Covariation Informed Graph Slepians for Motor Imagery Decoding

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 29; pp. 340 - 349
• Main Authors: Georgiadis, Kostas, Adamos, Dimitrios A., Nikolopoulos, Spiros, Laskaris, Nikos, Kompatsiaris, Ioannis
• Format: Journal Article
• Language: English
• Published: United States IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: BCI; Computational neuroscience; Data analysis; Data processing; Decoding; Domains; EEG; Electroencephalography; Empirical analysis; Fourier analysis; Fourier transforms; Graph signal processing (GSP); Imagery; Machine learning; Mental task performance; motor imagery; Sensor arrays; Sensors; Signal processing; Spatial filtering; Spatial filters; Support vector machines; Topology
• ISSN: 1534-4320; 1558-0210; 1558-0210
• DOI: 10.1109/TNSRE.2021.3049998

Graph signal processing (GSP) provides signal analytic tools for data defined in irregular domains, as is the case of non-invasive electroencephalography (EEG). In this work, the recently introduced technique of Graph Slepian functions is exploited for the robust decoding of motor imagery (MI) brain...