  Common Spatial Patterns (CSP) is a feature extraction algorithm widely used
in Brain-Computer Interface (BCI) Systems for detecting Event-Related
Potentials (ERPs) in multi-channel magneto/electroencephalography (MEG/EEG)
time series data. In this article, we develop and apply a CSP algorithm to the
problem of identifying whether a given epoch of multi-detector Gravitational
Wave (GW) strains contains coalescenses. Paired with Signal Processing
techniques and a Logistic Regression classifier, we find that our pipeline is
correctly able to detect 76 out of 82 confident events from Gravitational Wave
Transient Catalog, using H1 and L1 strains, with a classification score of
$93.72 \pm 0.04\%$ using $10 \times 5$ cross validation. The false negative
events were: GW170817-v3, GW191219 163120-v1, GW200115 042309-v2, GW200210
092254-v1, GW200220 061928-v1, and GW200322 091133-v1.
