In the field of Brain-Computer Interfaces (BCI), robust methods for the decoding of continuous brain states are of great interest as new application fields are arising. When capturing brain activity by an elec-troencephalogram (EEG), the Source Power Comodulation (SPoC) algorithm allows to compute spatial filters for the decoding of a continuous variable. However, dealing with high-dimensional EEG data that suffer from low signal-to-noise ratio, the method reveals instabilities for small training data sets and is prone to overfitting. In this paper, we introduce a framework for applying Tikhonov regularization to the SPoC approach in order to restrict the solution space of filters. Our findings show that an additional trace normalization of the included covariance matrices is a necessary prerequisite to tune the sensitivity of the resulting algorithm. In an offline analysis with data from N=18 subjects, the introduced trace normalized and Tihonov regularized SPoC variant (NTR-SPoC) outperforms the standard SPoC method for the majority of individuals. With this proof-of-concept study, a generalizable regularization framework for SPoC has been established which allows to implement a variety of different regularization strategies in the future.