用自监督学习和注意力机制提升脑电图的无标签训练效果
SSL-SE-EEG: A Framework for Robust Learning from Unlabeled EEG Data with Self-Supervised Learning and Squeeze-Excitation Networks
- 将脑电信号转为2D图像，结合自监督学习与注意力网络提取特征
- 在多个数据集上达到91%准确率，显著降低对标注数据依赖
- 适合低功耗、实时脑机接口场景，推动神经工程应用落地
脑电图（EEG）在脑机接口和神经诊断中至关重要，但其实际应用受噪声干扰、数据缺失和标注成本高的制约。本文提出SSL-SE-EEG框架，融合自监督学习（SSL）与压缩-激励网络（SE-Nets），提升特征提取能力，增强抗噪性能，并减少对标注数据的依赖。不同于传统处理方法，该框架将脑电信号转换为结构化的二维图像表示，适配深度学习模型。在MindBigData、TUH-AB、SEED-IV和BCI-IV数据集上的实验验证表明，该方法在MindBigData上达到91%准确率，在TUH-AB上达85%，表现领先于现有技术，适用于实时脑机接口应用。通过实现低功耗、可扩展的脑电分析，SSL-SE-EEG为生物医学信号处理、神经工程及下一代脑机接口提供了有前景的解决方案。
原文摘要 · Abstract (English)
Electroencephalography (EEG) plays a crucial role in brain-computer interfaces (BCIs) and neurological diagnostics, but its real-world deployment faces challenges due to noise artifacts, missing data, and high annotation costs. We introduce SSL-SE-EEG, a framework that integrates Self-Supervised Learning (SSL) with Squeeze-and-Excitation Networks (SE-Nets) to enhance feature extraction, improve noise robustness, and reduce reliance on labeled data. Unlike conventional EEG processing techniques, SSL-SE-EEG} transforms EEG signals into structured 2D image representations, suitable for deep learning. Experimental validation on MindBigData, TUH-AB, SEED-IV and BCI-IV datasets demonstrates state-of-the-art accuracy (91% in MindBigData, 85% in TUH-AB), making it well-suited for real-time BCI applications. By enabling low-power, scalable EEG processing, SSL-SE-EEG presents a promising solution for biomedical signal analysis, neural engineering, and next-generation BCIs.
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