  Dealing with irregular domains, graph signal processing (GSP) has attracted
much attention especially in brain imaging analysis. Motor imagery tasks are
extensively utilized in brain-computer interface (BCI) systems that perform
classification using features extracted from Electroencephalogram signals. In
this paper, a GSP-based approach is presented for two-class motor imagery tasks
classification. The proposed method exploits simultaneous diagonalization of
two matrices that quantify the covariance structure of graph spectral
representation of data from each class, providing a discriminative subspace
where distinctive features are extracted from the data. The performance of the
proposed method was evaluated on Dataset IVa from BCI Competition III.
Experimental results show that the proposed method outperforms two
state-of-the-art alternative methods.
