ArticleFrontiers in neurorobotics2017
Hybrid EEG-fNIRS-Based Eight-Command Decoding for BCI: Application to Quadcopter Control.
Article in Frontiers in neurorobotics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 76 papers.
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76 citing papers in PubMed.
- Technical system of electroencephalography-based brain-computer interface: Advances, applications, and challenges.Neural regeneration research · 2026Article
- Decoupled Bidirectional Spatio-Temporal Fusion Network for Hybrid EEG-fNIRS Cognitive Task Classification.Brain sciences · 2026Article
- Trait psychoticism is associated with greater intersubject neural dissimilarity during naturalistic social movie viewing.Scientific reports · 2026Article
- A hybrid CNN model for classification of motor tasks obtained from hybrid BCI system.Scientific reports · 2025Article
- Neurotechnology for enhancing human operation of robotic and semi-autonomous systems.Frontiers in robotics and AI · 2025Review
- Filter bank common spatial pattern and envelope-based features in multimodal EEG-fTCD brain-computer interfaces.PloS one · 2025Article
- A Brain Network Analysis Model for Motion Sickness in Electric Vehicles Based on EEG and fNIRS Signal Fusion.Sensors (Basel, Switzerland) · 2024Article
- Association of Compensatory Mechanisms in Prefrontal Cortex and Impaired Anatomical Correlates in Semantic Verbal Fluency: A Functional Near-Infrared Spectroscopy Study.Psychiatry investigation · 2024Article
- Investigating the relationship between CSAT scores and prefrontal fNIRS signals during cognitive tasks using a quantum annealing algorithm.Scientific reports · 2024Article
- Deep learning networks based decision fusion model of EEG and fNIRS for classification of cognitive tasks.Cognitive neurodynamics · 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
- Imagined Speech Classification Using EEG and Deep Learning.Bioengineering (Basel, Switzerland) · 2023Article
- Metaheuristic Optimization-Based Feature Selection for Imagery and Arithmetic Tasks: An fNIRS Study.Sensors (Basel, Switzerland) · 2023Article
- Deep learning-based framework for real-time upper limb motion intention classification using combined bio-signals.Frontiers in neurorobotics · 2023Article
- Investigating User Proficiency of Motor Imagery for EEG-Based BCI System to Control Simulated Wheelchair.Sensors (Basel, Switzerland) · 2022Article
- Concurrent fNIRS and EEG for Brain Function Investigation: A Systematic, Methodology-Focused Review.Sensors (Basel, Switzerland) · 2022Review
- A Comprehensive Review of Endogenous EEG-Based BCIs for Dynamic Device Control.Sensors (Basel, Switzerland) · 2022Review
- Multimodal Autoencoder Predicts fNIRS Resting State From EEG Signals.Neuroinformatics · 2022Article
- Sonomyography shows feasibility as a tool to quantify joint movement at the muscle level.IEEE ... International Conference on Rehabilitation Robotics : [proceedings] · 2022Article
16 more citing papers are in PubMed but not listed here.
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2 authors.
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Abstract
In this paper, a hybrid electroencephalography-functional near-infrared spectroscopy (EEG-fNIRS) scheme to decode eight active brain commands from the frontal brain region for brain-computer interface is presented. A total of eight commands are decoded by fNIRS, as positioned on the prefrontal cortex, and by EEG, around the frontal, parietal, and visual cortices. Mental arithmetic, mental counting, mental rotation, and word formation tasks are decoded with fNIRS, in which the selected features for classification and command generation are the peak, minimum, and mean ΔHbO values within a 2-s moving window. In the case of EEG, two eyeblinks, three eyeblinks, and eye movement in the up/down and left/right directions are used for four-command generation. The features in this case are the number of peaks and the mean of the EEG signal during 1 s window. We tested the generated commands on a quadcopter in an open space. An average accuracy of 75.6% was achieved with fNIRS for four-command decoding and 86% with EEG for another four-command decoding. The testing results show the possibility of controlling a quadcopter online and in real-time using eight commands from the prefrontal and frontal cortices
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