KCS-FCnet: Kernel Cross-Spectral Functional Connectivity Network for EEG-Based Motor Imagery Classification

• Published in: Diagnostics (Basel) Vol. 13; no. 6; p. 1122
• Main Authors: García-Murillo, Daniel Guillermo, Álvarez-Meza, Andrés Marino, Castellanos-Dominguez, Cesar German
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.03.2023; MDPI
• Subjects: Algorithms; Analysis; Classification; Cognition & reasoning; cross-spectral distribution; deep learning; EEG; Eidetic imagery; Electroencephalography; functional connectivity; Health aspects; Imagery (Psychology); Information literacy; kernel methods; Motor ability; motor imagery; Neural networks; Neuroimaging; Signal processing; Technology application; Colombia; kernel methods; deep learning; motor imagery; cross-spectral distribution; functional connectivity; EEG
• ISSN: 2075-4418; 2075-4418
• DOI: 10.3390/diagnostics13061122

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.