ArticleFrontiers in neuroinformatics2018
A Ternary Hybrid EEG-NIRS Brain-Computer Interface for the Classification of Brain Activation Patterns during Mental Arithmetic, Motor Imagery, and Idle State.
Article in Frontiers in neuroinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 2 of them syntheses that pooled it.
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Who cites it
36 citing papers in PubMed, 2 syntheses or guidelines pooled it, 93 citations in OpenAlex.
- The relationship between motor and cognitive performance and cortical activity in patients with multiple sclerosis: a systematic review of functional near-infrared spectroscopy studies.Frontiers in rehabilitation sciences · 2026Pooled it
- Motor-Imagery EEG-Based BCIs in Wheelchair Movement and Control: A Systematic Literature Review.Sensors (Basel, Switzerland) · 2021Pooled it
- Advances in fully integrated wearable near-infrared spectroscopy: hardware innovations and biomedical applications.JPhys photonics · 2026Review
- Unsupervised Dynamic Time Warping Clustering for Robust Functional Network Identification in fNIRS Motor Tasks.Sensors (Basel, Switzerland) · 2026Article
- Hybrid EEG-fNIRS phoneme classification based on imagined and perceived speech.Frontiers in neuroergonomics · 2026Article
- Development of Wearable Wireless Multichannel f-NIRS System to Evaluate Activities.Micromachines · 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
- Deep learning networks based decision fusion model of EEG and fNIRS for classification of cognitive tasks.Cognitive neurodynamics · 2024Article
- Cortical signals analysis to recognize intralimb mobility using modified RNN and various EEG quantities.Heliyon · 2024Article
- EF-Net: Mental State Recognition by Analyzing Multimodal EEG-fNIRS via CNN.Sensors (Basel, Switzerland) · 2024Article
- Feasibility of local interpretable model-agnostic explanations (LIME) algorithm as an effective and interpretable feature selection method: comparative fNIRS study.Biomedical engineering letters · 2023Article
- Understanding, detecting, and stimulating consciousness recovery in the ICU.Acta neurochirurgica · 2023Review
- Revealing brain connectivity: graph embeddings for EEG representation learning and comparative analysis of structural and functional connectivity.Frontiers in neuroscience · 2023Article
- A case for hybrid BCIs: combining optical and electrical modalities improves accuracy.Frontiers in human neuroscience · 2023Article
- Concurrent fNIRS and EEG for Brain Function Investigation: A Systematic, Methodology-Focused Review.Sensors (Basel, Switzerland) · 2022Review
- Subdividing Stress Groups into Eustress and Distress Groups Using Laterality Index Calculated from Brain Hemodynamic Response.Biosensors · 2022Article
- EEG hybrid brain-computer interfaces: A scoping review applying an existing hybrid-BCI taxonomy and considerations for pediatric applications.Frontiers in human neuroscience · 2022Article
- Bimodal EEG-fNIRS in Neuroergonomics. Current Evidence and Prospects for Future Research.Frontiers in neuroergonomics · 2022Review
- Multimodal measurement approach to identify individuals with mild cognitive impairment: study protocol for a cross-sectional trial.BMJ open · 2021Article
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3 authors at 1 institution in 1 country.
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Abstract
The performance of a brain-computer interface (BCI) can be enhanced by simultaneously using two or more modalities to record brain activity, which is generally referred to as a hybrid BCI. To date, many BCI researchers have tried to implement a hybrid BCI system by combining electroencephalography (EEG) and functional near-infrared spectroscopy (NIRS) to improve the overall accuracy of binary classification. However, since hybrid EEG-NIRS BCI, which will be denoted by hBCI in this paper, has not been applied to ternary classification problems, paradigms and classification strategies appropriate for ternary classification using hBCI are not well investigated. Here we propose the use of an hBCI for the classification of three brain activation patterns elicited by mental arithmetic, motor imagery, and idle state, with the aim to elevate the information transfer rate (ITR) of hBCI by increasing the number of classes while minimizing the loss of accuracy. EEG electrodes were placed over the prefrontal cortex and the central cortex, and NIRS optodes were placed only on the forehead. The ternary classification problem was decomposed into three binary classification problems using the "one-versus-one" (OVO) classification strategy to apply the filter-bank common spatial patterns filter to EEG data. A 10 × 10-fold cross validation was performed using shrinkage linear discriminant analysis (sLDA) to evaluate the average classification accuracies for EEG-BCI, NIRS-BCI, and hBCI when the meta-classification method was adopted to enhance classification accuracy. The ternary classification accuracies for EEG-BCI, NIRS-BCI, and hBCI were 76.1 ± 12.8, 64.1 ± 9.7, and 82.2 ± 10.2%, respectively. The classification accuracy of the proposed hBCI was thus significantly higher than those of the other BCIs (
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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.