ArticleScientific reports2025
A novel EEG artifact removal algorithm based on an advanced attention mechanism.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
- Research on assessment and adaptive correction technology of power system protection fixed value mismatch.Scientific reports · 2026Article
- Prediction of college student psychological state based on deep learning framework combining the improved Whale Optimization Algorithm and LSTM.Scientific reports · 2026Article
- Effect of Emotional States on EEG-Based Biometric Identification: A Comparative Study of Classifiers.Bioengineering (Basel, Switzerland) · 2026Article
- MGFNet: A Multi-Granularity Fusion Network with Coupling-Guided Sparse Routing for Hybrid EEG-fNIRS Decoding.Sensors (Basel, Switzerland) · 2026Article
- Recent Advances in Polypyrrole-Based Functional Coatings: Surface Protection and Emerging Applications.Materials (Basel, Switzerland) · 2026Review
- Towards Robust UAV Navigation in Agriculture: Key Technologies, Application, and Future Directions.Plants (Basel, Switzerland) · 2026Review
- Suppressing Non-Stationary Motion Artefacts in Mobile EEG Using Generalized Eigenvalue Decomposition.Sensors (Basel, Switzerland) · 2026Article
- A multi-scale transformer-enhanced YOLO framework for unified road damage detection and boundary-aware segmentation.Frontiers in artificial intelligence · 2026Article
- Neuroengineering Frontiers: A Selective Review of Neural Interfaces, Brain-Machine Interactions, and Artificial Intelligence in Neurodegenerative Diseases.Applied sciences (Basel, Switzerland) · 2025Article
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Authors and funding
10 authors.
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Abstract
EEG is widely applied in emotion recognition, brain disease detection, and other fields due to its high temporal resolution and non-invasiveness. However, artifact removal remains a crucial issue in EEG signal processing. Recently, with the rapid development of deep learning, there has been a significant transformation in the methods of EEG artifact removal. Nonetheless, existing research still exhibits some limitations: (1) insufficient capability to remove unknown artifacts; (2) inability to adapt to tasks where artifact removal needs to be applied to the overall input of multi-channel EEG data. Therefore, this study proposes CLEnet by integrating dual-scale CNN (Convolutional Neural Networks) and LSTM (Long Short-Term Memory), and incorporating an improved EMA-1D (One-Dimensional Efficient Multi-Scale Attention Mechanism). CLEnet can extract the morphological features and temporal features of EEG, thereby separating EEG from artifacts. We conducted experiments on three datasets, and the results showed that CLEnet performed best. Specifically, in the task of removing artifacts from multi-channel EEG data containing unknown artifacts, CLEnet shows improvements of 2.45% and 2.65% in SNR(signal-to-noise ratio) and CC(average correlation coefficient). Moreover, RRMSE
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