In the field of EEG analysis, single-trial analysis is a common approach used to investigate brain activity. However, accurate analysis can be challenging due to various sources of noise and interference. In this paper, we explore the use of spatial filters to optimize single-trial analysis of EEG data. Specifically, we investigate the use of Common Spatial Patterns (CSP) and illustrate its effectiveness in reducing noise and improving signal-to-noise ratio. Our findings suggest that by optimizing spatial filters, accurate single-trial EEG analysis can be achieved.