. The results demonstrate that our hybrid system significantly reduces computational burden while achieving a classification accuracy with high reliability comparable to the existing literature.
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