ArticleFrontiers in human neuroscience2017
Enhancing Performance of a Hybrid EEG-fNIRS System Using Channel Selection and Early Temporal Features.
Article in Frontiers in human neuroscience, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.
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Who cites it
47 citing papers in PubMed.
- EEG vs. Hybrid EEG-fNIRS BCI for FES Control in Healthy Subjects: A Blind Randomized Study and an Open Dataset.Sensors (Basel, Switzerland) · 2026Trial
- Hand movements prediction via fNIRS and AI algorithms.BMC medical informatics and decision making · 2026Article
- Functional near-infrared spectroscopy study of intermittent theta burst stimulation on lower-limb motor dysfunction in patients with post-stroke hemiplegia.Frontiers in human neuroscience · 2026Article
- Hybrid EEG-fNIRS phoneme classification based on imagined and perceived speech.Frontiers in neuroergonomics · 2026Article
- Wearable technologies for assisted mobility in the real world.Nature communications · 2025Review
- Article
- Aberrant neural activation during inhibitory control in girls with fragile X syndrome.Cerebral cortex (New York, N.Y. : 1991) · 2025Article
- Protocol for Simultaneous EEG and fNIRS Measurement in Characterizing Brain State.Phenomics (Cham, Switzerland) · 2025Article
- Co-localized optode-electrode design for multimodal functional near infrared spectroscopy and electroencephalography.Neurophotonics · 2025Article
- Investigating the cortical effect of false positive feedback on motor learning in motor imagery based rehabilitative BCI training.Journal of neuroengineering and rehabilitation · 2025Article
- Longitudinal Changes in Functional Neural Activation and Sensitization During Face Processing in Fragile X Syndrome.Biological psychiatry · 2025Article
- A hybrid CNN model for classification of motor tasks obtained from hybrid BCI system.Scientific reports · 2025Article
- Multimodal fNIRS-EEG sensor fusion: Review of data-driven methods and perspective for naturalistic brain imaging.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- A novel scrolling text reading paradigm for improving the performance of multiclass and hybrid brain computer interface systems.PloS one · 2025Article
- Investigating the relationship between CSAT scores and prefrontal fNIRS signals during cognitive tasks using a quantum annealing algorithm.Scientific reports · 2024Article
- Article
- EF-Net: Mental State Recognition by Analyzing Multimodal EEG-fNIRS via CNN.Sensors (Basel, Switzerland) · 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
- fNIRS-EEG BCIs for Motor Rehabilitation: A Review.Bioengineering (Basel, Switzerland) · 2023Review
- Automatic depression diagnosis through hybrid EEG and near-infrared spectroscopy features using support vector machine.Frontiers in neuroscience · 2023Article
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4 authors.
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Abstract
Brain-Computer Interface (BCI) techniques hold a great promise for neuroprosthetic applications. A desirable BCI system should be portable, minimally invasive, and feature high classification accuracy and efficiency. As two commonly used non-invasive brain imaging modalities, Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) BCI system have often been incorporated in the development of hybrid BCI systems, largely due to their complimentary properties. In this study, we aimed to investigate whether the early temporal information extracted from singular EEG and fNIRS channels on each hemisphere can be used to enhance the accuracy and efficiency of a hybrid EEG-fNIRS BCI system. Eleven healthy volunteers were recruited and underwent simultaneous EEG-fNIRS recording during a motor execution task that included left and right hand movements. Singular EEG and fNIRS channels corresponding to the motor cortices of each hemisphere were selected using a general linear model. Early temporal information was extracted from the EEG channel (0-1 s) along with initial hemodynamic dip information from fNIRS (0-2 s) for classification using a support vector machine (SVM). Results demonstrated a lofty classification accuracy using a minimal number of channels and features derived from early temporal information. In conclusion, a hybrid EEG-fNIRS BCI system can achieve higher classification accuracy (91.02 ± 4.08%) and efficiency by integrating their complimentary properties, compared to using EEG (85.64 ± 7.4%) or fNIRS alone (85.55 ± 10.72%). Such a hybrid system can also achieve minimal response lag in application by focusing on rapidly-evolving brain dynamics.
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