Before revision: Data-driven spatial filtering approaches are commonly used to assess rhythmic brain activity from multichannel recordings such as electroencephalography (EEG). As spatial filter estimationis prone to noise, non-stationarity effects and limited data, a high model variability induced by slight changes of, e.g., involved hyperparametersis generally encountered. 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.
After revision: 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 , e.g., in the field of rehabilitation.