Different mental states result in different synchronizations or desynchronizations between multiple brain regions, and subsequently, electroencephalogram (EEG) connectivity analysis gains increasing attention in brain computer interfaces (BCIs). Conventional connectivity analysis is usually conducted at the scalp-level and in an unsupervised manner. However, due to the volume conduction effect, EEG data suffer from low signal-to-noise ratio and poor spatial resolution. Thus, it is hard to effectively identify the task-related connectivity pattern at the scalp-level using unsupervised method. There exist extensive discriminative spatial filtering methods for different BCI paradigms. However, in conventional spatial filter optimization methods, signal correlations or connectivities are not taken into consideration in the objective functions. To address the issue, in this work, we propose a discriminative connectivity pattern-learning method. In the proposed framework, EEG correlations are used as the features, with which Fisher's ratio objective function is adopted to optimize spatial filters. The proposed method is evaluated with a binary motor imagery EEG dataset. Experimental results show that more connectivity information are maintained with the proposed method, and classification accuracies yielded by the proposed method are comparable to conventional discriminative spatial filtering method.
