Using Discriminative Lasso to Detect a Graph Fourier Transform (GFT) Subspace for robust decoding in Motor Imagery BCI.

A novel decoding scheme for motor imagery (MI) brain computer interfaces (BCI's) is introduced based on the GFT concept. It considers the recorded EEG activity as a signal defined over (the graph of) the sensor array. A graph encapsulating the functional covariations emerging during the execution of a specific imagined movement is first defined, from a small training set of relevant trials. The ensemble of graphs signals corresponding to a multi-trial training dataset is then analyzed using a graph-guided decomposition and, based on discriminative Lasso (dLasso), an information-rich GFT subspace is defined. After training, only simple matrix operations are required for transforming the multichannel signal into features to be fed into a classifier that decides whether brain activity conforms with the graph structure associated with the targeted movement. The proposed decoding scheme is evaluated based on two different datasets and found to compare favorably against popular alternatives in the field.