Enhanced DEMUCS-based algorithm is utilized for EEG artifact suppression

Zhengji Li; Li Chen; Yating Sun; Jiacheng Xie; Haowei Wang; Dan Yang; Shiqing Chen; Xin Wei; Yuhong Gao

doi:10.1117/12.3025903

8 April 2024 Enhanced DEMUCS-based algorithm is utilized for EEG artifact suppression

Zhengji Li, Li Chen, Yating Sun, Jiacheng Xie, Haowei Wang, Dan Yang, Shiqing Chen, Xin Wei, Yuhong Gao

Proceedings Volume 13090, International Conference on Computer Application and Information Security (ICCAIS 2023); 1309027 (2024) https://doi.org/10.1117/12.3025903
Event: International Conference on Computer Application and Information Security (ICCAIS 2023), 2023, Wuhan, China

Abstract

Electroencephalography (EEG) signals have significant implications in the fields of clinical medicine, brain research, and neurological disease research. During the collection process, the EEG signals are often interfered with by various factors, such as muscle movement and ambient noise, which cause the decline of signal quality and have a significant impact on subsequent EEG analysis. In this study, firstly a dataset with 50380 EEG signals has been built by mixing clean EEG signals and electromyographic (EMG) artifact signals. Furthermore, an improved DEMUCS method, which is a framework for a speech enhancement model, is incorporated into the process of suppressing EEG signals. Additionally, the encoder and decoder layers, as well as the size of the convolutional kernel, are optimized. The experimental results indicate that the improved DEMUCS model demonstrates excellent performance in artifact removal. Compared with other methods, the algorithm proposed in this article achieved MSE (Mean Square Error) of 0.0699 on test data, showing a significant improvement. This study provides new solutions for the field of EEG signal processing, with the potential to improve the quality of EEG signal data and promote neuroscience and medical research.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhengji Li, Li Chen, Yating Sun, Jiacheng Xie, Haowei Wang, Dan Yang, Shiqing Chen, Xin Wei, and Yuhong Gao "Enhanced DEMUCS-based algorithm is utilized for EEG artifact suppression", Proc. SPIE 13090, International Conference on Computer Application and Information Security (ICCAIS 2023), 1309027 (8 April 2024); https://doi.org/10.1117/12.3025903

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KEYWORDS

Electroencephalography

Signal processing

Education and training

Signal to noise ratio

Brain diseases

Electromyography

Mental disorders

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