  Data-driven spatial filtering algorithms optimize scores such as the contrast
between two conditions to extract oscillatory brain signal components. Most
machine learning approaches for filter estimation, however, disregard
within-trial temporal dynamics and are extremely sensitive to changes in
training data and involved hyperparameters. This leads to highly variable
solutions and impedes the selection of a suitable candidate for,
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 a rich within-trial structure. Based on electroencephalography 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, it could be utilized for a wide range of
neurotechnological applications, e.g., to select and monitor functionally
relevant features for brain-computer interface protocols in stroke
rehabilitation.
