ArticleFrontiers in human neuroscience2015
Correlates of Near-Infrared Spectroscopy Brain-Computer Interface Accuracy in a Multi-Class Personalization Framework.
Article in Frontiers in human neuroscience, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Brain-Computer Interfaces for Children With Complex Communication Needs and Limited Mobility: A Systematic Review.Frontiers in human neuroscience · 2021Pooled it
- Guiding functional near-infrared spectroscopy optode-layout design using individual (f)MRI data: effects on signal strength.Neurophotonics · 2021Article
- See, Hear, or Feel - to Speak: A Versatile Multiple-Choice Functional Near-Infrared Spectroscopy-Brain-Computer Interface Feasible With Visual, Auditory, or Tactile Instructions.Frontiers in human neuroscience · 2021Article
- An Augmented-Reality fNIRS-Based Brain-Computer Interface: A Proof-of-Concept Study.Frontiers in neuroscience · 2020Article
- Brain-Based Binary Communication Using Spatiotemporal Features of fNIRS Responses.Frontiers in human neuroscience · 2020Article
- Feature Extraction and Classification Methods for Hybrid fNIRS-EEG Brain-Computer Interfaces.Frontiers in human neuroscience · 2018Review
- Hybrid EEG-fNIRS-Based Eight-Command Decoding for BCI: Application to Quadcopter Control.Frontiers in neurorobotics · 2017Article
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2 authors.
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Abstract
Brain-computer interfaces (BCIs) provide individuals with a means of interacting with a computer using only neural activity. To date, the majority of near-infrared spectroscopy (NIRS) BCIs have used prescribed tasks to achieve binary control. The goals of this study were to evaluate the possibility of using a personalized approach to establish control of a two-, three-, four-, and five-class NIRS-BCI, and to explore how various user characteristics correlate to accuracy. Ten able-bodied participants were recruited for five data collection sessions. Participants performed six mental tasks and a personalized approach was used to select each individual's best discriminating subset of tasks. The average offline cross-validation accuracies achieved were 78, 61, 47, and 37% for the two-, three-, four-, and five-class problems, respectively. Most notably, all participants exceeded an accuracy of 70% for the two-class problem, and two participants exceeded an accuracy of 70% for the three-class problem. Additionally, accuracy was found to be strongly positively correlated (Pearson's) with perceived ease of session (ρ = 0.653), ease of concentration (ρ = 0.634), and enjoyment (ρ = 0.550), but strongly negatively correlated with verbal IQ (ρ = -0.749).
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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.