Near Infrared Spectroscopy is a method that measures the brain’s haemodynamic response. It is of interest in brain-computer interfaces where haemodynamic patterns in motor tasks are exploited to detect movement. However, the NIRS signal is usually corrupted with background biological processes, some of which are periodic or quasi-periodic in nature. Singular spectrum analysis (SSA) is a time-series decomposition method which separates a signal into a trend, oscillatory components and noise with minimal prior assumptions about their nature. Due to the frequency spectrum overlap of the movement response and of background processes such as Mayer waves, spectral filters are usually suboptimal. In this study, we perform SSA both in an online and a block fashion resulting in the removal of periodic components and in increased classification performance. Our study indicates that SSA is a practical method that can replace spectral filtering and is evaluated on healthy participants and patients with tetraplegia.