抽象的な

Automatic EEG Artifact Removal Using Blind-Source Separation Methods

John Beckwith

Electroencephalograms are often contaminated with artifacts. In this project, we explore two similar Blind-Source Separation methods that have potential for automated artifact removal. These methods are known as Canonical Correlation Analysis (CCA) and Independent Component Analysis (ICA). We successfully implement muscle artifact removal via CCA, and we also train a simple artificial neural network that can identify eye movement artifacts for removal via ICA with about 92% accuracy.