Before revision:  Exemplified for a hand force task with  rich within-trial structure , the approach was evaluated on EEG data of 18 healthy subjects . We found that functional characteristics of single componentsare revealed by distinct temporal dynamics of their event-related power changes. Based on a within-subject analysis, our clustering revealed seven groups of homogeneous envelope dynamics on average. To support introspection by practitioners, we provide a set of metrics to characterize and validate single clusterings. We show that identified clusters contain components of strictly confined frequency ranges, dominated by the alpha and beta band. Our method is applicable to any spatial filtering algorithm .
After revision:  Exemplified for a hand force task with rich within-trial structure , the approach was evaluated on EEG data of 18 healthy subjects , we found that the components' distinct temporal envelope dynamics are highly subject-specific. On average, we obtained seven clusters per subject, which were strictly confined regarding their underlying frequency bands. As the analysis method is not limited to a specific spatial filtering algorithm .