ArticleFrontiers in human neuroscience2020
Random Subspace Ensemble Learning for Functional Near-Infrared Spectroscopy Brain-Computer Interfaces.
Article in Frontiers in human neuroscience, 2020. 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
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
9 citing papers in PubMed.
- Machine learning model optimization for flood susceptibility zonation over the Kosi megafan, Himalayan foreland basin, India.Scientific reports · 2025Article
- Application of a novel nested ensemble algorithm in predicting motor function recovery in patients with traumatic cervical spinal cord injury.Scientific reports · 2024Article
- Feasibility of local interpretable model-agnostic explanations (LIME) algorithm as an effective and interpretable feature selection method: comparative fNIRS study.Biomedical engineering letters · 2023Article
- From brain to worksite: the role of fNIRS in cognitive studies and worker safety.Frontiers in public health · 2023Review
- [Neurofeedback technology based on functional near infrared spectroscopy imaging and its applications].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2022Article
- Applying antagonistic activation pattern to the single-trial classification of mental arithmetic.Heliyon · 2022Article
- [Progress of classification algorithms for motor imagery electroencephalogram signals].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2021Article
- Systemic Review on Transcranial Electrical Stimulation Parameters and EEG/fNIRS Features for Brain Diseases.Frontiers in neuroscience · 2021Review
- Analysis of Human Gait Using Hybrid EEG-fNIRS-Based BCI System: A Review.Frontiers in human neuroscience · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The feasibility of the random subspace ensemble learning method was explored to improve the performance of functional near-infrared spectroscopy-based brain-computer interfaces (fNIRS-BCIs). Feature vectors have been constructed using the temporal characteristics of concentration changes in fNIRS chromophores such as mean, slope, and variance to implement fNIRS-BCIs systems. The mean and slope, which are the most popular features in fNIRS-BCIs, were adopted. Linear support vector machine and linear discriminant analysis were employed, respectively, as a single strong learner and multiple weak learners. All features in every channel and available time window were employed to train the strong learner, and the feature subsets were selected at random to train multiple weak learners. It was determined that random subspace ensemble learning is beneficial to enhance the performance of fNIRS-BCIs.
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.