Connectome spectrum electromagnetic tomography: a method to reconstruct electrical brain source-networks at high-spatial resolution

Rué-Queralt, Joan; Fluhr, Hugo; Tourbier, Sébastien; Gomez, Yasser Aleman; Pascucci, David; Yerly, Jérôme; Glomb, Katharina; Plomp, Gijs; Hagmann, Patric

doi:10.1101/2022.07.26.501544

Cited by 2 publications

(1 citation statement)

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“…For EEG data, a number of studies have shown promising results, namely, for dimensionality reduction (Tanaka et al, 2016; Kalantar et al, 2017), signal denoising (Cattai et al, 2021), and motor imagery (MI) decoding (Georgiadis et al, 2021; Cattai et al, 2022). Moreover, diffusion MRI-derived connectome harmonics have been used to characterize EEG data within a GSP setting, for tracking fast spatio-temporal cortical dynamics (Glomb et al, 2020b), their sparse representation (Rué-Queralt et al, 2021), and for source reconstruction (Rué-Queralt et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Spectral Representation of EEG Data using Learned Graphs with Application to Motor Imagery Decoding

Miri

Abootalebi

Saeedi-Sourck

et al. 2022

Preprint

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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 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.

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