  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.
