Electroencephalographic (EEG) signals are fundamental to neuroscience research and clinical applications such as brain-computer interfaces and neurological disorder diagnosis. These signals are typically a combination of neurological activity and noise, originating from various sources, including physiological artifacts like ocular and muscular movements. Under this setting, we tackle the challenge of distinguishing neurological activity from noise-related sources. We develop a novel EEG denoising model that operates in the frequency domain, leveraging prior knowledge about noise spectral features to adaptively compute optimal convolutional filters for noise separation. The model is trained to learn an empirical relationship connecting the spectral characteristics of noise and noisy signal to a non-linear transformation which allows signal denoising. Performance evaluation on the EEGdenoiseNet dataset shows that the proposed model achieves optimal results according to both temporal and spectral metrics. The model is found to remove physiological artifacts from input EEG data, thus achieving effective EEG denoising. Indeed, the model performance either matches or outperforms that achieved by benchmark models, proving to effectively remove both muscle and ocular artifacts without the need to perform any training on the particular type of artifact.

Gabardi, M., Saibene, A., Gasparini, F., Rizzo, D., Stella, F. (2023). A multi-artifact EEG denoising by frequency-based deep learning. In Proceedings of the 2nd Workshop on Artificial Intelligence for Human-Machine Interaction 2023 co-located with the 22nd International Conference of the Italian Association for Artificial Intelligence (AIxIA 2023) (pp.28-41). CEUR-WS.

A multi-artifact EEG denoising by frequency-based deep learning

Gabardi M.
;
Saibene A.;Gasparini F.;Stella F. A.
2023

Abstract

Electroencephalographic (EEG) signals are fundamental to neuroscience research and clinical applications such as brain-computer interfaces and neurological disorder diagnosis. These signals are typically a combination of neurological activity and noise, originating from various sources, including physiological artifacts like ocular and muscular movements. Under this setting, we tackle the challenge of distinguishing neurological activity from noise-related sources. We develop a novel EEG denoising model that operates in the frequency domain, leveraging prior knowledge about noise spectral features to adaptively compute optimal convolutional filters for noise separation. The model is trained to learn an empirical relationship connecting the spectral characteristics of noise and noisy signal to a non-linear transformation which allows signal denoising. Performance evaluation on the EEGdenoiseNet dataset shows that the proposed model achieves optimal results according to both temporal and spectral metrics. The model is found to remove physiological artifacts from input EEG data, thus achieving effective EEG denoising. Indeed, the model performance either matches or outperforms that achieved by benchmark models, proving to effectively remove both muscle and ocular artifacts without the need to perform any training on the particular type of artifact.
paper
deep learning (DL); EEG denoising; electroencephalography (EEG); frequency-based neural network;
English
2nd Workshop on Artificial Intelligence for Human-Machine Interaction, AIxHMI 2023 - November 6, 2023
2023
Saibene, A; Corchs, S; Fontana, S; Solé-Casals, J
Proceedings of the 2nd Workshop on Artificial Intelligence for Human-Machine Interaction 2023 co-located with the 22nd International Conference of the Italian Association for Artificial Intelligence (AIxIA 2023)
2023
3576
28
41
https://ceur-ws.org/Vol-3576/
open
Gabardi, M., Saibene, A., Gasparini, F., Rizzo, D., Stella, F. (2023). A multi-artifact EEG denoising by frequency-based deep learning. In Proceedings of the 2nd Workshop on Artificial Intelligence for Human-Machine Interaction 2023 co-located with the 22nd International Conference of the Italian Association for Artificial Intelligence (AIxIA 2023) (pp.28-41). CEUR-WS.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/454678
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