Pembersihan Artefak EOG dari Sinyal EEG menggunakan Denoising Autoencoder

HASBIAN FAUZY PERDHANA, HASBALLAH ZAKARIA

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ABSTRAK

Elektroensefalografi (EEG) adalah teknik perekaman yang merekam aktivitas elektrik pada otak menggunakan elektroda yang ditempelkan pada kulit kepala. Artefak elektrookulografi (EOG) adalah salah satu artefak yang kerap muncul pada perekaman EEG dikarenakan pergerakan mata dan menyebabkan sinyal EEG berubah bentuk. Untuk membersihkan EEG, artefak perlu dibuang dengan tetap menjaga informasi penting dari EEG. Pada penelitian ini kami mendeteksi artefak EOG menggunakan Independent Component Analysis (ICA) dan deteksi puncak, dan untuk rekonstruksi sinyal EEG kami menggunakan Denoising Autoencoder (DAE). Pada penelitian ini kami meneliti model DAE apakah dapat merekonstruksi sinyal EEG dari artefak EOG. Metode pendeteksian artefak mendapatkan 85% sensitivitas dan 83% Positive Predictive Value (PPV) pada dataset sekunder dan 82% sensitivitas pada dataset primer. Model DAE dilatih dengan validasi silang 10 lipat dan mendapatkan rerata mean squared error (MSE) 0,007±0,008. Penelitian ini membuktikan kemampuan DAE untuk merekonstruksi sinyal EEG dengan
masukan segmen sinyal EEG terkontaminasi artefak EOG.

Kata kunci: EEG, Artefak EOG, Denoising Autoencoder

 

ABSTRACT

The Electroencephalography (EEG) is a recording technique to record electrical activity on the brain using electrodes attached to the head scalp. Electrooculography (EOG) 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 need to remove artifacts while conserving EEG information. In this research, we detect EOG artifactual signal using Independent Component Analysis (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 cross-validation 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.

Keywords: EEG, Eye movement artifact, Denoising Autoencoder


Kata Kunci


EEG; Artefak EOG; Denoising Autoencoder

Teks Lengkap:

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Referensi


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DOI: https://doi.org/10.26760/elkomika.v10i3.639

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ISSN (print) : 2338-8323 | ISSN (electronic) : 2459-9638

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Contact: +627272215 (ext. 206)

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