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.
翻译:电磁图(EEG)显示了一种有用的方法来生成大脑-计算机界面(BCI)。一维(1-D) EEG信号由于时间分辨率高,很容易被某些工艺品(a.k.a.噪声)干扰。因此,必须消除收到的EEEG信号中的噪音。最近,深层次的基于学习的EEEG信号分泌方法与传统方法相比取得了令人印象深刻的性能。为了解决这个问题,我们建议以2D变异器(即EEEGDnet)为新型的1-D信号(包括非本地和地方的和本地的信号)。具体地说,我们从深层次的基于学习的EEEEG信号分泌方法忽略了非本地的自异性(例如1D神经网络)或本地的自异性(例如,完全连接的网络和经常的神经网络)或异性能。为了解决这一问题,我们建议采用新型的1DEEEG信号(即EEGDnet)为2D变异的自我变异性网络。我们全面考虑到非本地和本地的自我变异性模型,通过内部的自我变异性模型,通过非本地的自我变异性模型,使得当地的自我的自我变异性能的自我变异性(EEEEEEEEG-EG-EG)的自我的自我的自我显示的自我的自我的自我的自我的自我显示的自我反应。