TY - JOUR T1 - EEG-based Motor Imagery Decoding via Graph Signal Processing on Learned Graphs JF - bioRxiv DO - 10.1101/2022.08.13.503836 SP - 2022.08.13.503836 AU - Maliheh Miri AU - Vahid Abootalebi AU - Hamid Saeedi-Sourck AU - Hamid Behjat Y1 - 2022/01/01 UR - http://biorxiv.org/content/early/2022/08/16/2022.08.13.503836.abstract N2 - Objective This paper presents a graph signal processing (GSP)-based approach for decoding two-class motor imagery EEG data via deriving task-specific discriminative features.Methods First, a graph learning (GL) method is used to learn subject-specific graphs from EEG signals. Second, by diagonalizing the normalized Laplacian matrix of each subject’s graph, an orthonormal basis is obtained using which the graph Fourier transform (GFT) of the EEG signals is computed. Third, the GFT coefficients are mapped into a discriminative subspace for differentiating two class data using a projection matrix obtained by the Fukunaga-Koontz transform (FKT). Finally, an SVM classifier is trained and tested on the variance of the resulting features to differentiate motor imagery classes.Results The proposed method is evaluated on Dataset IVa of the BCI Competition III and its performance is compared to i) using features extracted on a graph constructed by Pearson correlation coefficients and ii) three state-of-the-art alternative methods.Conclusion Experimental results indicate the superiority of the proposed method over alternative methods, reflecting the added benefit of integrating elements from GL, GSP and FKT.Significance The proposed method and results underpin the importance of integrating spatial and temporal characteristics of EEG signals in extracting features that can more powerfully differentiate motor imagery classes.Competing Interest StatementThe authors have declared no competing interest. ER -