ArticleCognitive neurodynamics2021
Novel channel selection method based on position priori weighted permutation entropy and binary gravity search algorithm.
Article in Cognitive neurodynamics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Comparative Effectiveness of Noninvasive Brain-Computer Interface-Based Interventions for Upper Limb Rehabilitation in Poststroke Hemiplegia: Systematic Review and Network Meta-Analysis of Randomized Controlled Trials.Journal of medical Internet research · 2026Pooled it
- BCI-Based Rehabilitation on the Stroke in Sequela Stage.Neural plasticity · 2020Trial
- Motor Intention Quantization for Patients With Disorders of Consciousness by Multimodal BCI Combining Electroencephalography and Functional Near-Infrared Spectroscopy.CNS neuroscience & therapeutics · 2025Article
- The Application of Entropy in Motor Imagery Paradigms of Brain-Computer Interfaces.Brain sciences · 2025Review
- A novel dual-step transfer framework based on domain selection and feature alignment for motor imagery decoding.Cognitive neurodynamics · 2024Article
- STGAT-CS: spatio-temporal-graph attention network based channel selection for MI-based BCI.Cognitive neurodynamics · 2024Article
- Coherence-based channel selection and Riemannian geometry features for magnetoencephalography decoding.Cognitive neurodynamics · 2024Article
- [A review on electroencephalogram based channel selection].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024Review
- Novel channel selection model based on graph convolutional network for motor imagery.Cognitive neurodynamics · 2023Article
- Decoding motor imagery with a simplified distributed dipoles model at source level.Cognitive neurodynamics · 2023Article
- Variance characteristic preserving common spatial pattern for motor imagery BCI.Frontiers in human neuroscience · 2023Article
- Coefficient-of-variation-based channel selection with a new testing framework for MI-based BCI.Cognitive neurodynamics · 2022Article
- Miniaturization for wearable EEG systems: recording hardware and data processing.Biomedical engineering letters · 2022Review
- A novel classification method for EEG-based motor imagery with narrow band spatial filters and deep convolutional neural network.Cognitive neurodynamics · 2022Article
- Coupling feature extraction method of resting state EEG Signals from amnestic mild cognitive impairment with type 2 diabetes mellitus based on weight permutation conditional mutual information.Cognitive neurodynamics · 2021Article
- Evaluation of color modulation in visual P300-speller using new stimulus patterns.Cognitive neurodynamics · 2021Article
- Evolutionary Multitasking-Based Multiobjective Optimization Algorithm for Channel Selection in Hybrid Brain Computer Interfacing Systems.Frontiers in neuroscience · 2021Article
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Authors and funding
6 authors.
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Abstract
Brain-computer interface (BCI) system based on motor imagery (MI) usually adopts multichannel Electroencephalograph (EEG) signal recording method. However, EEG signals recorded in multi-channel mode usually contain many redundant and artifact information. Therefore, selecting a few effective channels from whole channels may be a means to improve the performance of MI-based BCI systems. We proposed a channel evaluation parameter called position priori weight-permutation entropy (PPWPE), which include amplitude information and position information of a channel. According to the order of PPWPE values, we initially selected half of the channels with large PPWPE value from all sampling electrode channels. Then, the binary gravitational search algorithm (BGSA) was used in searching a channel combination that will be used in determining an optimal channel combination. The features were extracted by common spatial pattern (CSP) method from the final selected channels, and the classifier was trained by support vector machine. The PPWPE + BGSA + CSP channel selection method is validated on two data sets. Results showed that the PPWPE + BGSA + CSP method obtained better mean classification accuracy (88.0% vs. 57.5% for Data set 1 and 91.1% vs. 79.4% for Data set 2) than All-C + CSP method. The PPWPE + BGSA + CSP method can achieve higher classification in fewer channels selected. This method has great potential to improve the performance of MI-based BCI systems.
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