# Application of Common Spatial Patterns in Gravitational Waves Detection

arXiv: https://arxiv.org/abs/2201.04086 · HTML: https://mlsanity.com/paper/2201.04086/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 18 references that are in the map.
- First posted on arXiv 11 Jan 2022.

## Builds on (most cited first, 2 of 2)

- [Eigenvalue and Generalized Eigenvalue Problems: Tutorial](https://mlsanity.com/paper/1903.11240/) (2019)
- [Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation with LIGO Data](https://mlsanity.com/paper/1711.07966/) (2017)

## Abstract (arXiv)

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 ± 0.04% using 10 × 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.