摘要：Classification of multichannel EEG recordings during motor imagination has been exploited successfully for brain-computer
interfaces (BCI). In this paper, we consider EEG signals as the outputs of a networked dynamical system (the cortex), and exploit
synchronization features from the dynamical system for classification. Herein, we also propose a new framework for
learning optimal filters automatically from the data, by employing a Fisher ratio criterion. Experimental evaluations comparing the
proposed dynamical system features with the CSP and the AR features reveal their competitive performance during
classification. Results also show the benefits of employing the spatial and the temporal filters optimized using the proposed learning approach.
