EEG Signal Denoising Using pix2pix GAN: Enhancing Neurological Data Analysis
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866917842342903808 |
|---|---|
| author | Wang, Haoyi Chen, Xufang Yang, Yue Zhou, Kewei Lv, Meining Wang, Dongrui Zhang, Wenjie |
| author_facet | Wang, Haoyi Chen, Xufang Yang, Yue Zhou, Kewei Lv, Meining Wang, Dongrui Zhang, Wenjie |
| contents | Electroencephalography (EEG) is essential in neuroscience and clinical practice, yet it suffers from physiological artifacts, particularly electromyography (EMG), which distort signals. We propose a deep learning model using pix2pixGAN to remove such noise and generate reliable EEG signals. Leveraging the EEGdenoiseNet dataset, we created synthetic datasets with controlled EMG noise levels for model training and testing across a signal-to-noise ratio (SNR) from -7 to 2. Our evaluation metrics included RRMSE and Pearson's CC, assessing both time and frequency domains, and compared our model with others. The pix2pixGAN model excelled, especially under high noise conditions, showing significant improvements in lower RRMSE and higher CC values. This demonstrates the model's superior accuracy and stability in purifying EEG signals, offering a robust solution for EEG analysis challenges and advancing clinical and neuroscience applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_13288 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | EEG Signal Denoising Using pix2pix GAN: Enhancing Neurological Data Analysis Wang, Haoyi Chen, Xufang Yang, Yue Zhou, Kewei Lv, Meining Wang, Dongrui Zhang, Wenjie Signal Processing I.4.9 Electroencephalography (EEG) is essential in neuroscience and clinical practice, yet it suffers from physiological artifacts, particularly electromyography (EMG), which distort signals. We propose a deep learning model using pix2pixGAN to remove such noise and generate reliable EEG signals. Leveraging the EEGdenoiseNet dataset, we created synthetic datasets with controlled EMG noise levels for model training and testing across a signal-to-noise ratio (SNR) from -7 to 2. Our evaluation metrics included RRMSE and Pearson's CC, assessing both time and frequency domains, and compared our model with others. The pix2pixGAN model excelled, especially under high noise conditions, showing significant improvements in lower RRMSE and higher CC values. This demonstrates the model's superior accuracy and stability in purifying EEG signals, offering a robust solution for EEG analysis challenges and advancing clinical and neuroscience applications. |
| title | EEG Signal Denoising Using pix2pix GAN: Enhancing Neurological Data Analysis |
| topic | Signal Processing I.4.9 |
| url | https://arxiv.org/abs/2411.13288 |