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

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 ar...