Electroencephalography (EEG) signals are easily corrupted by various
artifacts, making artifact removal crucial for improving signal quality in
scenarios such as disease diagnosis and brain-computer interface (BCI). In this
paper, we present a fully convolutional neural architecture, called DTP-Net,
which consists of a Densely Connected Temporal Pyramid (DTP) sandwiched between
a pair of learnable time-frequency transformations for end-to-end
electroencephalogram (EEG) denoising. The proposed method first transforms a
single-channel EEG signal of arbitrary length into the time-frequency domain
via an Encoder layer. Then, noises, such as ocular and muscle artifacts, are
extracted by DTP in a multi-scale fashion and reduced. Finally, a Decoder layer
is employed to reconstruct the artifact-reduced EEG signal. Additionally, we
conduct an in-depth analysis of the representation learning behavior of each
module in DTP-Net to substantiate its robustness and reliability. Extensive
experiments conducted on two public semi-simulated datasets demonstrate the
effective artifact removal performance of DTP-Net, which outperforms
state-of-art approaches. Experimental results demonstrate cleaner waveforms and
significant improvement in Signal-to-Noise Ratio (SNR) and Relative Root Mean
Square Error (RRMSE) after denoised by the proposed model. Moreover, the
proposed DTP-Net is applied in a specific BCI downstream task, improving the
classification accuracy by up to 5.55% compared to that of the raw signals,
validating its potential applications in the fields of EEG-based neuroscience
and neuro-engineering.

本研究提出了一种名为 DTP-Net 的全卷积神经架构，用于端到端脑电图信号去噪，通过深入分析 DTP-Net 中各模块的表示学习行为来验证其鲁棒性和可靠性，并通过实验验证表明 DTP-Net 在去除噪音方面具有有效性，优于现有方法，进一步将 DTP-Net 应用到特定的脑机接口任务中，相对于原始信号，分类准确率提高了 5.55%，证明其在基于脑电图的神经科学和神经工程领域中具有潜在应用价值。