In cardiac MRI we have to deal with both cardiac and respiratory motion. Nowadays, breath holds and the use of external devices such as ECG are clinical practice to deal with this motion. However, not all patients can hold their breath and external devices are error-prone. Therefore, self-gating techniques have been developed to extract the respiration and cardiac signal from the k-space data itself. Many of those require various pre- and post-processing steps like filtering or averaging and lack robustness. Here, we present a novel and robust, yet easy to implement self-gating approach based on Singular Spectrum Analysis.