EOG ARTIFACT CLEANING IN EEG SIGNAL USING DENOISING AUTOENCODER
Electroencephalography (EEG) is a recording technique to record electrical activity on the brain using electrodes attached to the head scalp. Electrooculography (EOG) artifact is one of the artifacts that are prone to appear on EEG due to eye movement and cause EEG signals to deform. To fix the E...
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| Format: | Theses |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/63549 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Electroencephalography (EEG) is a recording technique to record electrical
activity on the brain using electrodes attached to the head scalp.
Electrooculography (EOG) artifact is one of the artifacts that are prone to appear
on EEG due to eye movement and cause EEG signals to deform. To fix the EEG
signal, we have to remove the artifact while conserving EEG information. In this
research, we detect EOG artifactual signal using ICA and peak detection and used
a generative model Denoising Autoencoder (DAE) to reconstruct clean EEG by
using EEG artifact-corrupted signal. Our artifact detection method scores 85%
sensitivity and 83% Positive Predictive Value on the secondary dataset and 82%
sensitivity on the primary dataset. We train the DAE model with 10-fold crossvalidation and got 0.007 ± 0.008 Mean Squared Error (MSE). We demonstrated
DAE on its ability to generate a clean EEG segment by feeding it contaminated
EEG segment. |
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