  The use of time- and frequency-based features has proven effective in the
process of classifying mental tasks in Brain Computer Interfaces (BCIs). Still,
most of those methods provide little insight about the underlying brain
activity and functions. Thus, a better understanding of the mechanisms and
dynamics of brain activity, is necessary in order to obtain useful and
informative features for BCIs. In the present study, the objective is to
investigate the differences in functional connectivity of two motor imagery
tasks, through a partial directed coherence (PDC) analysis, which is a
frequency-domain metric that provides information about directionality in the
interaction between signals recorded at different channels. Four healthy
subjects participated in this study, two mental tasks were evaluated:
Imagination of the movement of the right hand or left hand. We carry out the
differentiation of these tasks through two different approaches: on one hand,
the traditional one based on spectral power; on the other hand, an approach
based on PDC. The results showed that EEG-based PDC analysis provides
additional information and it can potentially improve the feature selection
mainly in the beta frequency band.
