Covariation Informed Graph Slepians for Motor Imagery Decoding

Abstract

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 activity. The particular technique builds over the concept of graph Fourier transform (GFT) and provides additional flexibility in the subsequent data analysis by incorporating domain knowledge. Based on contrastive learning, we introduce an algorithmic pipeline that attains a data driven and subject specific design of Graph Slepian functions. These functions, by incorporating both the topology of the sensor array and the empirical evidence about the differential functional covariation, act as spatial filters that enhance the information conveyed by the multichannel signal and specifically relates to the participant's intention. The proposed technique for crafting Graph Slepians is incorporated in a MI-decoding scheme, in which the informed projections are fed to a support vector machine (SVM) that casts a prediction regarding the type of intended movement. The employed MI-decoder is evaluated based on two publicly available datasets and its superiority against popular alternatives in the field is established. Computational efficiency is listed among its main advantages, since it involves only simple matrix operations, allowing to consider its use in real-time implementations.

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

“…The proposed method using log-penalized graph learning outperforms the three alternative methods, on average across subjects. The GSL method, which is GSP-based [37], shows the best classification accuracy in subject av , whereas the RCSSP method, which utilizes an extension of FKT [15], shows the best accuracy in subject aw . In the other three subjects, the proposed method yields higher classification accuracy.…”

“…In [36], a graph Laplacian denoising method is proposed which improves the separation of MI and resting mental states in MI-BCI EEG data. In [37], an MI decoding approach is proposed that utilizes graph Slepian functions [38], using which discriminative features for classification are extracted from a structural sub-graph of the brain. Inspired by the promising results of the use of GSP in brain imaging applications, we propose a GSP-based method for classification of MI EEG data.…”

“…A number of studies have also shown promising results in applying GSP techniques in classification, dimensionality reduction, and denoising of EEG signals [32][33][34][35][36][37]. In [32], network harmonics of the brain structural connectivity graph are derived for tracking fast spatiotemporal cortical dynamics.…”

Representation learning schemes of multichannel encephalographic signals for Brain Computer Interfaces

“…In both stages the multichannel traces are filtered within the frequency range of interest, i.e. [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25] Hz.…”

“…Secondly, its application in EEG signals remains slightly unexplored. Consequently, the three following hypothesis revolve around the use of various GSP aspects to design robust MI decoding schemes [6]- [8]. Thus, the fifth hypothesis that was examined in this thesis is formed as follows:…”