The Impact of Architectural Components on Deep Learning-Based BCG Artifact Removal
8th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, ICHORA 2026, Ankara, Türkiye, 21 - 23 Mayıs 2026, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Doi Numarası: 10.1109/ichora69329.2026.11537040
- Basıldığı Şehir: Ankara
- Basıldığı Ülke: Türkiye
- Anahtar Kelimeler: attention layer, autoencoder, BCG artifact removal, BiLSTM, deep learning
- Ankara Üniversitesi Adresli: Hayır
Özet
The multimodal EEG-fMRI structure combines the advantages of EEG's high temporal resolution with the high spatial resolution of fMRI. However, BCG artifacts that arise due to the effect of the magnetic field during simultaneous recording distort EEG data, adversely affecting data analysis. In order to accurately interpret the data, these artifacts must be removed from the EEG recordings. Although classical artifact removal methods are widely used, they have many limitations. In recent years, deep learning-based methods have proven to be highly successful in eliminating BCG artifacts and offer an alternative to traditional approaches. The foundation of this success lies not only in the amount of data and computational power but also in the proper design of model architecture. In this study, an autoencoder-based model is proposed for the automatic removal of BCG artifacts. The effects of essential architectural components and attention mechanisms used in autoencoder-based models on model performance are examined. The advantages, disadvantages, and use-case scenarios of different components are discussed comparatively. This study highlights the critical role of architectural design in the success of the model.