Brain-Computer Interface (BCI) is applied in the study of different cognitive processes or clinical conditions as enhancing cognitive skills, motor rehabilitation, and control. However, many approaches focus on using a robust classifier instead of providing a better feature space. This work develops a feature representation methodology through the kernel canonical correlation analysis to reveal nonlinear relations between filter-banked common spatial patterns (CSP) extracted. Our approach reveals nonlinear relations between ranked filter-banked multi-class CSP features and the labels in a finite-dimensional canonical space. We tested the performance of our methodology on the BCI Competition IV dataset 2a. The introduced feature representation using a classic linear SVM achieves accuracy rates competitive with the state-of-the-art BCI strategies. Besides, the processing pipeline allows identifying the spatial and spectral features driven by the underlying brain activity and best modeling the motor imagery intentions.Clinical relevance- This BCI strategy assesses the nonlinear relationships between time series to improve the interpretation of brain electrical activity, taking into account the spatial and spectral features driven by the underlying brain dynamic.