ArticleScientific reports2024
AnEEG: leveraging deep learning for effective artifact removal in EEG data.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Event-Preserving Neural Network Denoising for Impedance Flow Cytometry.Nanomaterials (Basel, Switzerland) · 2026Article
- Reliable but Wrong? Automated Detection of Heart Rate Artifacts as a Source of Bias in Pediatric EEG Data.Research square · 2026Article
- The use of quantitative electroencephalography in evaluating epilepsy treatment.Acta epileptologica · 2026Review
- Deep learning in fetal, infant, and toddler neuroimaging research.Developmental cognitive neuroscience · 2026Review
- Balancing noise reduction and neural signature preservation in EEG biometrics.Scientific reports · 2026Article
- A lightweight deep convolutional neural network for detecting artifacts in continuous EEG signals.Clinical neurophysiology practice · 2026Article
- CLSP-REQA: a real-time quality-aware closed-loop seizure prediction framework with Mamba-BiLSTM and confidence-gated intervention.Frontiers in neuroinformatics · 2026Article
- Soft Multiaxial Strain Mapping Interface with AI-Driven Decoding for Silent Speech in Noise.Cyborg and bionic systems (Washington, D.C.) · 2026Article
- ICA-SFrontiers in neuroscience · 2026Article
- Data-Centric AI for EEG-Based Emotion Recognition: Noise Filtering and Augmentation Strategies.Bioengineering (Basel, Switzerland) · 2025Article
- EEG Complexity Analysis of Psychogenic Non-Epileptic and Epileptic Seizures Using Entropy and Machine Learning.Entropy (Basel, Switzerland) · 2025Article
- Exploring cognitive effort and divergent thinking in metaphor translation using eye-tracking and EEG technology.Scientific reports · 2025Article
- Deep learning approaches for EEG-based healthcare applications: a comprehensive review.Frontiers in human neuroscience · 2025Review
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Authors and funding
3 authors.
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Abstract
In neuroscience and clinical diagnostics, electroencephalography (EEG) is a crucial instrument for capturing neural activity. However, this signal is polluted by different artifacts like muscle activity, eye blinks, environmental interference, etc., which makes it more difficult to retrieve important information from the signal. Deep learning methods have demonstrated the potential to lower these artifacts and enhance the EEG's quality in recent years. In this work, a novel deep learning method,"AnEEG" is presented for eliminating artifacts from EEG signal. The quantitative matrices NMSE, RMSE, CC, SNR and SAR are calculated to confirm the effectiveness of the proposed model. Through this process, it was found that the suggested model outperformed wavelet decomposition techniques. The model achieves lower NMSE and RMSE values, which indicates better agreement with the original signal. Achieving higher CC values means stronger linear agreement with the ground truth signals. Additionally, the model shows improvements in both SNR and SAR values. Overall, this suggested approach showcases promising results in improving the quality of EEG data by utilizing deep learning.
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