ArticleFrontiers in neuroscience2020
An Inter- and Intra-Subject Transfer Calibration Scheme for Improving Feedback Performance of Sensorimotor Rhythm-Based BCI Rehabilitation.
Article in Frontiers in neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Improving inter-session performance via relevant session-transfer for multi-session motor imagery classification.Heliyon · 2024Article
- 2020 International brain-computer interface competition: A review.Frontiers in human neuroscience · 2022Review
- Exploration on neurobiological mechanisms of the central-peripheral-central closed-loop rehabilitation.Frontiers in cellular neuroscience · 2022Review
- A Transfer Learning Algorithm to Reduce Brain-Computer Interface Calibration Time for Long-Term Users.Frontiers in neuroergonomics · 2022Article
- P3-MSDA: Multi-Source Domain Adaptation Network for Dynamic Visual Target Detection.Frontiers in human neuroscience · 2021Article
- A Review on Signal Processing Approaches to Reduce Calibration Time in EEG-Based Brain-Computer Interface.Frontiers in neuroscience · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The Brain Computer Interface (BCI) system is a typical neurophysiological application which helps paralyzed patients with human-machine communication. Stroke patients with motor disabilities are able to perform BCI tasks for clinical rehabilitation. This paper proposes an effective scheme of transfer calibration for BCI rehabilitation. The inter- and intra-subject transfer learning approaches can improve the low-precision classification performance for experimental feedback. The results imply that the systematical scheme is positive in increasing the confidence of voluntary training for stroke patients. In addition, it also reduces the time consumption of classifier calibration.
Indexed as
Identifiers
What OpenQuestion holds
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.