Real-time Noise Detection and Classification in Single-Channel EEG: A Lightweight Machine Learning Approach for EMG, White Noise, and EOG Artifacts
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866914083508322304 |
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| author | Enshaei, Hossein Jebreili, Pariya Sakhaei, Sayed Mahmoud |
| author_facet | Enshaei, Hossein Jebreili, Pariya Sakhaei, Sayed Mahmoud |
| contents | Electroencephalogram (EEG) artifact detection in real-world settings faces significant challenges such as computational inefficiency in multi-channel methods, poor robustness to simultaneous noise, and trade-offs between accuracy and complexity in deep learning models. We propose a hybrid spectral-temporal framework for real-time detection and classification of ocular (EOG), muscular (EMG), and white noise artifacts in single-channel EEG. This method, in contrast to other approaches, combines time-domain low-pass filtering (targeting low-frequency EOG) and frequency-domain power spectral density (PSD) analysis (capturing broad-spectrum EMG), followed by PCA-optimized feature fusion to minimize redundancy while preserving discriminative information. This feature engineering strategy allows a lightweight multi-layer perceptron (MLP) architecture to outperform advanced CNNs and RNNs by achieving 99% accuracy at low SNRs (SNR -7) dB and >90% accuracy in moderate noise (SNR 4 dB). Additionally, this framework addresses the unexplored problem of simultaneous multi-source contamination(EMG+EOG+white noise), where it maintains 96% classification accuracy despite overlapping artifacts. With 30-second training times (97% faster than CNNs) and robust performance across SNR levels, this framework bridges the gap between clinical applicability and computational efficiency, which enables real-time use in wearable brain-computer interfaces. This work also challenges the ubiquitous dependence on model depth for EEG artifact detection by demonstrating that domain-informed feature fusion surpasses complex architecture in noisy scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_26058 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Real-time Noise Detection and Classification in Single-Channel EEG: A Lightweight Machine Learning Approach for EMG, White Noise, and EOG Artifacts Enshaei, Hossein Jebreili, Pariya Sakhaei, Sayed Mahmoud Machine Learning Artificial Intelligence Neural and Evolutionary Computing Electroencephalogram (EEG) artifact detection in real-world settings faces significant challenges such as computational inefficiency in multi-channel methods, poor robustness to simultaneous noise, and trade-offs between accuracy and complexity in deep learning models. We propose a hybrid spectral-temporal framework for real-time detection and classification of ocular (EOG), muscular (EMG), and white noise artifacts in single-channel EEG. This method, in contrast to other approaches, combines time-domain low-pass filtering (targeting low-frequency EOG) and frequency-domain power spectral density (PSD) analysis (capturing broad-spectrum EMG), followed by PCA-optimized feature fusion to minimize redundancy while preserving discriminative information. This feature engineering strategy allows a lightweight multi-layer perceptron (MLP) architecture to outperform advanced CNNs and RNNs by achieving 99% accuracy at low SNRs (SNR -7) dB and >90% accuracy in moderate noise (SNR 4 dB). Additionally, this framework addresses the unexplored problem of simultaneous multi-source contamination(EMG+EOG+white noise), where it maintains 96% classification accuracy despite overlapping artifacts. With 30-second training times (97% faster than CNNs) and robust performance across SNR levels, this framework bridges the gap between clinical applicability and computational efficiency, which enables real-time use in wearable brain-computer interfaces. This work also challenges the ubiquitous dependence on model depth for EEG artifact detection by demonstrating that domain-informed feature fusion surpasses complex architecture in noisy scenarios. |
| title | Real-time Noise Detection and Classification in Single-Channel EEG: A Lightweight Machine Learning Approach for EMG, White Noise, and EOG Artifacts |
| topic | Machine Learning Artificial Intelligence Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2509.26058 |