ArticleFrontiers in computational neuroscience2023
Riemannian geometry-based metrics to measure and reinforce user performance changes during brain-computer interface user training.
Article in Frontiers in computational neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Exploration-based feedback for BCI training: a case study with an adolescent with paraplegia.Journal of neuroengineering and rehabilitation · 2026Article
- Ethical imperatives in the commercialization of brain-computer interfaces.IBRO neuroscience reports · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite growing interest and research into brain-computer interfaces (BCI), their usage remains limited outside of research laboratories. One reason for this is BCI inefficiency, the phenomenon where a significant number of potential users are unable to produce machine-discernible brain signal patterns to control the devices. To reduce the prevalence of BCI inefficiency, some have advocated for novel user-training protocols that enable users to more effectively modulate their neural activity. Important considerations for the design of these protocols are the assessment measures that are used for evaluating user performance and for providing feedback that guides skill acquisition. Herein, we present three trial-wise adaptations (running, sliding window and weighted average) of Riemannian geometry-based user-performance metrics (
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