在EEG生物识别中平衡降噪和神经特征保护
Muhammad Usman1,2, Nadia Sultan1,3, Ammara Nasim1,3
1Center of Excellence in AI (CoE-AI), Bahria University, Islamabad, Pakistan.
Scientific reports
|January 30, 2026
概括
EEG生物识别提供安全的身份验证. 一个新的框架,优化了预处理,光谱特征 (MFCC) 和整体分类 (XGBoost),大大提高了对象识别的准确性.
科学领域:
- 神经科学是一个神经科学.
- 生物识别信息 生物识别信息
- 信号处理 信号处理
背景情况:
- 基于脑电图 (EEG) 的对象识别是一种有前途的生物识别技术,用于安全的身份验证.
- 优化整个处理管道对于可靠的EEG生物识别性能至关重要.
- 关键的挑战包括提高信号质量,同时保持独特的神经特征.
研究的目的:
- 提出和评估基于EEG的对象识别的综合框架.
- 整合宽松的预处理,光谱特征提取和整体分类.
- 为优化EEG生物识别系统提供实际指导方针.
主要方法:
- 利用大脑编码数据集 (BED) 使用原始EEG,放松的PREP管道数据和预提取的特征.
- 使用Mel-frequency cepstral系数 (MFCC) 提取的光谱特征.
- 采用组合分类器,结合决策树,随机森林,支向量机器和XGBoost.
主要成果:
- 在清理数据上使用XGBoost与视觉唤起潜力 (VEP) 刺激实现了98.00%的峰值精度.
- 与原始信号 (5.3%) 和预提取特征 (8.4%) 相比,显著改善.
- 在各种实验条件下表现出强的性能,并提高了跨会话可靠性.
结论:
- 拟议的框架建立了基于EEG的对象识别的原则性方法.
- 优化的预处理,特征提取和分类提高了生物识别性能.
- 闭着眼睛的休息状态成为EEG生物识别中最稳定的范式.
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