ArticlePloS one2019
A conceptual space for EEG-based brain-computer interfaces.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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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
6 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Past, Present, and Future of EEG-Based BCI Applications.Sensors (Basel, Switzerland) · 2022Pooled it
- Brain-Computer Interfaces Systems for Upper and Lower Limb Rehabilitation: A Systematic Review.Sensors (Basel, Switzerland) · 2021Pooled it
- A Brain-Controlled Quadruped Robot: A Proof-of-Concept Demonstration.Sensors (Basel, Switzerland) · 2023Article
- An Optimal Transport Based Transferable System for Detection of Erroneous Somato-Sensory Feedback from Neural Signals.Brain sciences · 2021Article
- Anytime collaborative brain-computer interfaces for enhancing perceptual group decision-making.Scientific reports · 2021Article
- Transcending the brain: is there a cost to hacking the nervous system?Brain communications · 2019Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Brain-Computer Interfaces (BCIs) have become more and more popular these last years. Researchers use this technology for several types of applications, including attention and workload measures but also for the direct control of objects by the means of BCIs. In this work we present a first, multidimensional feature space for EEG-based BCI applications to help practitioners to characterize, compare and design systems, which use EEG-based BCIs. Our feature space contains 4 axes and 9 sub-axes and consists of 41 options in total as well as their different combinations. We presented the axes of our feature space and we positioned our feature space regarding the existing BCI and HCI taxonomies and we showed how our work integrates the past works, and/or complements them.
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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.