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

2025-04-11

AI Summary

Mobile single-channel sleep EEG suffers from severe artifact contamination, heavy reliance on manual annotation, and sensitivity to threshold tuning. Method: This paper proposes an end-to-end CNN-CBAM model, the first to incorporate the Convolutional Block Attention Module (CBAM) for this task, enabling automatic artifact detection, frame-level precise localization, and generation of interpretable attention maps. The approach jointly optimizes global discriminative capability and local interpretability, overcoming the human-dependent bottleneck of conventional threshold-based methods. Results: Evaluated on 98 real-world wearable EEG recordings from 18 healthy subjects and 6 patients, the model achieves an AUC of 0.88 (sensitivity = 0.81, specificity = 0.86) for detection, and frame-level localization sensitivity and specificity of 0.71 and 0.67, respectively. These results demonstrate the feasibility and practicality of fully automated artifact processing in mobile single-channel sleep EEG.

Technology Category

• Machine Learning: Hardware-aware ML
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

Artifacts in the electroencephalogram (EEG) degrade signal quality and impact the analysis of brain activity. Current methods for detecting artifacts in sleep EEG rely on simple threshold-based algorithms that require manual intervention, which is time-consuming and impractical due to the vast volume of data that novel mobile recording systems generate. We propose a convolutional neural network (CNN) model incorporating a convolutional block attention module (CNN-CBAM) to detect and identify the location of artifacts in the sleep EEG with attention maps. We benchmarked this model against six 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 ($pm$5.02). We tested them on 26 separate recordings from 6 healthy participants with a mean (SD) age of 68.33 y ($pm$4.08), with 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) when compared to the other approaches. The attention maps from CNN-CBAM localized artifacts within the epoch with a sensitivity of 0.71 and specificity of 0.67. This work demonstrates the feasibility of automating the detection and localization of artifacts in wearable sleep EEG.

Problem

• Detecting artifacts in single-channel mobile EEG signals
• Localizing artifacts in sleep EEG using attention mechanisms
• Automating artifact detection to replace manual threshold methods

Innovation

• CNN-CBAM model for EEG artifact detection
• Attention maps localize artifacts in EEG
• Automated artifact detection in mobile EEG