Pembersihan Artefak EOG dari Sinyal EEG menggunakan Denoising Autoencoder

HASBIAN FAUZY PERDHANA, HASBALLAH ZAKARIA

Sari


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:

PDF

Referensi


Bank, D., Koenigstein, N., & Giryes, R. (2020). Autoencoders. http://arxiv.org/abs/2003.05991

Elbert, T., Lutzenberger, W., Rockstroh, B., & Birbaumer, N. (1985). Removal of ocular artifacts from the EEG - A biophysical approach to the EOG. Electroencephalography and Clinical Neurophysiology, 60(5), 455–463. https://doi.org/10.1016/0013-4694(85)91020-X

Fotiadou, E., & Vullings, R. (2020). Multi-Channel Fetal ECG Denoising With Deep Convolutional Neural Networks. Frontiers in Pediatrics, 8. https://doi.org/10.3389/fped.2020.00508

Hadiyoso, S., Zakaria, H., Mengko, T. L. E. R., & Ong, P. A. (2021). Preliminary Study of EEG Characterization Using Power Spectral Analysis in Post-stroke Patients with Cognitive Impairment (pp. 579–592). https://doi.org/10.1007/978-981-33-6926-9_51

Hartmann, K. G., Schirrmeister, R. T., & Ball, T. (2018). EEG-GAN: Generative adversarial networks for electroencephalograhic (EEG) brain signals. http://arxiv.org/abs/1806.01875

Hu, J., Wang, C. sheng, Wu, M., Du, Y. xiao, He, Y., & She, J. (2015). Removal of EOG and EMG artifacts from EEG using combination of functional link neural network and adaptive neural fuzzy inference system. Neurocomputing, 151(P1), 278–287. https://doi.org/10.1016/j.neucom.2014.09.040

Hwang, S., Hong, K., Son, G., & Byun, H. (2019). EZSL-GAN: EEG-based Zero-Shot Learning approach using a Generative Adversarial Network. 7th International Winter Conference on Brain-Computer Interface, BCI 2019, (pp. 1–4). https://doi.org/10.1109/IWW-BCI.2019.8737322

Islam, M. K., Rastegarnia, A., & Yang, Z. (2016). Les méthodes de détection et de rejet d’artefact de l’EEG de scalp : revue de littérature. Neurophysiologie Clinique, 46(4–5), 287–305. https://doi.org/10.1016/j.neucli.2016.07.002

Issa, M. F., & Juhasz, Z. (2019). Improved EOG artifact removal using wavelet enhanced independent component analysis. Brain Sciences, 9(12). https://doi.org/10.3390/brainsci9120355

Jiang, X., Bian, G. Bin, & Tian, Z. (2019). Removal of artifacts from EEG signals: A review. Sensors (Switzerland), 19(5), 1–18. https://doi.org/10.3390/s19050987

Jiao, Y., Deng, Y., Luo, Y., & Lu, B. L. (2020). Driver sleepiness detection from EEG and EOG signals using GAN and LSTM networks. Neurocomputing, 408, 100–111. https://doi.org/10.1016/j.neucom.2019.05.108

Klados, M. A., & Bamidis, P. D. (2016). A semi-simulated EEG/EOG dataset for the comparison of EOG artifact rejection techniques. Data in Brief, 8, 1004–1006. https://doi.org/10.1016/j.dib.2016.06.032

Lee, D., Choi, S., & Kim, H.-J. (2018). Performance evaluation of image denoising developed using convolutional denoising autoencoders in chest radiography. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 884, 97–104. https://doi.org/10.1016/j.nima.2017.12.050

Luo, T. J., Fan, Y., Chen, L., Guo, G., & Zhou, C. (2020). EEG Signal Reconstruction Using a Generative Adversarial Network With Wasserstein Distance and Temporal-Spatial-Frequency Loss. Frontiers in Neuroinformatics, 14. https://doi.org/10.3389/fninf.2020.00015

Morley, A., Hill, L., & Kaditis, A. G. (2016). 10-20 System EEG Placement. European Respiratory Society, 34. http://en.wikipedia.org/wiki/10-20_system_%28EEG%29

Nejedly, P., Cimbalnik, J., Klimes, P., Plesinger, F., Halamek, J., Kremen, V., Viscor, I., Brinkmann, B. H., Pail, M., Brazdil, M., Worrell, G., & Jurak, P. (2019). Intracerebral EEG Artifact Identification Using Convolutional Neural Networks. Neuroinformatics, 17(2), 225–234. https://doi.org/10.1007/s12021-018-9397-6

Saba-Sadiya, S., Chantland, E., Alhanai, T., Liu, T., & Ghassemi, M. M. (2021). Unsupervised EEG Artifact Detection and Correction. Frontiers in Digital Health, 2(January), 1–11. https://doi.org/10.3389/fdgth.2020.608920

Saini, M., Payal, & Satija, U. (2020). An Effective and Robust Framework for Ocular Artifact Removal From Single-Channel EEG Signal Based on Variational Mode Decomposition. IEEE Sensors Journal, 20(1), 369–376. https://doi.org/10.1109/JSEN.2019.2942153

Winkler, I., Debener, S., Muller, K.-R., & Tangermann, M. (2015). On the influence of high-pass filtering on ICA-based artifact reduction in EEG-ERP. 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), (pp. 4101–4105). https://doi.org/10.1109/EMBC.2015.7319296

Winkler, I., Haufe, S., & Tangermann, M. (2011). Automatic Classification of Artifactual ICA-Components for Artifact Removal in EEG Signals. Behavioral and Brain Functions, 7(1), 30. https://doi.org/10.1186/1744-9081-7-30




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

Refbacks

  • Saat ini tidak ada refbacks.


_______________________________________________________________________________________________________________________

ISSN (cetak) : 2338-8323 | ISSN (elektronik) : 2459-9638

diterbitkan oleh :

Teknik Elektro Institut Teknologi Nasional Bandung

Alamat : Gedung 20 Jl. PHH. Mustofa 23 Bandung 40124

Kontak : Tel. 7272215 (ext. 206) Fax. 7202892

Surat Elektronik : jte.itenas@itenas.ac.id________________________________________________________________________________________________________________________

Statistik Pengunjung

Free counters!

Web Analytics Made Easy - StatCounter

Lihat Statistik Jurnal

Jurnal ini terlisensi oleh Creative Commons Attribution-ShareAlike 4.0 International License.

Creative Commons License