A novel EEG artifact removal algorithm based on an advanced attention mechanism
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...
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Nature Portfolio
2025-06-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-98653-1 |
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| author | Rui Jiang Shen Tong Jiawei Wu Haowei Hu Ran Zhang Heng Wang Yan Zhao Weixin Zhu Shuyan Li Xiao Zhang |
| author_facet | Rui Jiang Shen Tong Jiawei Wu Haowei Hu Ran Zhang Heng Wang Yan Zhao Weixin Zhu Shuyan Li Xiao Zhang |
| author_sort | Rui Jiang |
| collection | DOAJ |
| description | 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 t (relative root mean square error in the temporal domain) and RRMSE f (relative root mean square error in the frequency domain) decrease by 6.94% and 3.30%. |
| format | Article |
| id | doaj-art-d576a6e2974c42a48e458128349877fa |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-d576a6e2974c42a48e458128349877fa2025-08-20T02:05:48ZengNature PortfolioScientific Reports2045-23222025-06-0115111910.1038/s41598-025-98653-1A novel EEG artifact removal algorithm based on an advanced attention mechanismRui Jiang0Shen Tong1Jiawei Wu2Haowei Hu3Ran Zhang4Heng Wang5Yan Zhao6Weixin Zhu7Shuyan Li8Xiao Zhang9School of Medical Information and Engineering, Xuzhou Medical UniversitySchool of Medical Information and Engineering, Xuzhou Medical UniversitySchool of Medical Information and Engineering, Xuzhou Medical UniversitySchool of Medical Information and Engineering, Xuzhou Medical UniversitySchool of Medical Information and Engineering, Xuzhou Medical UniversitySchool of Medical Information and Engineering, Xuzhou Medical UniversityXuzhou Jiazhi information Technology Co., Ltd.Jinhua Central HospitalSchool of Medical Information and Engineering, Xuzhou Medical UniversitySchool of Medicine and Health, Harbin Institute of TechnologyAbstract 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 t (relative root mean square error in the temporal domain) and RRMSE f (relative root mean square error in the frequency domain) decrease by 6.94% and 3.30%.https://doi.org/10.1038/s41598-025-98653-1Electroencephalogram (EEG)Artifact removalDeep learningAttention mechanism |
| spellingShingle | Rui Jiang Shen Tong Jiawei Wu Haowei Hu Ran Zhang Heng Wang Yan Zhao Weixin Zhu Shuyan Li Xiao Zhang A novel EEG artifact removal algorithm based on an advanced attention mechanism Scientific Reports Electroencephalogram (EEG) Artifact removal Deep learning Attention mechanism |
| title | A novel EEG artifact removal algorithm based on an advanced attention mechanism |
| title_full | A novel EEG artifact removal algorithm based on an advanced attention mechanism |
| title_fullStr | A novel EEG artifact removal algorithm based on an advanced attention mechanism |
| title_full_unstemmed | A novel EEG artifact removal algorithm based on an advanced attention mechanism |
| title_short | A novel EEG artifact removal algorithm based on an advanced attention mechanism |
| title_sort | novel eeg artifact removal algorithm based on an advanced attention mechanism |
| topic | Electroencephalogram (EEG) Artifact removal Deep learning Attention mechanism |
| url | https://doi.org/10.1038/s41598-025-98653-1 |
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