On the Spatiotemporal Characteristics of Class-Discriminating Functional Networks.

Abstract

We target the problem of identifying brain's functional networks that are discriminatory across classes of tasks, using data obtained through electroencephalography (EEG). A three-step framework is presented. First, the EEG data is segmented to identify the intervals during which cortical functional networks remain quasi-stationary. Second, these functional networks are spatially localized in the cortex. Finally, by employing the proposed discriminative Boolean matrix factorization (DBMF) algorithm, functional networks that are most recurrent in one class of tasks, but are least recurrent in the other are identified. The DBMF algorithm is capable of providing the spatial maps of the discriminative functional networks as well as information about their dynamic occurrence over time. The framework is applied to experimental EEG data, recorded during a motor task. The results show that the proposed framework identifies several parietal/motor functional networks as being the most discriminatory for motor execution trials from non-execution trials.