ArticleFrontiers in human neuroscience2016
Determining Optimal Feature-Combination for LDA Classification of Functional Near-Infrared Spectroscopy Signals in Brain-Computer Interface Application.
Article in Frontiers in human neuroscience, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.
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48 citing papers in PubMed.
- Prefrontal fNIRS hemodynamic correlates of attentional load during rapid serial visual presentation tasks.Frontiers in human neuroscience · 2026Article
- Detecting cognitive motor dissociation by functional near-infrared spectroscopy.Frontiers in neurology · 2025Article
- fNIRS-EEG BCIs for Motor Rehabilitation: A Review.Bioengineering (Basel, Switzerland) · 2023Review
- Driving drowsiness detection using spectral signatures of EEG-based neurophysiology.Frontiers in physiology · 2023Article
- A Graph-Based Nonlinear Dynamic Characterization of Motor Imagery Toward an Enhanced Hybrid BCI.Neuroinformatics · 2022Article
- Migraine classification by machine learning with functional near-infrared spectroscopy during the mental arithmetic task.Scientific reports · 2022Article
- Acupuncture enhances brain function in patients with mild cognitive impairment: evidence from a functional-near infrared spectroscopy study.Neural regeneration research · 2022Article
- LASSO Homotopy-Based Sparse Representation Classification for fNIRS-BCI.Sensors (Basel, Switzerland) · 2022Article
- Analyzing Classification Performance of fNIRS-BCI for Gait Rehabilitation Using Deep Neural Networks.Sensors (Basel, Switzerland) · 2022Article
- Explainable artificial intelligence model to predict brain states from fNIRS signals.Frontiers in human neuroscience · 2022Article
- Classification of Individual Finger Movements from Right Hand Using fNIRS Signals.Sensors (Basel, Switzerland) · 2021Article
- Most favorable stimulation duration in the sensorimotor cortex for fNIRS-based BCI.Biomedical optics express · 2021Article
- Vector Phase Analysis Approach for Sleep Stage Classification: A Functional Near-Infrared Spectroscopy-Based Passive Brain-Computer Interface.Frontiers in human neuroscience · 2021Article
- Subject-independent decoding of affective states using functional near-infrared spectroscopy.PloS one · 2021Article
- Motor Training Using Mental Workload (MWL) With an Assistive Soft Exoskeleton System: A Functional Near-Infrared Spectroscopy (fNIRS) Study for Brain-Machine Interface (BMI).Frontiers in neurorobotics · 2021Article
- Enhancing Classification Performance of fNIRS-BCI by Identifying Cortically Active Channels Using the z-Score Method.Sensors (Basel, Switzerland) · 2020Article
- Analysis of Human Gait Using Hybrid EEG-fNIRS-Based BCI System: A Review.Frontiers in human neuroscience · 2020Review
- Reduction of Onset Delay in Functional Near-Infrared Spectroscopy: Prediction of HbO/HbR Signals.Frontiers in neurorobotics · 2020Article
- Performance Improvement of Near-Infrared Spectroscopy-Based Brain-Computer Interface Using Regularized Linear Discriminant Analysis Ensemble Classifier Based on Bootstrap Aggregating.Frontiers in neuroscience · 2020Article
- Detection of Mild Cognitive Impairment Using Convolutional Neural Network: Temporal-Feature Maps of Functional Near-Infrared Spectroscopy.Frontiers in aging neuroscience · 2020Article
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4 authors.
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Abstract
In this study, we determine the optimal feature-combination for classification of functional near-infrared spectroscopy (fNIRS) signals with the best accuracies for development of a two-class brain-computer interface (BCI). Using a multi-channel continuous-wave imaging system, mental arithmetic signals are acquired from the prefrontal cortex of seven healthy subjects. After removing physiological noises, six oxygenated and deoxygenated hemoglobin (HbO and HbR) features-mean, slope, variance, peak, skewness and kurtosis-are calculated. All possible 2- and 3-feature combinations of the calculated features are then used to classify mental arithmetic vs. rest using linear discriminant analysis (LDA). It is found that the combinations containing mean and peak values yielded significantly higher (p < 0.05) classification accuracies for both HbO and HbR than did all of the other combinations, across all of the subjects. These results demonstrate the feasibility of achieving high classification accuracies using mean and peak values of HbO and HbR as features for classification of mental arithmetic vs. rest for a two-class BCI.
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