轻量级方法实时识别单通道脑电中的眼动、肌电和白噪声干扰。
Real-time Noise Detection and Classification in Single-Channel EEG: A Lightweight Machine Learning Approach for EMG, White Noise, and EOG Artifacts
- 融合时域与频域特征，用PCA优化降维以保留关键信息
- 在低信噪比(-7dB)下准确率达99%，多源干扰下仍保持96%准确率
- 训练快30秒，适合可穿戴脑机接口的实时应用
单通道脑电图（EEG）在真实场景中面临计算效率低、多源噪声干扰鲁棒性差、深度学习模型精度与复杂度难平衡等问题。本文提出一种混合谱-时域框架，用于实时检测与分类眼动（EOG）、肌电（EMG）及白噪声伪影。该方法结合时域低通滤波（针对低频EOG）与频域功率谱密度（PSD）分析（捕捉宽频EMG），再通过PCA优化特征融合，减少冗余同时保留判别信息。这一特征工程策略使轻量级多层感知机（MLP）在信噪比-7 dB时达到99%准确率，在4 dB中等噪声下超过90%。尤其在同时存在多种伪影（EMG+EOG+白噪声）的情况下，仍维持96%分类准确率。训练时间仅30秒（比CNN快97%），且在不同信噪比下表现稳定。该框架突破了依赖深层模型的传统范式，证明领域知识驱动的特征融合在噪声环境下优于复杂网络结构，为可穿戴脑机接口提供了兼具临床可用性与计算高效性的解决方案。
原文摘要 · Abstract (English)
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.
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