Connectivity steered graph Fourier transform for motor imagery BCI decoding.

OBJECTIVE: Graph signal processing concepts are exploited for brain activity decoding and particularly the detection and recognition of a Motor Imagery (MI) movement. A novel signal analytic technique that combines Graph Fourier Transform (GFT) with estimates of cross-frequency coupling and discriminative learning is introduced as a means to recover the subject's intention from the multichannel signal.
 Approach. Adopting a multi-view perspective, based on the popular concept of co-existing and interacting brain rhythms, a multilayer network model is first built from empirical data and its connectivity graph is used to derive the GFT-basis. A personalized decoding scheme supporting a binary decision, either "left vs right" or "rest vs MI", is crafted from a small set of training trials. Electroencephalographic (EEG) activity from 12 volunteers recorded during two randomly alternating, externally cued, MI tasks (clenching either left or right fist) and a rest condition is used to introduce and validate our methodology. In addition, the introduced methodology was further validated based on dataset IVa of BCI III competition. 
 Main results. Our GFT-domain decoding scheme achieves nearly optimal performance and proves superior to alternative techniques that are very popular in the field. 
 Significance. At a conceptual level, our work suggests a fruitful way to introduce network neuroscience in BCI research. At a more practical level, it is characterized by efficiency. Training is realized using a small number of exemplar trials and decoding requires very simple operations that leaves room for real-time implementation.&#13.