ArticlePeerJ. Computer science2021
The classification of motor imagery response: an accuracy enhancement through the ensemble of random subspace k-NN.
Article in PeerJ. Computer science, 2021. 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
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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.
- Diagnosis of Schizophrenia Using EEG Sensor Data: A Novel Approach with Automated Log Energy-Based Empirical Wavelet Reconstruction and Cepstral Features.Sensors (Basel, Switzerland) · 2024Article
- Design of feature selection algorithm for high-dimensional network data based on supervised discriminant projection.PeerJ. Computer science · 2023Article
- A novel framework for classification of two-class motor imagery EEG signals using logistic regression classification algorithm.PloS one · 2023Article
- Survey on the research direction of EEG-based signal processing.Frontiers in neuroscience · 2023Review
- Stockwell transform and semi-supervised feature selection from deep features for classification of BCI signals.Scientific reports · 2022Article
- Diagnosis of hearing deficiency using EEG based AEP signals: CWT and improved-VGG16 pipeline.PeerJ. Computer science · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Brain-computer interface (BCI) is a viable alternative communication strategy for patients of neurological disorders as it facilitates the translation of human intent into device commands. The performance of BCIs primarily depends on the efficacy of the feature extraction and feature selection techniques, as well as the classification algorithms employed. More often than not, high dimensional feature set contains redundant features that may degrade a given classifier's performance. In the present investigation, an ensemble learning-based classification algorithm, namely random subspace
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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.