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

Artifacts in the electroencephalogram (EEG) degrade signal quality and impact the analysis of brain activity. Current methods for detecting artifacts in sleep EEG range from threshold-based algorithms to machine learning approaches, yet applications remain limited for single-channel mobile EEG. We propose a convolutional neural network (CNN) model incorporating a convolutional block attention module (CNN-CBAM) to detect and localize artifacts in sleep EEG using attention maps. We benchmarked this model against 6 other machine learning and signal processing approaches. We trained/tuned all models on 72 manually annotated EEG recordings obtained during home-based monitoring from 18 healthy participants with a mean (SD) age of 68.05 y ( ± 5.02). We tested them on 26 separate recordings from 6 healthy participants with a mean (SD) age of 68.33 y ( ± 4.08), which contained artifacts in 4% of epochs. CNN-CBAM achieved the highest area under the receiver operating characteristic curve (0.88), sensitivity (0.81), and specificity (0.86) among the tested approaches. Under the ideal choice of an attention threshold of 0.66, the attention maps from CNN-CBAM localized artifacts within detected artifact epochs with a sensitivity of 0.61 and specificity of 0.63. This work demonstrates the feasibility of automating artifact detection and localization in wearable sleep EEG.