ArticleDiagnostics (Basel, Switzerland)2023
KCS-FCnet: Kernel Cross-Spectral Functional Connectivity Network for EEG-Based Motor Imagery Classification.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Gaussian Connectivity-Driven EEG Imaging for Deep Learning-Based Motor Imagery Classification.Sensors (Basel, Switzerland) · 2025Article
- Takens-Based Kernel Transfer Entropy Connectivity Network for Motor Imagery Classification.Sensors (Basel, Switzerland) · 2025Article
- A new approach for neural decoding by inspiring of hyperdimensional computing for implantable intra-cortical BMIs.Scientific reports · 2024Article
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Authors and funding
3 authors.
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Abstract
This paper uses EEG data to introduce an approach for classifying right and left-hand classes in Motor Imagery (MI) tasks. The Kernel Cross-Spectral Functional Connectivity Network (KCS-FCnet) method addresses these limitations by providing richer spatial-temporal-spectral feature maps, a simpler architecture, and a more interpretable approach for EEG-driven MI discrimination. In particular, KCS-FCnet uses a single 1D-convolutional-based neural network to extract temporal-frequency features from raw EEG data and a cross-spectral Gaussian kernel connectivity layer to model channel functional relationships. As a result, the functional connectivity feature map reduces the number of parameters, improving interpretability by extracting meaningful patterns related to MI tasks. These patterns can be adapted to the subject's unique characteristics. The validation results prove that introducing KCS-FCnet shallow architecture is a promising approach for EEG-based MI classification with the potential for real-world use in brain-computer interface systems.
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