<ul><li>Artifacts in the electroencephalogram (EEG) degrade signal quality and impact the analysis of brain activity.</li><li>A convolutional neural network (CNN) model incorporating a convolutional block attention module (CNN-CBAM) is proposed to detect and identify the location of artifacts in sleep EEG with attention maps.</li><li>The CNN-CBAM model achieved high performance with the highest area under the receiver operating characteristic curve (0.88), sensitivity (0.81), and specificity (0.86) when compared to other approaches.</li><li>This work demonstrates the feasibility of automating the detection and localization of artifacts in wearable sleep EEG.</li></ul>

Artifact detection and localization in single-channel mobile EEG for sleep research using deep learning and attention mechanisms

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