Functional connectivity is often used in brain-computer interface studies as well as other neuroscience fields as a feature extraction method. In the functional connectivity using electroencephalography (EEG), connectivity patterns are extracted by a dependency matrix showing the coherence between electrode pairs. A variety of dependence measures can be used to calculate this matrix. In this study, a total of 15 coherence measures were analyzed comparatively in terms of computation time, accuracy and statistical significance in discriminating motor/motor imagery activities. As dependence measures, in addition to methods used in the literature for brain connectivity, five other methods used as contrast function in independent component analysis and two novel mutual information calculators proposed in this study were evaluated. Furthermore, a novel hierarchical clustering based statistical test procedure was also proposed for motor/motor imagery activity comparison, along with a similar statistical significance test applied on data from 103 subjects on four different activity types. In experiments on real data set, significance results of dependence measures differed according to the type of activity and time window duration of activity signals. Considering both computation time and accuracy performances on synthetic data, a number of methods with high statistical significance and different dependence characteristics were identified as feasible for a connectivity based brain-computer interface.