ArticleComputational intelligence and neuroscience2019
An Optimized Channel Selection Method Based on Multifrequency CSP-Rank for Motor Imagery-Based BCI System.
Article in Computational intelligence and neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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
18 citing papers in PubMed.
- BCI-Based Rehabilitation on the Stroke in Sequela Stage.Neural plasticity · 2020Trial
- A novel channel reduction concept to enhance the classification of motor imagery tasks in brain-computer interface systems.PloS one · 2025Article
- How low can you go: evaluating electrode reduction methods for EEG-based speech imagery BCIs.Frontiers in neuroergonomics · 2025Article
- Performance Improvement with Reduced Number of Channels in Motor Imagery BCI System.Sensors (Basel, Switzerland) · 2024Article
- STGAT-CS: spatio-temporal-graph attention network based channel selection for MI-based BCI.Cognitive neurodynamics · 2024Article
- Transforming Motor Imagery Analysis: A Novel EEG Classification Framework Using AtSiftNet Method.Sensors (Basel, Switzerland) · 2024Article
- Exploring Feature Selection and Classification Techniques to Improve the Performance of an Electroencephalography-Based Motor Imagery Brain-Computer Interface System.Sensors (Basel, Switzerland) · 2024Article
- Motor Imagery Classification Using Effective Channel Selection of Multichannel EEG.Brain sciences · 2024Article
- A novel framework for classification of two-class motor imagery EEG signals using logistic regression classification algorithm.PloS one · 2023Article
- EEG Channel Selection Techniques in Motor Imagery Applications: A Review and New Perspectives.Bioengineering (Basel, Switzerland) · 2022Review
- Coefficient-of-variation-based channel selection with a new testing framework for MI-based BCI.Cognitive neurodynamics · 2022Article
- [Applications, industrial transformation and commercial value of brain-computer interface technology].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2022Article
- [Key technologies for intelligent brain-computer interaction based on magnetoencephalography].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2022Article
- A Comprehensive Review on Critical Issues and Possible Solutions of Motor Imagery Based Electroencephalography Brain-Computer Interface.Sensors (Basel, Switzerland) · 2021Review
- Enhanced Multiple Instance Representation Using Time-Frequency Atoms in Motor Imagery Classification.Frontiers in neuroscience · 2020Article
- Dynamic Modeling of Common Brain Neural Activity in Motor Imagery Tasks.Frontiers in neuroscience · 2020Article
- Selective Feature Generation Method Based on Time Domain Parameters and Correlation Coefficients for Filter-Bank-CSP BCI Systems.Sensors (Basel, Switzerland) · 2019Article
- Evaluating a Semiautonomous Brain-Computer Interface Based on Conformal Geometric Algebra and Artificial Vision.Computational intelligence and neuroscience · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDue to the redundant information contained in multichannel electroencephalogram (EEG) signals, the classification accuracy of brain-computer interface (BCI) systems may deteriorate to a large extent. Channel selection methods can help to remove task-independent electroencephalogram (EEG) signals and hence improve the performance of BCI systems. However, in different frequency bands, brain areas associated with motor imagery are not exactly the same, which will result in the inability of traditional channel selection methods to extract effective EEG features. NEW
methodTo address the above problem, this paper proposes a novel method based on common spatial pattern- (CSP-) rank channel selection for multifrequency band EEG (CSP-R-MF). It combines the multiband signal decomposition filtering and the CSP-rank channel selection methods to select significant channels, and then linear discriminant analysis (LDA) was used to calculate the classification accuracy.
resultsThe results showed that our proposed CSP-R-MF method could significantly improve the average classification accuracy compared with the CSP-rank channel selection method.
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.