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Electroencephalography (EEG) signals are easily corrupted by variousartifacts, making artifact removal crucial for improving signal quality inscenarios such as disease diagnosis and brain-computer interface (BCI). In thispaper, we present a fully convolutional neural architecture, called DTP-Net,which consists of a Densely Connected Temporal Pyramid (DTP) sandwiched betweena pair of learnable time-frequency transformations for end-to-endelectroencephalogram (EEG) denoising. The proposed method first transforms asingle-channel EEG signal of arbitrary length into the time-frequency domainvia an Encoder layer. Then, noises, such as ocular and muscle artifacts, areextracted by DTP in a multi-scale fashion and reduced. Finally, a Decoder layeris employed to reconstruct the artifact-reduced EEG signal. Additionally, weconduct an in-depth analysis of the representation learning behavior of eachmodule in DTP-Net to substantiate its robustness and reliability. Extensiveexperiments conducted on two public semi-simulated datasets demonstrate theeffective artifact removal performance of DTP-Net, which outperformsstate-of-art approaches. Experimental results demonstrate cleaner waveforms andsignificant improvement in Signal-to-Noise Ratio (SNR) and Relative Root MeanSquare Error (RRMSE) after denoised by the proposed model. Moreover, theproposed DTP-Net is applied in a specific BCI downstream task, improving theclassification accuracy by up to 5.55% compared to that of the raw signals,validating its potential applications in the fields of EEG-based neuroscienceand neuro-engineering.

DTP-Net: Learning to Reconstruct EEG signals in Time-Frequency Domain by Multi-scale Feature Reuse