  Electroencephalogram (EEG) has shown a useful approach to produce a
brain-computer interface (BCI). One-dimensional (1-D) EEG signal is yet easily
disturbed by certain artifacts (a.k.a. noise) due to the high temporal
resolution. Thus, it is crucial to remove the noise in received EEG signal.
Recently, deep learning-based EEG signal denoising approaches have achieved
impressive performance compared with traditional ones. It is well known that
the characteristics of self-similarity (including non-local and local ones) of
data (e.g., natural images and time-domain signals) are widely leveraged for
denoising. However, existing deep learning-based EEG signal denoising methods
ignore either the non-local self-similarity (e.g., 1-D convolutional neural
network) or local one (e.g., fully connected network and recurrent neural
network). To address this issue, we propose a novel 1-D EEG signal denoising
network with 2-D transformer, namely EEGDnet. Specifically, we comprehensively
take into account the non-local and local self-similarity of EEG signal through
the transformer module. By fusing non-local self-similarity in self-attention
blocks and local self-similarity in feed forward blocks, the negative impact
caused by noises and outliers can be reduced significantly. Extensive
experiments show that, compared with other state-of-the-art models, EEGDnet
achieves much better performance in terms of both quantitative and qualitative
metrics.
