Before revision:  These aspects challenge the assessment of functionally relevant features which are of special importance in closed-loop applications as ,  e.g., in the field of rehabilitation. We propose a data-driven method to identify groups of reliable and functionally relevant oscillatory components computed by a spatial filtering approach. Therefore, we initially embrace the variability of decoding models in a large configuration space before condensing information by density-based clustering of components' functional signatures. Exemplified for a hand force task with  rich within-trial structure , the approach was evaluated on EEG data of 18 healthy subjects .
After revision:  These aspects challenge the assessment of functionally relevant features which are of special importance in closed-loop applications as , e.g., ~neurotechnological applications. Fostering component introspection, we propose to embrace this variability by condensing the functional signatures of a large set of oscillatory components into homogeneous clusters, each representing specific within-trial envelope dynamics. The proposed method is exemplified by and evaluated on a complex hand force task with rich within-trial structure , the approach was evaluated on EEG data of 18 healthy subjects .