ArticleFrontiers in neuroscience2023
Motor imagery classification method based on relative wavelet packet entropy brain network and improved lasso.
Article in Frontiers in neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- The Application of Entropy in Motor Imagery Paradigms of Brain-Computer Interfaces.Brain sciences · 2025Review
- Improved filter bank common spatial pattern algorithm based on the sparrow search algorithm.Frontiers in human neuroscience · 2025Article
- Exploring Feature Selection and Classification Techniques to Improve the Performance of an Electroencephalography-Based Motor Imagery Brain-Computer Interface System.Sensors (Basel, Switzerland) · 2024Article
- Classification of cognitive impairment in older adults based on brain functional state measurement data via hierarchical clustering analysis.Frontiers in aging neuroscience · 2023Article
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Authors and funding
6 authors.
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Abstract
Motor imagery (MI) electroencephalogram (EEG) signals have a low signal-to-noise ratio, which brings challenges in feature extraction and feature selection with high classification accuracy. In this study, we proposed an approach that combined an improved lasso with relief-f to extract the wavelet packet entropy features and the topological features of the brain function network. For signal denoising and channel filtering, raw MI EEG was filtered based on an R
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