ArticleFrontiers in human neuroscience2014
Decoding of four movement directions using hybrid NIRS-EEG brain-computer interface.
Article in Frontiers in human neuroscience, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 80 papers.
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Who cites it
80 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
- Decoupled Bidirectional Spatio-Temporal Fusion Network for Hybrid EEG-fNIRS Cognitive Task Classification.Brain sciences · 2026Article
- Hybrid EEG-fNIRS phoneme classification based on imagined and perceived speech.Frontiers in neuroergonomics · 2026Article
- Toward practical BCIs: a BMNABC-based feature selection and sensor optimization framework for implicit learning detection from multimodal EEG-fNIRS data.Frontiers in human neuroscience · 2026Article
- fNIRS-EEG BCIs for Motor Rehabilitation: A Review.Bioengineering (Basel, Switzerland) · 2023Review
- Towards hippocampal navigation for brain-computer interfaces.Scientific reports · 2023Article
- EEG/fNIRS Based Workload Classification Using Functional Brain Connectivity and Machine Learning.Sensors (Basel, Switzerland) · 2022Article
- Concurrent fNIRS and EEG for Brain Function Investigation: A Systematic, Methodology-Focused Review.Sensors (Basel, Switzerland) · 2022Review
- Unmanned Aerial Vehicle for Laser Based Biomedical Sensor Development and Examination of Device Trajectory.Sensors (Basel, Switzerland) · 2022Article
- Intelligent Extraction of Salient Feature From Electroencephalogram Using Redundant Discrete Wavelet Transform.Frontiers in neuroscience · 2022Article
- Review
- 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
- Decoding Multiple Sound-Categories in the Auditory Cortex by Neural Networks: An fNIRS Study.Frontiers in human neuroscience · 2021Article
- Article
- Subject-Independent Functional Near-Infrared Spectroscopy-Based Brain-Computer Interfaces Based on Convolutional Neural Networks.Frontiers in human neuroscience · 2021Article
- [Recognition of three different imagined movement of the right foot based on functional near-infrared spectroscopy].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2020Article
- Decoding Three Different Preference Levels of Consumers Using Convolutional Neural Network: A Functional Near-Infrared Spectroscopy Study.Frontiers in human neuroscience · 2020Article
- Analysis of Human Gait Using Hybrid EEG-fNIRS-Based BCI System: A Review.Frontiers in human neuroscience · 2020Review
- Hybrid EEG-fNIRS BCI Fusion Using Multi-Resolution Singular Value Decomposition (MSVD).Frontiers in human neuroscience · 2020Article
- Performance Improvement for Detecting Brain Function Using fNIRS: A Multi-Distance Probe Configuration With PPL Method.Frontiers in human neuroscience · 2020Article
20 more citing papers are in PubMed but not listed here.
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3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The hybrid brain-computer interface (BCI)'s multimodal technology enables precision brain-signal classification that can be used in the formulation of control commands. In the present study, an experimental hybrid near-infrared spectroscopy-electroencephalography (NIRS-EEG) technique was used to extract and decode four different types of brain signals. The NIRS setup was positioned over the prefrontal brain region, and the EEG over the left and right motor cortex regions. Twelve subjects participating in the experiment were shown four direction symbols, namely, "forward," "backward," "left," and "right." The control commands for forward and backward movement were estimated by performing arithmetic mental tasks related to oxy-hemoglobin (HbO) changes. The left and right directions commands were associated with right and left hand tapping, respectively. The high classification accuracies achieved showed that the four different control signals can be accurately estimated using the hybrid NIRS-EEG technology.
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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.