Computer Engineering Commons^™

Evaluation Of Automated Eye Blink Artefact Removal Using Stacked Dense Autoencoder, Matthew Rigney

Dissertations

The presence of artefacts in Electroencephalograph (EEG) signals can have a considerable impact on the information they portray. In this comparative study, the automated removal of eye blink artefacts using the constrained latent representation of a stacked dense autoencoders (SDAE) and comparing its ability to that of the manual independent component analysis (ICA) approach was evaluated. A comparative evaluation of 5 stacked dense autoencoder architectures lead to a chosen architecture for which the ability to automatically detect and remove eye blink artefacts were both statistically and humanistically evaluated. The ability of the stacked dense autoencoder was statistically evaluated with the …