Abstract: Normal EEG signals are susceptible to contamination by various EEG artifacts, resulting in low signal-to-noise ratios. In order to improve the signal-to-noise ratio of EEG signals, a metric-based few-shot learning method was used to detect EEG artifacts. A few-shot learning model named EEG Artifact Prototype Network (EAPNet) based on a prototype network was proposed to detect artifacts in EEG signals. The model was able to learn a nonlinear mapping from EEG features to the target space, calculate the distance of each class prototype representation and classify them according the distance. In addition, the EAPNet model required only a small number of examples of each new class to train the model to achieve accurate recognition of artifact signals. Artifact recognition experiments were conducted in the public EEG artifact dataset TUAR (TUH EEG Artifact Corpus). The EAPNet model is compared with 2 deep learning models (EEGNet, Fully Connected Neural Network (FNN)) and 7 machine learning models (Gaussian NB, Random Forest (RF), Logistic Regression (LR), Lasso Regression (Lasso), Support Vector Machine (SVM), Ridge Regression (Ridge) and Nearest Neighbor Algorithms (KNN)) in a comparative experiment. The experimental results showed that the EAPNet model was an efficient artifact detection method, with a detection accuracy of 69.44%, 77.21% and 80.01% for 2-way K-shot(K=1, 5, 10) tasks respectively. Among all the compared models, the EAPNet model had the highest recognition accuracy.