ArticleJournal of medical systems2015
Classification of hemodynamic responses associated with force and speed imagery for a brain-computer interface.
Article in Journal of medical systems, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- Virtual and Actual Humanoid Robot Control with Four-Class Motor-Imagery-Based Optical Brain-Computer Interface.BioMed research international · 2017Trial
- Classifying Motion Intention of Step Length and Synchronous Walking Speed by Functional Near-Infrared Spectroscopy.Cyborg and bionic systems (Washington, D.C.) · 2021Article
- Symbolic time series analysis of fNIRS signals in brain development assessment.Journal of neural engineering · 2018Article
- Feature Extraction and Classification Methods for Hybrid fNIRS-EEG Brain-Computer Interfaces.Frontiers in human neuroscience · 2018Review
- Article
- Existence of Initial Dip for BCI: An Illusion or Reality.Frontiers in neurorobotics · 2018Review
- Pilot Study on Gait Classification Using fNIRS Signals.Computational intelligence and neuroscience · 2018Article
- Hybrid Brain-Computer Interface Techniques for Improved Classification Accuracy and Increased Number of Commands: A Review.Frontiers in neurorobotics · 2017Review
- Comparison of Brain Activation during Motor Imagery and Motor Movement Using fNIRS.Computational intelligence and neuroscience · 2017Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Functional near-infrared spectroscopy (fNIRS) is an emerging optical technique, which can assess brain activities associated with tasks. In this study, six participants were asked to perform three imageries of hand clenching associated with force and speed, respectively. Joint mutual information (JMI) criterion was used to extract the optimal features of hemodynamic responses. And extreme learning machine (ELM) was employed to be the classifier. ELM solved the major bottleneck of feedforward neural networks in learning speed, this classifier was easily implemented and less sensitive to specified parameters. The 2-class fNIRS-BCI system was firstly built with an average accuracy of 76.7%, when all force and speed tasks were categorized as one class, respectively. The multi-class systems based on different levels of force and speed attempted to be investigated, the accuracies were moderate. This study provided a novel paradigm for establishing fNIRS-BCI system, and provided a possibility to produce more degrees of freedom in BCI system.
Indexed as
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Registered trials
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.